Real-Time Weld Process Monitoring

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Real-time weld process monitoring

WPNL2204

Related titles: Advanced welding processes (ISBN 978-1-84569-130-1) This book introduces the range of advanced welding techniques currently in use. It covers gas tungsten arc welding (GTAW), gas metal arc welding (GMAW), highenergy density processes like laser welding, and narrow gap welding methods. The book reviews general issues such as power sources, filler materials and shielding gases. Particular attention is given to monitoring and process control as well as to automation and robotics. Computational welding mechanics (ISBN 978-1-84569-221-6) This book begins by discussing the physics of welding before going on to review modelling methods and options as well as validation techniques. It also reviews applications in areas such as fatigue, buckling and deformation, improved service life of components and process optimisation. Some of the numerical methods described in the book are illustrated using software available from the author which allows readers to explore computational welding mechanics in more depth. New developments in advanced welding (ISBN 978-1-85573-970-3) Recent developments in high-technology areas have significantly transformed the welding industry where automation, computers, process control, sophisticated scientific instruments and advanced processing methods are all common. Today’s engineers and technologists have to support complex systems and apply sophisticated welding technologies. This comprehensive new book discusses the changes in advanced welding technologies preparing the reader for the modern industry. Details of these and other Woodhead Publishing materials books, as well as materials books from Maney Publishing, can be obtained by: • •

visiting our web site at www.woodheadpublishing.com contacting Customer Services (e-mail: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext. 130; address: Woodhead Publishing Ltd, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, England)

If you would like to receive information on forthcoming titles, please send your address details to: Francis Dodds (address, tel. and fax as above; e-mail: francisd@ woodhead-publishing.com). Please confirm which subject areas you are interested in. Maney currently publishes 16 peer-reviewed materials science and engineering journals. For further information visit www.maney.co.uk/journals.

WPNL2204

Real-time weld process monitoring Edited by YuMing Zhang

Woodhead Publishing and Maney Publishing on behalf of The Institute of Materials, Minerals & Mining WPNL2204

CRC Press Boca Raton Boston New York Washington, DC

Cambridge England

WPNL2204

Woodhead Publishing Limited and Maney Publishing Limited on behalf of The Institute of Materials, Minerals & Mining Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington Cambridge CB21 6AH, England www.woodheadpublishing.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway, NW Suite 300, Boca Raton, FL 33487, USA First published 2008, Woodhead Publishing Limited and CRC Press LLC © 2008, Woodhead Publishing Limited The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN 978-1-84569-268-1 (book) Woodhead Publishing ISBN 978-1-84569-440-1 (e-book) CRC Press ISBN 978-1-4200-7787-2 CRC Press order number WP7787 The publishers’ policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elementary chlorine-free practices. Furthermore, the publishers ensure that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by SNP Best-set Typesetter Ltd., Hong Kong Printed by TJ International Limited, Padstow, Cornwall, England

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Contents

Contributor contact details

1

ix

1.1 1.2 1.3 1.4 1.5 1.6 1.7

An analysis of welding process monitoring and control Y.-M. Zhang, University of Kentucky, USA Introduction Welding process as a system Welding process as a complex system Welding process as an uncertain system Monitoring of welding processes Overview of the book References

1 1 4 6 7 10 11

Part I

Monitoring technologies

13

2

Arc sensors in weld monitoring G. E. Cook, A. M. Strauss, D. H. Lammlein and P. A. Fleming, Vanderbilt University, USA Introduction Background Non-consumable electrode processes Consumable electrode processes Power supply considerations Signal processing and control approaches Future trends Sources of further information and advice List of symbols and abbreviations References

15

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10

1

15 15 20 25 31 36 39 41 41 43 v

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Contents

3

Optical sensors in welding G. M. Saeed, Schlumberger, USA and H. Song, University of Kentucky, USA Introduction Knowledge of optics Optical sensor component Optical sensing system Application of optical sensors References

45

Infrared sensors in welding H. Yang, H. C. Wikle, S. Nagarajan, M. Johnson, P. Banerjee and B. A. Chin, Auburn University, USA Introduction Theory of infrared sensors Measurement of weld temperature distribution Infrared sensors for real-time weld quality control Summary References

74

3.1 3.2 3.3 3.4 3.5 3.6 4

4.1 4.2 4.3 4.4 4.5 4.6 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7

Ultrasonic sensors in welding J. Shao and Y. Yan, University of Kent, UK Introduction Principles of ultrasonic sensing Ultrasonic techniques and their applications Future trends Summary Sources of further information and advice References

45 46 52 62 69 72

74 74 80 82 99 99 104 104 105 110 122 123 124 124

Part II Monitoring of welding processes

129

6

131

6.1 6.2 6.3 6.4 6.5 6.6

Weld seam monitoring S.-J. Na, Korea Advanced Institute of Science and Technology, Korea Introduction Methods of weld seam monitoring Through-arc monitoring of weld seams in gas metal arc welding Electromagnetic monitoring of weld seams in gas metal arc welding Laser visual monitoring of weld seams in gas metal arc welding References

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Contents 7

7.1 7.2 7.3 7.4 7.5 8

8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 9

9.1 9.2 9.3 9.4 9.5 9.6 10 10.1 10.2 10.3 10.4

vii

Weld profile monitoring L. Wu and G.-J. Zhang, Harbin Institute of Technology, China Necessity for weld profile measurement Outline of weld profile Surface shaping defects Automatic detection of weld profile References

164

Weld penetration monitoring W. Lu, Cummins Inc., USA and Y.-M. Zhang, University of Kentucky, USA Introduction Weld pool oscillation sensor Infrared sensor Ultrasonic sensor Vision-based sensor Weld pool sag depression Plasma charge-based sensors Weld penetration control using sensor feedback Summary References

186

Weld pool surface monitoring C. S. Wu, Institute for Materials Joining, Shandong University, China Vision-based sensing 2D surface geometry of weld pool in gas tungsten arc welding Sensing 2D weld pool boundary in gas metal arc welding Modeling and simulation of weld pool surface Sensing 3D weld pool surface using specular reflection Improvement on observation of dynamic 3D weld pool surface References

213

Monitoring resistance welding D. F. Farson, The Ohio State University, USA Introduction Resistance spot welding systems, weld formation and signals Real-time process monitoring and control Future trends

238

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186 187 191 193 195 196 199 205 209 211

213 217 220 229 232 236

238 239 245 255

viii

Contents

10.5 10.6 10.7

Sources of further information and advice Acknowledgment References

258 258 258

11

Monitoring laser welding A. Ancona and T. Sibillano, CNR-INFM LIT3, Italy Introduction Principles of laser welding Optical sensing Frequency analyses of acoustic and optical emissions Smart systems Other systems and future trends References

260

Index

289

11.1 11.2 11.3 11.4 11.5 11.6 11.7

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Contributor contact details

(* = main contact)

Editor and Chapter 1

555 Industrial Blvd Sugarland TX 77475 USA

YuMing Zhang, PhD Professor and James R. Boyd Professor Director of Graduate Studies Department of Electrical and Computer Engineering College of Engineering University of Kentucky Lexington, KY 40506 USA

Email: [email protected] Dr Hongsheng Song University of Kentucky USA Email: [email protected]

Chapter 4

Email: [email protected]

Professor George E. Cook,* A. M. Strauss, D. H. Lammlein and P. A. Fleming Vanderbilt University Nashville, TN 37240 Tennessee USA

Hong Yang,* Howard Clyde Wikle, Sundaram Nagarajan, Michael Johnson, Probal Banerjee and Bryan A. Chin 275 Wilmore Laboratories Materials Engineering Auburn University Auburn AL 36849 USA

Email: george.e.cook@vanderbilt. edu

Email: [email protected] [email protected]

Chapter 3

Chapter 5

Dr Gohar M. Saeed* Schlumberger

Dr Jiaqing Shao* and Professor Yong Yan

Chapter 2

ix WPNL2204

x

Contributor contact details YuMing Zhang, PhD Professor and James R. Boyd Professor Director of Graduate Studies Department of Electrical and Computer Engineering College of Engineering University of Kentucky USA

Department of Electronics University of Kent Canterbury Kent CT2 7NT UK Email: [email protected] [email protected]

Chapter 6


Professor Suck-Joo Na Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 335 Gwahangno (373-1 Guseong-dong) Yuseong-gu Daejeon 305-701 Republic of Korea

Chapter 9 Professor Chuan Song Wu Institute for Materials Joining, Shandong University 73 Jingshi Road Jinan 250061 China Email: [email protected]


Chapter 7

Chapter 10

Professor Lin Wu and Dr GuangJun Zhang* State Key Laboratory of Advanced Welding Production Technology Harbin Institute of Technology Harbin 150001 China

Dr Dave F. Farson Welding Engineering Program The Ohio State University OH 43210, USA Email: [email protected]

Chapter 11

Dr Wei Lu* Cummins Inc. 4411 Westminster Place Columbus IN 47201 USA

Dr Antonio Ancona* and Dr Teresa Sibillano CNR-INFM Regional Laboratory ‘LIT3’ Dipartimento Interateneo di Fisica ‘M. Merlin’ via Orabona 4 Bari Italy I-70126




Chapter 8

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1 An analysis of welding process monitoring and control Y. - M . Z H A N G, University of Kentucky, USA

1.1

Introduction

The book is devoted to real-time monitoring of welding processes. Carefully selected leading experts around the world will introduce relatively established methodologies/techniques which have been developed, applied, or commercialized to provide effective solutions to various applications. They will also present state-of-the-art developments toward improved or nextgeneration technologies. There is no doubt that the rapid developments in micro-electronics and microprocessors have allowed engineers and researchers to develop more effective technologies to monitor welding processes in difficult environments through ways that are better, more accurate, and more robust. Although these established as well as newly explored, cutting-edge monitoring technologies will be discussed in detail through the book, I wish to take this opportunity to express my personal views on some questions the reader may ask before committing his or her valuable time to reading the book. I imagine that such questions would center around two fundamental issues: why should a welding process be monitored and how can they be effectively monitored? To answer these questions, I would like to begin by arguing that the welding process is a very complex system.

1.2

Welding process as a system

In a typical fusion welding process of metals, such as arc welding, laser welding, electron beam welding, resistance welding, as considered in this book, a heat source is applied locally to the interfaces of the two metals to be joined. In resistance or spot welding, this interface consists of the surfaces of the metals that face each other and are to be joined by a nugget. In arc welding, the interface will be the weld seam. Also, depending on the joint design, the interface where the metals are supposed to be locally heated may vary from application to application. 1 WPNL2204

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The major goal of fusion welding is to melt the interfaces of the two metals (base metals hereafter) to be joined so that they will be bridged by the liquid metal melted from them, and the liquid metal melted from the filler metal if any, and be joined together as a weld after the liquid metal solidifies. However, complex processes and phenomena occur due to the heating/melting and cooling/solidifying, which, if not appropriately controlled, may produce adverse effects on the properties of the weld and degrade the base metal properties in the heat-affected zone (HAZ). Hence, fusion welding of metals as considered in this book is the joining of base metals to meet the service requirements. First, the two originally separate base metals must be joined to become a single piece. Second, the joined single piece must function as required for the planned service environments and conditions. To be considered as single piece, a minimal requirement is that the necessary level of fusion must be assured. If the level of fusion is sufficient, the weld itself must also meet the service and application requirements, which may include strength, tensile, impact and fatigue properties. It is apparent that the chemistry, heating, cooling, and solidification all affect the final properties of the weld. In addition, the HAZ is also affected by heating and cooling, and its properties may change from the original properties of the base metals. Since the majority of the structure will still be the base metals, we typically do not require that the weld and HAZ have better properties than the base metals. Instead, we typically require that the properties of the weld and the HAZ meet at least the minimal requirements on these properties of the base metals. Now we are ready to say that a welding process can be considered as a system that generates outputs from inputs. Theoretically, all the properties of the weld and HAZ are the outputs of the system for which we have certain requirements. All adjustable welding parameters, such as heat input, current or current waveform, arc voltage, wire feed speed and travel speed, may be used as the inputs of the system and be designed to assure the production of the required outputs. Typical responsibilities of a welding engineer should include selecting an appropriate welding process and designing appropriate welding parameters as the inputs to produce the required outputs, which are the properties of the weld and HAZ. To this end, experiments need to be done to produce specimens to conduct required tests to make sure the produced properties of the weld and HAZ meet the requirements, i.e. the outputs are in the required ranges. In many applications, it is possible that the designed welding parameters (inputs) can assure the production of the desired outputs. However, to this end, the welding conditions such as the root opening of the square butt

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An analysis of welding process monitoring and control

3

joint, the geometry of the groove and the chemistry of the base metals, typically must be well controlled within the ranges in which the welding engineer conducted the experiments to select the ‘appropriate welding parameters’ and verify the properties produced. If the welding conditions are out of range, the outputs generated by the inputs may also be out of the range of requirements. From the system’s point of view, the outputs are affected by different parameters. Those parameters that are adjusted on purpose to control the outputs in the required ranges can be called control variables or simply inputs. Some parameters, such as the chemical composition of the base metals, which does not vary or is not supposed to vary during welding, or the geometry of the groove which we choose not to change, can be called the welding conditions. Hence, welding conditions are those parameters that are supposed to be constant (although they may not be) and are not adjusted during welding. The same parameter may be chosen either as an input or as a welding condition. We may try to keep those parameters that are not convenient or economic to adjust (although they may affect the output) at their nominal constant and treat them as a welding condition. However, sometimes the accurate control of the welding condition parameters at their nominal constants may be difficult either from the feasibility or the economic point of view. Fluctuations or variations from their nominal constants will exist. In such cases, these fluctuations or variations will also affect the outputs but the changes are not intentional. From a system’s point of view, such fluctuations or variations may be considered as disturbances of the system. Because of the system disturbances, the designed control variables may not achieve the desired outputs. Monitoring of the process, either of the disturbances (fluctuations or variations of the welding conditions) or of their effect on the heating, cooling, and solidification or the final outputs, may thus be needed. The monitoring results of the welding conditions may be used to make sure that the disturbances are negligible, the welding conditions are controlled at their nominal constants, and the desired outputs will thus be produced by the designed control variables; or the monitoring results of the outputs may be used to make sure that the outputs are satisfactory or to provide the feedback to adjust the control variables to maintain the outputs at their desired values. In summary, a welding process may be expressed as a system shown in Fig. 1.1 whose outputs depend on the welding conditions or their nominal constants (which determine the dynamic model of the system), controlled by the control variables or inputs (adjustable welding parameters), and affected by the disturbances (fluctuations or variations of welding conditions from their nominal constants).

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Real-time weld process monitoring Disturbances (fluctuations and variations) Inputs or control variables

Welding process (nominal welding conditions)

(adjustable welding parameters)

Outputs (properties of weld and HAZ)

1.1 The welding process as a system.

1.3

Welding process as a complex system

The welding process in general can always be viewed as a complex system which has multiple inputs, multiple outputs, and multiple disturbances unless it is simplified to emphasize the production of a major output such as the weld joint penetration at its desired level. An output is typically affected by more than one input or may even be affected by another output. From the system’s point of view, the welding process is a coupled system. Because of coupling, adjusting one input does not necessarily change a particular output. As a result, when a single output needs to be changed, multiple inputs may have to be changed/redesigned in order to maintain all the outputs at their required values. Hence, welding is a complex system. The welding process is complex also because of the large number of parameters that control and affect the outputs including the control variables and disturbances involved. Because of the coupling, changing a single output or the occurrence of a single disturbance may mean a large number of inputs have to be adjusted to maintain all the outputs at the required values. This need to adjust a large number of inputs makes the welding engineer’s job difficult. Typically, it is difficult to make sure all the outputs can meet their exact requirements. Instead, the welding engineer would have to make do with the outputs at the levels that can meet the minimum requirements, which would not necessarily be their optimal values. The welding process is a complex system because there is a lack of understanding of the quantitative relationships among the large number of inputs, disturbances, and outputs. The phenomena and physical/chemical processes occurring during welding are extremely complex. When we apply certain inputs, we do not have analytical equations to calculate or predict accurately how the outputs would respond to them. One of most important research areas in the field of welding is to numerically simulate the welding process and examine how properties/outputs would respond to the given welding parameters (inputs) under given welding conditions. However, simulation is done using the nominal welding conditions and to numerically solve the governing equations necessary, assumptions and simplifications often have

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to be used. Such assumptions/simplifications and the inability to assure the accurate parameters compromise the effectiveness of the simulation results. Further, numerical simulation is typically used to find the outputs of the process from the inputs and nominal welding conditions, but finding a set of inputs that can produce a set of desired outputs is an inverse problem that cannot be done by directly solving the governing equations. Indeed, the inverse problem is not unique owing to the multivariable, coupled, nonlinear nature of the governing equations. For example, neural network mappings of the inputs to the outputs have been shown to sometimes yield input combinations quite different from those used to program the neural network, but that produce outputs of acceptable values. The welding process is a complex system also because it is non-linear and dynamic. When a welding process gets started, the outputs gradually reach their steady state. If certain welding conditions change or the welding parameters (inputs) are intentionally adjusted, the outputs will also change before reaching the new steady state. However, the time required to reach the new steady state, as well as the dynamic nature of the change, may be quite different from one condition to another. This is due to the coupled, non-linear nature of the governing equations. Thus, if sensing and feedback are used to automatically control the output variables, to avoid instabilities the feedback controller may need to be adjusted in real time to maintain a desired dynamic response. In this case, adaptive feedback control is sometimes called for. Examples of this are discussed in this book. Let us further elaborate on the complexity of the system due to its being non-linear and dynamic. Before the steady state is reached, the outputs are determined by the inputs which have been applied since welding got started or since the welding conditions changed or welding parameters were adjusted. The system is thus dynamic and acurately estimating the outputs at a time before the steady state is reached is thus difficult because it would need the history of inputs over the same length of time. One may wait until the steady state is established to see if the outputs can meet the requirement. However, such a practice would introduce excessive delay to establish the required outputs. Hence, being a dynamic system adds difficulty to determining the correct set of input values to achieve the desired outputs. For such a dynamic system, knowing its dynamic behavior or dynamic model which correlates the history of inputs to the outputs can help optimize the adjustment of the inputs to improve the speed taken to converge at the desired levels of the outputs. However, the welding process is typically non-linear as has been observed [1–8]. A non-linear dynamic model may be obtained to correlate the outputs with the history of the inputs but the dynamic model for a non-linear system is typically complex. The effective use of non-linear dynamic models to optimally determine how to adjust the inputs to reach the desired outputs quickly is a typical non-linear system

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control problem which lacks generally applicable solutions and can be difficult even for advanced control engineers.

1.4

Welding process as an uncertain system

Theoretically, although the welding process may be complex, coupled, and non-linear, and may have disturbances, it is still possible to find a set of input values through experiments to achieve the desired outputs. However, to achieve this goal, the welding conditions must be maintained at their nominal constants which have been used to conduct the experiments to determine the optimal set of the input values. If the welding conditions are difficult to maintain at their nominal constants either economically or technically, use of the pre-optimized inputs will not achieve the desired outputs. There are two possible variations of welding conditions from their nominal constants: short-term disturbances and constant (or long-term) deviations. Examples of short-term disturbances include surface contamination/oxides, gas turbulence, and temporary wire feeding interruption, which it should be possible to overcome through better engineering practices. Their effects on the welding process would also be short term and the outputs should return to their desired values soon after the disturbances because the welding process is fundamentally a stable system whose outputs asymptotically converge to their steady-state values after the transition period. However, for some welding conditions such as the chemical composition, plate thickness, and joint preparation, they may deviate from their nominal constants during the whole welding period. If this occurs, the outputs of the welding process will deviate from their desired values over the whole welding period. The difference between a short-term disturbance and constant variations can be thought of in this way: short-term disturbances may sometimes be ignored (depending on the criticality of the weld) because the outputs would return the desired values soon after the disturbances. However, the effect of constant deviations on the outputs will not be corrected unless the inputs are adjusted. The problem is that the system dynamic model that determines how outputs respond to the input has now changed. Figure 1.2 shows a typical control system whose outputs are affected by the inputs, (short-term) disturbances, and system dynamics determined by the welding conditions. The disturbances are typically additive to the signals in the system and do not affect the dynamics of the system as described using a dynamic model. This treatment is of course an approximation for the welding process when we consider short-term perturbations as the disturbances. For example, if there is a tack in the welding seam, the thickness of the materials being heated by the arc would increase. This would change

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Short-term disturbances Disturbance model Inputs

S

Dynamic model (welding conditions)

Outputs

1.2 A typical dynamic system whose outputs are affected by inputs, disturbances, and system dynamics.

how the inputs generate the outputs through the welding process and the actual system dynamics would change. Hence, just simply adding a deviation to signals in the system to treat the disturbances is an approximation and a simplified treatment. However, if the disturbances are short, this simplified treatment may be effective. When the deviations are constant, the simplified treatment as additive disturbances may not be most appropriate. As a more accurate treatment, the effect of the constant deviations on the dynamic relationship between the inputs and outputs should be considered and be counted in an appropriate way. The most accurate method appears to consider the change of the system dynamics due to constant deviations. As a result, the system dynamic model is now affected by, and varies with, the constant deviations of welding conditions from their nominal constants. When the deviations are not known precisely, the dynamic model is also not known precisely. For systems whose dynamics may be time-invariant but not known precisely, the control algorithm does not know what the precise model is. To the control system designer or the control algorithm, the system becomes ‘uncertain’. Because the system model is not completely known, the control algorithm does not have an accurate prediction about how the system would respond to the inputs. The system is thus further complicated.

1.5

Monitoring of welding processes

As discussed above, the outputs are affected by inputs (welding parameters), disturbances, and welding conditions. It is apparent that an obvious method to produce the desired outputs is to assure that all the disturbances are eliminated or minimized and the welding conditions are maintained at their nominal constants. In this way, the inputs (welding parameters) determined from experiments can be applied. However, to assure that the desired outputs are produced, major inputs and welding conditions must have the values they are supposed to have. One way to make sure of this is to monitor

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all the major inputs (welding parameters) and welding conditions that may change and or have perturbations/deviations from their nominal constants. In case some inputs or welding conditions deviate from their desired values or nominal constants, simple corrections may be used. For example, if there is an error between the actual weld seam and the trajectory of the torch, the torch’s position should be adjusted to eliminate the error. If the welding current is lower than the values which should be, the welding current can be increased to its needed level. Such correction is made to individual inputs and welding conditions and the decision is straightforward. However, if the correction cannot be made to individual inputs or welding conditions (for example, if the groove angle is larger than the nominal angle) or the deviations of the outputs from the desired values are caused by unknown reasons, the decision on what to adjust and how to adjust would be more difficult because of the complexity of the uncertain system. Now we discuss how the process should be monitored to control the welding process as a complex and uncertain dynamic system. We might think that inputs (welding parameters) are the original values that can be directly manipulated and we should always adjust them whenever there is a need to bring the outputs to their desired values. However, although such manipulations are easy, their effects on the final outputs can take several steps to realize. From this point of view, the welding process may be considered to be a few sub-processes in series: each sub-process has its own inputs and outputs as intermediate variables. A possible solution to produce the desired outputs may be to monitor the intermediate variables which are close to the final outputs. For example, to achieve an accurate weld joint penetration as measured by the backside width of the weld bead, we may choose to fix all the inputs (welding parameters) and welding conditions. Under these fixed conditions, we understand when we apply the inputs (welding parameters) as pre-determined, a certain temperature field and a certain three-dimensional shape of the weld pool surface would be produced, if all the welding conditions are kept at their nominal constants. If this temperature field and weld pool surface geometry are obtained, the backside width of the weld bead would be produced at the desired level. In this case, the temperature field and the weld pool surface become the intermediate variables which bridge the inputs to the outputs but are closer to the outputs than the inputs. There are many welding conditions that affect the relationship between the inputs and the weld joint penetration. We can break the process into two cascade subprocesses, for example: sub-process 1, which produces the temperature field and the weld pool surface from the original inputs, and sub-process 2, whose output is the backside width of the weld bead and inputs are the temperature field and the weld pool surface as shown in Fig. 1.3; subprocess 2 will be less complex than the whole process. In this case, we

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Inputs

Temperature field and weld pool surface Sub-process 1 Sub-process 2

9

Back-side width of weld pool (outputs)

1.3 An example of artificial decomposition of complex system.

may monitor the temperature field and the three-dimensional surface of the weld pool rather than directly monitoring the backside width of the weld bead. If the needed temperature field and the weld pool surface are not achieved, the desired backside width may not be achieved and the welding parameters (inputs of sub-process 1) thus should be adjusted to achieve the required temperature field and three-dimensional weld pool surface. In this way, the final output, the backside width of the weld bead, may also be maintained through monitoring and control of the less complex system sub-process 1. The above example suggests that the process can be monitored at different levels. At the lowest level, the system inputs (welding parameters), such as the waveforms of the welding current and arc voltage, the wire feed speed, and the shield gas flow rate, can be monitored to make sure they are correct. If they are not correct, adjustments would have to be done to individual parameters in real time. At the second level, the welding conditions such as the weld seam and joint groove geometry may be monitored to make sure that the nominal conditions are present. At the third level, the intermediate variables can be monitored. These are controlled by the welding parameters and are affected by the welding conditions, which are not the final outputs but have a closer relationship with the final outputs than the welding parameters and welding conditions such as the temperature field and the weld pool surface as mentioned above. The relationship of these intermediate variables with the outputs is also affected by the welding conditions but such welding conditions typically may not be measurable or are difficult to measure. For example, when the temperature field and the three-dimensional surface of the weld pool are given, the actual back-side bead width may still be affected by the actual thickness of the plate, which may vary a little from the nominal constant, and the actual chemistry of the material, which may vary a little from its nominal constants. The thickness and chemical composition can also be considered as the welding conditions but they are difficult to measure. If the deviations from the nominal constants are insignificant and their effects on the relationship from the intermediate variables to the final outputs are negligible, intermediate variables (temperature field and weld pool surface), rather than the welding conditions (thickness,

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Monitoring Welding level 1 conditions Inputs

Welding conditions

Monitoring level 2

Sub-process 1

Monitoring level 2

Sub-process 2

Welding conditions

Monitoring level 3

Sub-process n

Outputs

Intermediate variables Monitoring level 3

Monitoring level 4

Monitoring level 3

1.4 Artificial decomposition of complex system for effective monitoring and control.

chemical composition), may be monitored. If these welding conditions vary significantly from their nominal constants, the outputs would need to be monitored directly. Hence, from a certain point of view as mentioned above, the monitoring of the welding process can have four major levels and the welding process can be decomposed into a few sub-processes. A major task to find an engineering solution to the problem is to determine how the system should be decomposed and where and at what level the monitoring needs to be conducted. Because of the complexity of the welding process as a system as mentioned earlier, monitoring the final outputs as the feedback to directly adjust the welding parameters may present a very complex control system design problem and would need appropriate training in order to design appropriate control algorithms to determine how the welding parameters should be adjusted. By decomposing such a complex control system into a few sub-processes and monitoring the disturbances, inputs, and outputs of each sub-process as shown in Fig. 1.4, the design of a complex system becomes the design of a few simpler systems. Such artificial decomposition of a complex system may greatly help us to find better strategies for the control of the complex welding process through the use of real-time monitoring technologies.

1.6

Overview of the book

This book is organized into two main parts. In the first part, the basic technologies used in the monitoring of welding processes and their principles are discussed. The emphasis is on the basic principles of the measurement technologies rather than how they are applied to resolve specific issues in the monitoring of welding processes. We have chosen four basic sensor technologies: arc sensor, optical sensor, infrared sensors, and ultrasonic sensor. In the second part, the emphasis is on particular objectives in welding

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process monitoring. These objectives have been selected from two points of view: needs that are common in weld and needs of specific processes. For common needs, arc length, weld seam, weld profile/geometry, temperature distribution, weld penetration, and weld pool surface are discussed in detail. For needs of specific processes, resistance welding and laser welding processes are selected. No specific arc welding processes are included because all the common needs are for various arc welding processes. Some interesting topics such as the automated monitoring of metal transfer process in gas metal arc welding (GMAW) have not been selected because they are still under development [9].

1.7

References

1 z. bingul, g.e. cook, ‘A real-time prediction model of electrode extension for GMAW,’ IEEE-ASME Transactions on Mechatronics, 11 (1): 47–54, 2006. 2 j.m. istre, y.m. zhang, ‘Predictive generic model control for non-linear interval systems with application in arc welding,’ Special Issue on Advanced Sensing, Modelling and Control of Welding Processes, International Journal of Modelling, Identification and Control, 1 (2): 140–150, 2006. 3 c.h. tsai, k.h. hou, h.t. chuang, ‘Fuzzy control of pulsed GTA welds by using real-time root bead image feedback,’ Journal of Materials Processing Technology, 176 (1–3): 158–167, 2006. 4 x.x. huang, s.b. chen, ‘Using support vector machine for modeling of pulsed GTAW process,’ Intelligent Data Engineering and Automated Learning Ideal 2005, Proceedings Lecture Notes in Computer Science, 3578: 155–163, 2005. 5 w. lu, y.m. zhang, w.-y. lin, ‘Nonlinear interval model control of quasi-keyhole arc welding process,’ Automatica, 40 (5): 805–813, 2004. 6 d.b. zhao, s.b. chen, l. wu, m. dai, q. chen, ‘Intelligent control for the shape of the weld pool in pulsed GTAW with filler metal,’ Welding Journal, 80 (11): 253S–260S, 2001. 7 z. bingul, g.e. cook, a.m. strauss, ‘Application of fuzzy logic to spatial thermal control in fusion welding,’ IEEE Transactions on Industry Applications, 36 (6): 1523–1530, 2000. 8 y. m. zhang, r. kovacevic, ‘Neurofuzzy model based control of weld fusion zone geometry,’ IEEE Transactions on Fuzzy Systems, 6 (3): 389–401, 1998. 9 z.z wang, y.m. zhang, ‘Image processing algorithm for automated monitoring of metal transfer in double-electrode GMAW,’ Measurement Science & Technology, 18: 2048–2058, 2007.

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Part I Monitoring technologies

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2 Arc sensors in weld monitoring G. E. COOK, A. M. STRAUSS, D. H. LAMMLEIN and P. A. FLEMING, Vanderbilt University, USA

2.1

Introduction

Arc sensing has many applications in automated welding. Some of these, such as automatic voltage control, date back over 40 years. The advantage of arc sensing is that by using the arc itself as a sensor there is no need for external sensors with their associated costs and concern for reliability in the harsh welding arc environment. In this chapter, a historical perspective of arc sensing will be given first. This will include the use of arc sensing in: (a) automatic voltage control with gas tungsten arc welding for arc length control, (b) consumable electrode processes for arc length control, and (c) seam tracking and fill control as demanded by automatic pipe welding, robotic welding, and other critical automated welding applications. This historical perspective will be followed by a discussion of nonconsumable electrode processes and consumable electrode processes vis-àvis steady-state and dynamic models important in understanding the design constraints and limitations of the arc sensing approach. The welding power supply will be considered in terms of its requirements for signal processing, control response speed, and pulsed-current operation. The control approaches will address the different ways in which the sensed arc signal may be processed and used for arc length control, seam tracking, and fill control. Finally, a section on future trends will address what advances based on arc sensing may be expected in the future.

2.2

Background

All of the applications of arc sensing can be related to the observation that the arc voltage and/or the arc current change as a function of the spacing between the welding torch and the part being welded, referred to here as the workpiece. For the non-consumable electrode gas tungsten arc welding (GTAW) process, a constant current welding power supply is normally 15 WPNL2204

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employed. A typical argon-shielded gas tungsten arc is shown in Fig. 2.1. If the spacing between the tungsten electrode and the workpiece changes, the arc voltage will change proportionately. This suggested that for automatic welding the welding torch could be attached to a servomechanism that varies the spacing between the tungsten electrode and the workpiece to make the sensed arc voltage equal to a desired reference voltage, as shown in Fig. 2.2. This seemingly simple feedback control system has been used for many years and is referred to as automatic voltage control (AVC). As

2.1 Gas tungsten arc.

Servo motor

Welding torch

Shielding gas

Vref AVC Varc Tungsten electrode

Shielding gas

Arc

Larc Workpiece

Work clamp

2.2 GTAW system with AVC.

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Water coolant

Constant current power source

Arc sensors in weld monitoring

17

will be shown later, this control system is not quite as simple as it first appears because the change in arc voltage per unit change in spacing is a non-linear function of the welding current. Hence, the feedback control system may have an optimal gain at normal welding current but an excessively high gain at low welding current. For critical applications, the high gain at low welding current may result in servo instability at the start and stop portions of the weld where the current is typically ramped to a low value to produce crack-free starts and stops. The change in arc voltage as a function of arc length with the nonconsumable electrode processes not only suggests the ability to control the arc length with an automatic voltage control system. It also suggests that if the electrode is weaved back and forth across a grooved joint, as depicted in Fig. 2.3, the difference in voltage at the extremes of the oscillating motion may be used as a measure of the centering of the electrode over the joint. If the voltages at the extremes of the oscillating motion are equal then it can be inferred that the electrode is centered with respect to a symmetrical joint. On the other hand, if the voltages at the extremes of the motion are not equal then this information can be used by a feedback control system to re-center the electrode over the joint. This gives rise to an automatic joint tracking system based on arc sensing. Additionally, the system may be designed with a variable weave width that is based on a measure of the sensed voltage as compared to a desired reference voltage. In this manner, the width of the weave varies as the width of the joint might vary. This allows one to control the joint ‘fill’ for variable width joints, as well as to track the joint.

Oscillation width W

h(−W/2)

h(+W/2)

h(0)

2.3 Torch position sensing by through-the-arc electrical arc signal variations.

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In the early years, consumable electrode processes (gas metal arc welding, GMAW, submerged arc welding, SAW, flux cored arc welding, FCAW) normally used a constant voltage power supply. This mode of operation results in arc length self-regulation. Self-regulation is characteristic of all consumable electrode welding with a constant voltage power supply. Figure 2.4 shows a schematic diagram of the gas metal arc process. Self-regulation refers to the tendency of the arc length to remain essentially constant, even though the torch contact-tube-to-work distance (CTWD) is varied substantially, the difference being made up by a change in the electrode extension. The sum of the voltage drops across the arc and electrode extension must equal the source voltage. Arc voltage is typically modeled as primarily dependent on arc length: as CTWD is increased, electrode extension grows and current drops so that extension voltage drop, burnoff rate, and arc voltage remain fairly constant. The self-regulation mode of conventional GMAW is unique with respect to the other manual processes, GTAW or shielded metal arc welding (SMAW), where the welder has direct control over the electrode-to-work separation. It is this inherent self-regulation characteristic that makes the constant potential GMAW process so easy to practice by a manual welder, provided the variations in CTWD are less than the electrode extension length. If the variations are greater than this, then the torch position must be changed to accommodate the large variation. For manual welding, this, of course, is the job of the welder, and it is easily accomplished. For some applications of mechanized, or automated welding, the part and fixturing variations are permitted to exceed the nominal electrode extension length. In this case, a closed-loop control system may

Vf

Ls + –

Voc

Rs

i

Power source model

Contact tube

Electrode extension Arc

L, VL h La, Va

Workpiece

2.4 Schematic diagram of the GMAW process.

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be used to sense variations in current and automatically move the torch in or out as appropriate. This is analogous to automatic voltage control with GTAW, only here the control is based on the sensed current, rather than the voltage. The change in current as a function of change in CTWD for constant potential, consumable electrode processes, suggests that a tracking and/or fill control system may be designed in much the same manner as described for the non-consumable electrode processes, but by using the change in current at the extremes of weave motion over a weld joint rather than the arc voltage. As was true of the AVC system, the consumable electrode tracking system is not quite as simple as it might first appear. This is because the dynamics of the self-regulation process affect the tracking system operation. Before leaving the discussion of constant potential GMAW with its inherent self-regulation characteristic, it is noted that with this mode of operation small variations in arc voltage may result in substantial current swings. Arc current is an important physical quantity, however, and influences the process characteristics in a number of ways, i.e. metal transfer, penetration characteristics, wire melting, arc stability, etc. (Allum, 1983). These properties all have different dependencies on arc current so that the current swings introduce a statistical factor into the process which might be expected to influence the output features of the weld (Schellhase and Weinschenk, 1979). This has led to monitoring of the arc signals and statistical analysis for improved process control (Cook et al., 1997; Barborak et al., 1999). Also, the self-regulating constant potential GMAW process is severely limited in its ability to accommodate a wide range of deposition rates with stable spray transfer for a given wire diameter, thereby restricting one’s ability to exercise much control over the output features of the weld. A question that might be asked, not only with respect to arc sensing limitations, but with respect to all of the shortcomings of the constant potential self-regulation process, is why not forsake self-regulation and use constant current welding for automated consumable electrode process applications. This has been done, but this mode of operation has its problems as well. The steady-state burnoff rate equation, first derived experimentally by Lesnewich (1958) and later derived from basic principles by Halmoy (1979) is given by vb = k1j + k2Lj2

[2.1]

where vb is the melting rate or burnoff rate expressed as a velocity, j is the current density, L is the electrode extension length, and k1 and k2 are constants. The burnoff rate equation, Eq. (2.1), is useful in examining the instabilities that might occur. For a given k1 and k2 where neither is negligible, as is the case with steel wire, the electrode extension is well defined

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if vb and j are known. Then, if the wire feed rate and average current are accurately controlled, a unique value of electrode extension will exist, and the system will be stable. However, if k2 is small compared with (k1/Lj), vb ≅ k1j

[2.2]

In this case, which is true for aluminum (typically, k1 ≅ 0.75 mm3/A s and k2 is negligible), the electrode extension L is undefined, and unstable arcing conditions in the form of wire hunting may then occur (Allum, 1983). In this case, the usual approach has been to sense and control the arc voltage by means of a closed-loop control system similar to the AVC system. While the constant current GMAW process with auxiliary automatic arc length control is a reasonable means of overcoming some of the limitations of the constant potential, self-regulating process, it is still limited in its ability to accommodate a wide range of deposition rates with stable spray transfer. This limitation is reduced with the pulsed current GMAW process discussed next. Pulsed current GMAW has been in use since the 1960s (Needham, 1966), but only in the last two decades or so has power supply technology permitted the full capabilities of the process to be exploited. Pulsing the current introduces additional parameters that must be controlled. These include peak current, base current, peak pulse time, and base pulse time. These are in addition to the electrode extension, voltage, and average current (derived from the pulse parameters). By appropriate choice of the pulse parameters it is possible to significantly extend the range of deposition rate (and average current) that may be achieved for a given wire diameter while maintaining stable spray transfer. This greatly improves the ability to control the process to achieve desired weld features. Allum (1983) has presented equations for predicting the stable parametric zone containing all feasible combinations of pulse parameters for any given wire feed rate (or mean current) and droplet volume (or pulse cycle time). The equations include some experimentally determined constants and an experimentally derived general inverse relationship between limiting pulse amplitude and duration. Kim and Eagar (1993) have presented a theoretical framework for better understanding the limits imposed by Allum. With these works and others, it is a relatively easy matter to compute pulse parameters that will produce stable spray transfer for the weld properties desired.

2.3

Non-consumable electrode processes

The non-consumable electrode processes include GTAW and plasma arc welding (PAW). However, most of the comments here will be limited to the GTAW process. As previously stated, traditional AVCs used with auto-

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mated GTAW usually perform adequately in most operating conditions. However, there are conditions during which the traditional systems cause problems and sometimes fail totally in providing acceptable position control. These problems have been traced to two significant characteristics of the welding arc that are not accounted for in traditional AVCs (Bjorgvinsson, 1992): 1 The arc voltage-to-arc length gain, Karc, is not constant but varies with the nominal values of the arc current and voltage. In the closed-loop AVC system, this arc sensitivity affects the dynamic response of the system since it is one of the parameters forming the forward loop of the AVC. 2 The arc voltage-to-gap relationship is not constant but varies with current. Thus, an AVC that has a fixed voltage set point will not maintain a fixed arc length if the arc current changes. Furthermore, during starts and stops of the weld, when the current is ramped from a low value to a high weld value and then back down to a low value, it is important to program the AVC reference voltage to prevent premature arc outages that may result in cracking.

2.3.1 Steady-state models Goldman (1966) described the voltage/current characteristics at constant arc length with an equation of the form: Varc = A + BIarc + C/Iarc

[2.3]

where A, B and C are constants. This equation gives a reasonable fit for the high-current region of experimental data, but it fails to follow the experimental data for the low-current negative slope region observed in experimentally derived voltage/current relationships (Bjorgvinsson, 1992). The Gaussian characteristics of the arc and the shape of the experimental relationship suggested that an exponential function of current, dominating in the low-current region and a straight line dominating in the high-current region might provide a better fit to experimental data. Combining these two functions gives an equation of the form: Varc = K1 + K2Iarc + K3exp(K4Iarc)

[2.4]

Bjorgvinsson (1992) found that this relationship gave a close fit to experimental data by choosing the right constants K1, K2, K3, and K4. However, if the arc length is changed, a new set of constants has to be established so the constants are clearly dependent on arc length. Based on experimental data, Bjorgvinsson approximated K1, K2, and K3 as linear functions of the arc length Larc, and K4 as a constant. He then differentiated Eq. (2.4) with

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respect to Larc to obtain an expression for the arc gain, Karc = dVarc/dLarc. The arc gain, or arc voltage sensitivity, is shown in Fig. 2.5 for a given set of conditions with argon shielding gas. An even greater change in Karc as a function of Iarc is observed for helium shielding gas.

2.3.2 Dynamic models Normally, the AVC system employs a typical dc servomotor to move the welding torch for a desired arc length. The characteristics of this servomotor are expressed by the transfer function Gs in the closed-loop proportional control system shown in Fig. 2.6. Here, Ks and as are constants characterizing the physical properties of the servomotor, Vref is the voltage signal to the servomotor, and Larc is the electrode-to-workpiece distance. In an AVC system the arc length, Larc, is indirectly sensed through the arc voltage, Varc, while the arc current is constant. This arc voltage and arc length relationship can be represented by the arc voltage-to-length sensitivity, Karc,

1.2

dV/dL (V/mm)

1.1 1.0 0.9 0.8 0.7 0.6

0

20

40

60

80 100 120 140 160 180 200 Arc current (A)

2.5 Calculated arc gain Karc for argon shielding gas.

Gs Vref

Larc

Ks

+ −

s(s + as)

2.6 Traditional closed-loop AVC.

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Karc

Varc


23

denoting a change in arc voltage for a change in arc length, Karc = dVarc/dLarc. The closed-loop transfer function of the AVC is given as: Garc =

Varc Ks Karc Ks Karc = = Vref s 2 + as s + Ks Karc s 2 + 2ζω n s + ω 2n

[2.5]

The above equation clearly shows that the system is second-order with a damping ratio ζ = as / 2ω n = as / 2 Ks Karc . Therefore, increasing Karc will reduce the damping ratio, resulting in a higher overshoot in the step response. In general, the AVC is designed for an optimal response with a constant Karc. But, as detailed in the previous paragraphs, in practice, Karc is not constant but varies as a function of the arc current and arc voltage. Therefore, the traditional AVC cannot provide adequate control when the arc current is varied over a wide range. In the worst case, the AVC may be unstable driving the torch into the weld pool (Bjorgvinsson, 1992; Koseeyaporn, 1999). Also, control of the arc length, to reduce the probabilities of cracking during tail-out, is usually impossible by using a traditional AVC. Bjorgvinsson (1992) proposed a gain-scheduling adaptive controller (see Fig. 2.7) to vary the controller gain in such a manner as to compensate for the changing arc sensitivity for all levels of welding current. Knowing the arc current, the adaptive controller uses information stored in a look-up table or computed from a mathematical model of the arc to adjust Ks in response to changes in Karc such that the product KsKarc is maintained constant independent of the current. The result is uniform closed-loop stability

Arc voltage reference

Servo amplifier

Controller

Gain scheduling adaptive controller

Reversible servo motor Signal conditioner

Arc sensor = K(l) = dV/dL Constant current power source

Voltage Current

2.7 Gain-scheduling adaptive automatic voltage control.

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characteristics of the AVC system throughout the complete weld. This includes the up-slope period, when the current is varied from the low arcinitiation value to the nominal welding current, which is maintained until the down-slope period, when the current is brought back to a low value for termination of the arc. While the gain-scheduling adaptive controller employed by Bjorgvinsson (1992) was demonstrated to work well under controlled conditions, the functional relationship between arc sensitivity and current is dependent on a number of parameters that are difficult to preprogram in the look-up table or to find from the mathematical relation he used. To circumvent this problem, Koseeyaporn et al. (2000) and Smithmaitrie et al. (2000) used adaptive schemes that did not require any a priori knowledge of the current sensitivity relationship. Koseeyaporn et al. (2000) used the partitioned control method employed by Craig (1988) for adaptive control of robot manipulators. With this approach, the controller is partitioned into a non-linear part chosen to cancel non-linearities of the system and a servo part. The non-linear part is chosen such that the servo part sees a simple unit-inertia system. The adaptive estimator, chosen to ensure Lyapunov stability, provides estimates of the unknown parameters in the non-linear part of the portioned controller. A block diagram of the adaptive controller is shown in Fig. 2.8. Smithmaitrie et al. (2000) used a fuzzy logic controller with two inputs (voltage error and derivative of voltage error) and one output (servomotor voltage) for the basic inner-loop AVC. The min/max inference and center of gravity defuzzification method (Passino and Yurkovich, 1998) was used in implementing the fuzzy logic controller. The outer adaptive loop consisted of an identical inverse fuzzy logic controller with two inputs, as shown

+

vref

v¢m

Σ +

c2

+

kp

+

Σ

c1

vref +

c2va + c1va = vm v a

kv

Adaptive law

e

Σ

va

+

e

vref

vm

+

Σ

–

–

2.8 The AVC system with a partitioned adaptive controller.

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Reference model

Inverse fuzzy model

+

−

G0

(1−Z–1)/T G1

Rule-base

ye(t)

(1−Z–1)/T Gyc

+

− y(t)

Fuzzy controller Inference mechanism

Rule-base

Defuzzification

Verror

Fuzzification

Vref

Inference mechanism

Fuzzification

Defuzzification

GP

ym(t) Gye

Plant H

Varc

2.9 Adaptive fuzzy control for arc voltage control in GTAW.

in Fig. 2.9. The difference between the arc voltage and the model arc voltage, ye(t), and its derivative are the inputs to the fuzzy inverse model. For given information (from the inverse model) about the necessary change in the input needed to make ye(t) approximately zero, the knowledge-base modifier changes the knowledge base of the fuzzy controller so that the previously applied control action will be modified by the amount specified by the inverse model output. To modify the knowledge base, the knowledgebase modifier shifts the centers of the output membership functions for the rules that were active during the previous control action. Both the adaptive controller of Koseeyaporn et al. (2000) and that of Smithmaitrie et al. (2000) permitted AVC over the full operating range of welding current from low starting values to normal welding values to low termination values. Both approaches proved more robust than that of Bjorgvinsson (1992) based on a preprogrammed relationship between current and arc sensitivity.

2.4

Consumable electrode processes

The consumable electrode processes include GMAW, SAW, and FCAW; however, most of the comments here will be limited to the GMAW process. The consumable electrode processes may use constant potential power supplies, constant current power supplies, or pulsed current power supplies.

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2.4.1 Steady-state models Halmoy (1979) developed a burnoff rate model that allowed him to calculate not only an ‘effective anode heating voltage’, but also the temperature of discharged droplets. His model is in the form of an energy balance equation: Ho = HA + HL

[2.6]

where Ho is the heat carried off by the droplets, HA is the anode heating, and HL is joule heating. Ho in this equation has units of J/mm3, where volume is referred to the room temperature density of the electrode material. The joule heating model uses the fact that there is a linear relationship between HL and the ‘action’ integral above HL ≈ 4 J/mm3. Halmoy designates the slope of the f(H) curve as 1/a, and the intercept by b/a; the action (time integral of current squared, in steady state) is given by Lj2/vf, so that: Lj 2 1 b = HL + vf a a

WL =

[2.7]

Power input from arc heating is taken as Iφ, where Halmoy calls φ the ‘effective anode potential’. Anode heating is thus given by: iφ vf A

HA =

[2.8]

Combining Eqs (2.6)–(2.8) gives: Ho =

iφ aLj 2 + −b vf A vf

[2.9]

Solving for vf: vf =

φ a j+ Lj 2 Ho + b Ho + b

[2.10]

Halmoy observes that this is of the same form as Lesnewich’s equation (Eq. 2.1), and concludes that the coefficients of the j and Lj2 terms must be constant. Because a and b are constants of the electrode material, independent of the welding process, he further concludes that φ and Ho must be constants, and given by; φ=a

k1 k2

Ho =

a −b k2

[2.11]

For values of HL above 4 J/mm3, Halmoy derives the following equation for voltage along the electrode extension:

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Arc sensors in weld monitoring VL = aLJ −

bvf J

27 [2.12]

2.4.2 Dynamic models As previously suggested, successful application of arc sensing to joint tracking with the consumable electrode processes requires an understanding of the process dynamics. The electrode melting rate is one of the most important parameters in the GMAW process, because arc stability depends on how closely the electrode federate and the electrode melting rate are balanced to maintain a constant arc length. For constant potential GMAW, arc length regulation is an inherent part of the self-regulation process in this mode of operation. The dynamic behavior of selfregulation can be qualitatively illustrated by referring to Fig. 2.10. At point A0 equilibrium is established between arc length and electrode extension. Welding current and arc voltage are as shown in the lower part of the figure. A sudden increase in CTWD is depicted by point A1; initially, the entire change occurs in arc length. The higher arc voltage causes a reduction in current and a reduced burnoff rate. The electrode extension increases until a new equilibrium is established at A′0. The equilibrium

A0

I1

I0′

I0 I2

A1 Voltage

A0

A2

A0′

A1

A0 A 0′ A2

I1

I0′

I0

I2

2.10 Arc variations over idealized stepped plate.

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current is lower than at A0 due to greater resistive heating in the longer electrode; arc voltage is slightly lower due to lower arc current. With a step decrease in CTWD, as seen with motion to point A2, the reverse takes place. Shepard (1991) used the assumptions that heat input to the electrode is only from joule heating and anode heating and that heat is removed from the electrode only by discharged droplets to study the constant potential, self-regulation GMAW process in a more quantitative manner. He furthermore assumes that joule heating is completely described by action (time integral of current squared), and anode power input is proportional to current. He expresses Eq. (2.9) in a different form based on Eqs (2.6)–(2.8): Ho =

iφ + aWL − b vb A

[2.13]

Shepard substitutes vb for vf in Eq. (2.8) since in dynamic conditions, it is the volume of electrode material passing through the anode region (i.e. burned off) which is heated by anode power. The explicit current dependence is only in the anode heating term, which is instantaneously proportional to current; the ‘memory’ of current history is captured in the WL term, so that this relationship is valid for time-varying current as long as HL ≥ 4 J/mm3. The equation is rewritten as: ⎛ φ ⎞ ⎟ ⎜⎝ k1 Ho + b ⎠ vb = j= j a ⎞ 1 − k ⎛ 2WL 1− ⎜ W ⎝ Ho + b ⎟⎠ L

[2.14]

In steady state, where Eq. (2.7) holds, this reduces to Lesnewich’s equation. The complete set of equations used by Shepard (1991) to describe the self-regulation model are summarized here. The state equations are: di Voc − Rl i − Varc − iRs = dt Ls

[2.15]

dl = vf − vb dt

[2.16]

The arc voltage is given by: Varc = Vo + Rai + Eala

[2.17]

The electrode resistance is:

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Arc sensors in weld monitoring RL = ∫

L

0

ρ(l ) dl A

29 [2.18]

where the resistivity is computed from the electrode action profile: ρ(l) = c0 + c1W(l) + c2W2(l) ρ(l) = d0 + d1W(l)

(0 ≤ W ≤ WT)

(WT ≤ W)

[2.19a] [2.19b]

The action is defined as (although not computed from): W (l ) = ∫

0

−Tl

j 2 ( t )d t

[2.20]

where the electrode travel time, Tl, satisfies the relationship: t

l (t ) = ∫ vf ( τ )dτ

[2.21]

−Tl

or Tl = l/vf for constant federate. The burnoff rate is given by: vb(t ) =

k1 j (t ) 1 − k2Wl(t )

[2.22]

where WL is the action at the electrode tip, and where: Voc: open circuit power source voltage Rs: power source effective resistance Ls: power source effective inductance Ra: current dependence of arc voltage Ea: length dependence of arc voltage A: electrode cross-sectional area l,L: electrode length la: burnoff rate constants k1,k2: burnoff rate constants vf: wire feedrate Shepard (1991) ran simulations and conducted experiments to measure the sensitivity of welding current to changes in CTWD as a function of perturbation frequency. The experimental conditions were chosen to illustrate the variation of dynamic response with electrode current density. Four sets of data were presented, two with 0.035″ (0.89 mm) and two with 0.062″ (1.57 mm) diameter steel wire. Dynamic response of self-regulation was determined experimentally by measuring the frequency response of current to sinusoidal CTWD perturbations between 0.5 and 18 Hz. Data were taken at two different currents with each of the two selected wires. The same experiments were run in simulation, and agreement was demonstrated with the experimental data. For 0.035″ (0.89 mm) diameter wire, the frequency

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response was flat until approximately 2 Hz, at which point it increased to a peak at approximately 10 Hz and then began to roll off in magnitude. For 0.062″ (1.57 mm) diameter wire, the frequency response was flat until approximately 0.3 Hz, at which point it increased to a peak at approximately 2 Hz and then began to roll off in magnitude. In both cases the roll off at higher frequencies was attributed to total circuit inductance. The application that originally motivated Shepard’s work was that of seam tracking using arc sensing. For this application, it is advantageous to maximize the current change observed in response to the effective CTWD change occurring on approach to joint sidewalls. It is clear from Shepard’s results that the static sensitivity (observed prior to the break frequency) falls within a fairly narrow range regardless of procedure. When compared with average currents, the normalized static sensitivities were observed by Shepard to range from 1.6%/mm to 7.2%/mm, which compared well with Cook’s (1983) previous finding of 2%/mm to 6%/mm. Shepard found, however, that significant increases are available from the dynamic response of self-regulation. It is furthermore clear from these results that the use of large wire and low feedrates can provide this enhancement in two ways. Firstly, the available maximum sensitivity is large because of the low total circuit resistance. Secondly, the dynamic sensitivity becomes effective around 1 Hz, a frequency achievable in crossseam oscillation. Shepard’s results give further insight into the effects of power source output impedance on self-regulation, and provide a basis for optimizing this characteristic. Several researchers (Nomura et al., 1994; Sugitani et al., 1994; Dilthey and Gollnick, 1998; Shi et al., 2005) have taken advantage of the greater sensitivity of current to CTWD change at higher frequencies by using a rotating torch mechanism. The electrode is eccentrically located with respect to the center line such that when the mechanism is rotated the arc traces a circular path with respect to the weld center line. By synchronizing the sampling at each side with the rotating mechanism, the samples are taken at each extremity of travel toward the sidewalls. Since the rotating torch mechanism may be rotated continuously, rates of as much as 30–50 Hz may be used. This takes full advantage of the I/CTWD sensitivity observed by Shepard (1991) to occur at higher frequencies. For constant current and pulsed current GMAW, arc length regulation must be provided by sensing and controlling the voltage. Design and operation of the regulation system requires an accurate dynamic melting rate model. A number of researchers have proposed dynamic models for this purpose. Richardson et al. (1994) showed that the response rate of the power supply has a notable influence on the melting rate when the joule heating in the wire increases. Quinn et al. (1994) developed a simplified dynamic model for electrode extension and found the dimensionless parameters that

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represent the dynamic behavior of the system around the steady-state solutions. Bingul et al. (2001) have developed a model that correlates the anode temperature profile with the dynamic melting rate in GMAW. The components of this model are the electrode melting rate, the temperaturedependent resistivity of the electrode, and the arc voltage. The differential equations describing the dynamic behavior of the electrode extension were derived from the mass continuity and energy relations. The temperature of the electrode extension was determined by analyzing conductive heat transfer and joule heating effects. One-dimensional solutions for the temperature and heat content were used to obtain the dynamic melting rate equation. Shi et al. (2005), Kim and Na (2001), and Yoo (2004) have used Eq. (2.12) from Halmoy to develop the differential equations to simulate the wire extension and welding current during GMAW.

2.5

Power supply considerations

The technology of constant current and/or constant potential welding power supplies has improved to the point that vis-à-vis the welding system they may be considered ideal components. The ripple is very small, the bandwidth is well in excess of 1 kHz, and they very accurately deliver the voltage or current asked for. For pulsed current power supplies used for pulsed GMAW, great strides have also been made, although there remains room for improvement. This is because pulsed current GMAW is not inherently self-regulating, but instead requires arc sensing and feedback control of arc length. And, for optimum performance, this requires a thorough understanding of the process dynamics. The pulsed GMAW process is shown schematically in Fig. 2.11. The basic power source is typically a high-performance inverter unit capable of either constant current or constant voltage operation. The power source is normally operated in the constant current mode although there have been some commercially available pulsed GMAW machines that switch between constant current and constant potential during the pulse on–off cycle. The pulsing controller is shown separate from the basic power supply even though the two are normally provided as an integrated package commercially. Most commercial providers of pulsed GMAW equipment provide a means of easily setting the pulse parameters for a given set of welding conditions. The user can accept the parameters suggested by the equipment provider, or all of the pulse parameters may be set individually. In the absence of the arc length regulation feedback, shown in Fig. 2.11, the pulsed GMAW process does not possess any self-regulation features that might impede the flexibility of the process with respect to weld output control. For mechanized or automated applications, arc length regulation may be unnecessary provided the robot, or torch positioning system, is

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Pulsed GMAW power source/controller User inputs

Pulse current control with arc length regulation

Highperformance constant current power source

Sensed voltage for arc length regulation

Vf

Contact tube

i

Electrode extension Arc

Workpiece

2.11 Pulsed GMAW schematic.

programmed to accurately maintain a constant CTWD. If this is not the case, then two approaches are possible. One approach is to sense the voltage (average voltage, peak voltage, or background voltage) and use it in a closed-loop control system to move the torch in or out as appropriate to maintain the voltage measure constant. The disadvantage of this approach is, of course, that an auxiliary servo system is required to position the torch in or out. The advantages are that its range of control is limited only by the limits of the positioning servo system, and that it does not require changing any of the pulse parameters. The second approach is depicted in Fig. 2.11. It is based on changing one or more of the pulse parameters to keep the voltage measure constant. In this case, the torch is not moved. Instead, the electrode extension changes as required to make up for the change in CTWD. No auxiliary positioning system is required. Obviously, the range of correction possible with this approach is less than the electrode extension, as is the case for the constant potential GMAW process with self-regulation. A disadvantage of this approach is that the pulse parameters are changed from the nominal values established by the user, and less than desirable weld characteristics may result for large corrections. Commercial manufacturers normally provide the arc-length regulation system depicted in Fig. 2.11. It serves the same purpose as self-regulation in constant potential GMAW. This form of self-regulation is essential for

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ease of manual welding, and is frequently used in automated or mechanized welding, as well. To gain additional insight into the self-regulation approach depicted in Fig. 2.11, welding across an idealized step plate will be considered. Two basic methods, with a number of variants, of using the pulse parameters to regulate the arc length are feasible. One method is based on varying the pulse frequency, the other on varying the pulse peak current. Arc length regulation based on pulsed frequency variation is depicted in Fig. 2.12. Here it is assumed that the background time is varied in response to voltage error. This results in changing the pulse frequency while maintaining the pulse duration fixed. The duty cycle, of course, changes with this approach. The sensed voltage may be the average voltage, peak voltage, or background voltage with some differences depending on the choice made. Referring to Fig. 2.12, at point A0, equilibrium is established between feedrate and burnoff rate and between arc length and electrode extension. A sudden increase in CTWD is depicted by point A1 with the entire change initially occurring in arc length. The sensed increase in voltage results in a decrease in the background time, and hence pulse frequency. The decrease in pulse frequency means that the energy per unit time (power) delivered to the electrode is reduced. This means that an incremental volume of the

A0

A2

A0′

A1

A0

Current

Current

Time Nominal pulses/s at A0

Time Decreased pulses/s at A0′

2.12 Arc length regulation via pulse frequency modulation.

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Real-time weld process monitoring Table 2.1 Representative pulse parameters 0.035″ (0.89 mm) diameter wire, 98 Ar–2 O shielding gas vf

Ip

Ib

Tp

fp

17.8 2.5

480 300

100 45

1.9 1.5

200 40

Ip = peak current (A), Ib = background current (A), Tp = pulse duration (s), fp = pulse frequency (pps), vf = wire speed (meters/min). Electrode extension = 16 mm.

electrode must travel farther from its cold state at the contact tube exit before reaching its melted state at the end of the electrode. The electrode extension is thus increased, bringing the arc length back to approximately its value prior to the step change. With a step decrease in CTWD, as seen with motion to point A2, the reverse takes place. The responsiveness of the arc length regulation system can be estimated in a qualitative sense by considering the time for an incremental volume of the electrode to travel from the contact tube to the electrode’s molten end. Table 2.1 gives representative values of pulse current parameters for high and low wire feedrates. The data is for 0.035″ (0.89 mm) diameter, ER70S-3 wire with 98% Ar–2% O shielding gas. Referring to Table 2.1, the time required for an incremental element of wire to move from the contact tube to the molten end of the wire is 0.054 s for vf = 17.8 m/min and 0.384 s for vf = 2.5 m/min. The numbers of pulses that occur in these times and that hence contribute to the heating history of the electrode are 10.8 and 15.4, respectively. If the electrode travel times are taken as a rough estimate of the time response of the regulation system, then the response time and equivalent frequency response for the high and low wire feed speed cases are 0.054 s (18.5 Hz) and 0.384 s (2.6 Hz), respectively. Certainly, these are just rough estimates of the regulation dynamics. More accurate estimates require a complete dynamic burnoff rate model and specific modeling of the closed-loop control. The sensed voltage averaging method (average over full cycle, pulse time, or background time) as well as other parameters of the regulator will affect this. Arc length regulation based on peak current variation is depicted in Fig. 2.13. Here the peak current is varied in response to voltage error. In moving across the step, the system operates as before. When the torch moves from A0 to A1, the sudden increase in voltage is sensed and the regulator reduces the peak current level. This reduces the energy per unit time delivered to the electrode, resulting in the increased electrode extension shown at position A′0. As before, the arc length is then approximately equal to its value

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A0

A1

A2

A0′

Current

35

A0

Current

Time

Time Decreased pulse current at A0′

Nominal pulse current at A0

2.13 Arc length regulation via pulse current modulation.

prior to the step change. Also, as before, with a step decrease in CTWD, the reverse action takes place. In both of the arc length regulation approaches considered, a small change in average welding current results from the change in pulse parameters to accommodate the change in CTWD. In applications using arc sensing, this change in current could be used as the sense signal for seam tracking purposes. On the other hand, clearly if the arc length regulation system is not activated, then sensed voltage would be the appropriate signal for seam tracking purposes. To optimize the arc sensing and control system for seam tracking purposes, it is important that the engineer understand the arc regulation dynamics and its effect on the nominal pulse parameters. The two arc length regulation approaches considered here have a number of variants that have been used. As already stated, some of these variants are characterized by the sensed voltage signal (average voltage, peak voltage, or background voltage). The two methods considered may also be advantageously combined to reduce the possibility of moving outside the zone of stable operating conditions. For example, if the regulation method based on pulse frequency modulation is used alone, large corrections may call for a change in pulse duty cycle (assuming the frequency is changed by varying the background time with a fixed pulse duration) that are not

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contained within a stable zone of operation. In this case, if the frequency variation were combined with the pulse current variation, the change required in each might remain in the stable zone. Any number of methods might be used for combining the two. For example, one method would be to vary the pulse frequency first and only vary the pulse current if the pulse duty cycle falls outside of a preset range. The dynamic response of electrode extension to step changes in average current for pulsed GMAW were measured and compared with a dynamic model by Krepp et al. (2005). The experiments were conducted at wire feedrates varying from 150 (63.5 mm/s) to 350 inch/min (148.2 mm/s) in increments of 50 inch/min (21.2 mm/s). The step changes in average current were obtained by imposing a step change in peak current with all other pulsed parameters held constant. With the exception of the experiments for 150 and 200 ipm (63.5 and 84.7 mm/s), the initial and final pulsed parameters were chosen to achieve an approximate detachment rate of one droplet per pulse. The imaging system used to capture the electrode extension variations consisted of a high-speed digital camera with a macro-lens viewing system. The electrode extension response was found to be a second order variation with close correlation to a second order non-linear model developed by Halmoy (1986). The settling time of the dynamic variation in electrode extension to step changes in average current were found to be approximately linearly related to the wire feed speed.

2.6

Signal processing and control approaches

A seemingly endless number of approaches to extracting information from the arc signals have been considered for estimating and controlling the position of the welding arc both laterally and vertically with respect to the weld joint. Even with all of the work that has been done, embedded computers and digital signal processing (DSP) technology continue to improve and provide new opportunities for optimizing the information that may be extracted from the arc signals for the purposes of positioning the torch as well as monitoring and controlling the characteristics and quality of the weld in real time.

2.6.1 Differential method The simplest and most widely used arc sensing and control scheme can be described as the differential method. This method consists of the following procedure. First, during a given torch oscillation cycle, the arc signal (voltage, current, or impedance as appropriate) is sampled during the right-hand dwell, center, and left-hand dwell portions of the torch trajectory, as shown in Fig. 2.14. These three arc signal samples are then converted to torch-to-

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Joint

37

Center arc signal sampled

Left-hand arc signal sampled

Expected Actual

Trajectory correction

Trajectory starting point

Right-hand arc signal sampled

2.14 Torch trajectory with differential sensing and control.

work distances using relationships previously described. The cross-seam torch position relative to the joint is computed, which is a function of the difference of the two sidewall samples. Similarly, the relative vertical position is computed, which is a function of the center sample. Once these relative positions have been computed, the positional errors are calculated in both directions, so that the torch trajectory is altered to (1) realign the center of oscillation with the center of the weld joint, and (2) adjust the vertical position of the torch to equal a desired and pre-set vertical height above the joint. The differential method can be extended to control the oscillation width to accommodate a widening or narrowing joint. One approach to doing this is to integrate the sensed signal from the center of oscillation and compare that with a preset reference width. Upon reaching this reference, the direction of oscillation travel is reversed, and the process is repeated in the opposite direction. At each oscillation cycle, the center of oscillation is reestablished for the next cycle. In this manner, not only is the joint tracked, but varying joint width is accommodated as well.

2.6.2 Template matching As has been described, a characteristic signal at an electric welding arc, such as the average welding current or average arc voltage is proportional to the electrode-to-workpiece spacing. Hence, by weaving the arc back and forth across the weld joint, the joint geometrical profile may be obtained. The most general approach to doing this is based on template matching. To explain the method employed, let t(x) equal the template signal as a function of displacement x with respect to the center of the arc weave pattern, s(x) equal the measured arc signal as a function of displacement x,

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and W equal the total width of weave from one side to the other. For centering control, the error criterion is given by: εc = ∫

0

−W 2

t ( x ) − s ( x ) dx − ∫

w2

0

t ( x ) − s ( x ) dx

[2.23]

and the width-control criterion (if used) is given by: εW = ∫

W 2

−W 2

t ( t ) − s ( x ) dx

[2.24]

The centering correction called for is thus made proportional to εc, i.e. center correction = kcεc; and the width correction is made proportional to εW, i.e. width correction = kWεW. The template match is then continuously obtained by the self-nulling action of the feedback system. As can be seen from Eq. (2.23), the centering control is obtained by comparing the template/signal match on one side of the weave pattern with that on the other side of the weave pattern. On the other hand, the widthcontrol correction signal is obtained by matching the template and signal over the full width of the weave pattern. In either case, the template signal may be analytically predetermined, empirically determined, in a test groove, or made up of a running average of signal data from a predetermined number of prior weave cycles. In the latter case the template signal may be continuously updated by discarding the oldest data and inserting the most recently acquired data for use in computing a running average for the template signal. Other measures of the degree of match between the template t(x) and the signal s(x) could be employed. These include the integrated difference and the integrated difference squared errors. The integrated absolute criterion chosen, however, proves to be a reasonable compromise between computational complexity and sensitivity to mismatch. Vertical, in–out, positioning control is accomplished by comparing s(x), sampled and averaged at and near the weave center-position, with a torchto-work reference signal. Thus, proper spacing, as well as centering, of the torch with respect to the weld groove is achieved. Additionally, the width of the weave pattern may be made to track variations in the joint width. In this case, the wire speed and/or travel speed may be varied to maintain a uniform depth of weld. Further details of both the differential control approach and the template matching approach can be found in Wells (1988).

2.6.3 Fuzzy logic control Bingul et al. (2000) reported a fuzzy logic controller use in a joint tracking system with arc sensing. The system was implemented with pulsed GMA welds under a variety of conditions. The goal was to obtain quick and accurate response to tracking errors in the presence of disturbances. A series of

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experiments were conducted to evaluate the performance of the fuzzy logic controller. The experimental results show that the fuzzy logic controller was suitable for these purposes. With positioning errors normally encountered in practice, the control system proved to be capable of tracking the joint with a maximum error of approximately ±0.5 mm. Fujimura et al. (1994) conducted tests with both PI (proportional plus integral) control and fuzzy control for seam tracking with arc sensing. The workpiece was set at an offset angle of about 3° with respect to the nominal torch path, and the vertical offset was about 2°. The workpiece length was 100 mm. Their results showed that the fuzzy control approach was better than the conventional PI control approach. Ohshima et al. (1994) combined a neural network with a fuzzy controller for seam tracking with arc sensing. The neural network was used to change the membership functions of the fuzzy variables as a function of the welding angle of the joint with respect to the nominal torch path. They validated their approach with various experiments.

2.7

Future trends

Over the past two decades, advances in welding automation can be traced in most instances to two major technological innovations: embedded computers and high-performance solid-state power electronics. With these innovations, the welding arc can be controlled with high precision, and robotic manipulators with joint sensing technology can maintain the heat source at exactly the desired position along the joint as the weld is made. Additionally, great strides have been made in sensing and controlling the weld geometry. Today, computers are embedded in almost all major welding equipment components. This includes the power supply, wire feeder, automatic voltage/ current controllers, tracking systems, penetration control systems, robotic manipulators, positioners, sensors, data acquisition systems, and gas monitoring systems. These embedded computers are designed to communicate with each other, as well as with other computers in the overall manufacturing facility. With this distributed computing capability, very advanced signal analysis techniques and control methods can be easily implemented and maintained. Advances in solid-state power electronics have shrunk the size of welding power supplies and motor controllers, and they have given us the capability to dynamically sense and respond in microseconds to changes in the electrical signals of the arc. This means we can, for example, precisely control the droplet transfer in pulsed GMAW over wide variations in the wire feed speed. It has also given us the ability to deliver short-circuiting transfer with virtually zero splatter. Indeed, with high-performance inverter power sources available today, we can literally treat the power supply as a ‘black box’ that can deliver on-demand any waveform or voltage–current

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characteristic we desire at speeds well above actual requirements at the arc. In the next 20 years, computers will continue to get smaller and faster, and substantial advances in the software technology of real-time systems and embedded computing for DSP and other purposes can be expected. These advances will find their way into welding equipment and control technology, resulting in continued improvements in realizations of truly intelligent welding systems. For example, the dynamic models discussed in this chapter, which continue to be developed, provide a basis to infer actual joint geometry from position and current information acquired during cross-seam oscillation. One approach assumes a basic joint shape defined by parameters such as width, depth, and sidewall angles. A reference model is run in real-time simulation and compared with the observed current, and the joint parameters adjusted to minimize the difference. Another approach inverts the model to produce CTWD as an output in response to current, and averages the resulting profiles over multiple cross-seam passes. Possible uses of such detailed joint profile information include in-process quality monitoring, adjusting torch cross-seam motion to ensure even joint fill and sidewall fusion, and adjustment of deposition rate to compensate for non-uniform fill of previous weld passes. With the use of wideband inverter-based power sources and DSP-based controls in production welding equipment, the platform will be available for using many of the techniques discussed here in real-time process control. It is suggested that the capability to perform model parameter identification and model validation exercises should be built into control software. The use of voltage perturbations to excite self-regulation allows frequency response measurements to be taken off-line with production welding equipment to validate model parameters prior to production use. With further work, it may be feasible to perform on-line model validation by continuous perturbation of welding voltage at an amplitude large enough to provide usable data, but not so large as to disturb weld quality. This same technique can provide for on-line measurement of incremental arc resistance. Continuous validation of the self-regulation model also suggests self-tuning of the model parameters to accommodate process changes. Methods that allow probing of the process based on excitation and observation and that are possible without extra instrumentation are highly attractive. Development of real-time control algorithms involving self-regulation dynamics is most easily accomplished in simulation, with occasional experimental work to validate intermediate results. Essential to robust design is the inclusion of accurate noise models to represent the random processes inherent in the physical system. There remains significant work to establish

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appropriate locations in the model structure for noise sources and to determine the statistical characteristics which produce acceptable results. Again the DSP-based data acquisition system can be of substantial use. In summary, the future goals of arc sensing in arc welding will be to build a system that can combine the arc signals with other sensor outputs to automatically detect and classify fault development and correct in-process.

2.8

Sources of further information and advice

Recommended reading on welding technology, welding automation, robotic welding, and arc sensing includes Sensors and Control Systems in Arc Welding (H. Nomura and J. E. Middle, Chapman & Hall, London, 1994), Robotic Welding (edited by J. Lane, IFS Publications Ltd, Bedford, UK, 1987), Automated Welding Systems in Manufacturing (Abington Publishing, Cambridge, UK, 1991), Developments in Automated and Robotic Welding (edited by D. N. Waller, The Welding Institute, Cambridge, UK, 1987), Robotic Welding, Intelligence and Automation (edited by T.-J. Tarn, S.-B. Chen and C. Zhou, Springer-Verlag, Berlin, Heidelberg, 2004), Industrial Robots (H. Colestock, McGraw-Hill, New York, 2005), Arc Welding Control (Pan Jiluan, Woodhead Publishing Limited, Cambridge, UK, 2003), Modeling, Sensing and Control of Gas Metal Arc Welding (S. Ozcelik, K. Moore, and D. S. Naidu, Elsevier Science, Oxford, 2003), Systems, Controls, Embedded Systems, Energy, and Machines (The Electrical Engineering Handbook); Third Edition (edited by R. C. Dorf, Taylor and Francis Group, Boca Raton, FL, 2006), Trends in Welding Research: Proceedings of the 5th International Conference (edited by J. M. Vitek, S. A. David, J. A. Johnson, H. B. Smartt, and T. DebRoy, ASM International, Metals Park, Ohio, 1999), and Trends in Welding Research: Proceedings of the 6th International Conference (edited by S. A. David, T. DebRoy, J. C. Lippold, H. B. Smartt, and J. M. Vitek, ASM International, Metals Park, Ohio, 2002).

2.9

List of symbols and abbreviations

A, B, C A0, A′0, A1, A2 Ar 1/a b/a as AVC CTWD

constants in arc voltage equation instantaneous arc conditions defined in Fig. 2.10 argon slope of f(H) curve where H is heat content of electrode material, J mm3 intercept of f(H) curve servomotor constant automatic voltage control contact-tube-to-work distance, mm

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42 c0, c1, c2 cˆ Ea e˙ FCAW GMAW G Gs Ho HA HL I, i Iarc j, J L, l la Larc Ls k1, k2 kc, kW K1–K4 Ks O Ra Rl, RL Rs SAW Tl t(x) s(x) t Varc vb vf VL Vo Voc W

Real-time weld process monitoring coefficients of quadratic part of resistivity versus action integral c estimate length dependence of arc voltage, V/mm time derivative of error flux cored arc welding gas metal arc welding gain transfer function of servomotor in automatic voltage control system heat content of discharged droplets, J mm3 heat input to electrode from anode heating, J mm3 heat content of electrode at tip due to joule heating, J mm3 current, A arc current, A current density, A/mm2 electrode extension length, mm arc length, mm electrode-to-workpiece distance, mm power source effective inductance, H burnoff rate coefficients proportionality constants in template matching constants in arc voltage equation servomotor constant oxygen current dependence of arc voltage, Ω total resistance of electrode extension, Ω power source effective resistance, submerged arc welding travel time of electrode from contact tip to electrode tip, s template signal for template matching approach to joint tracking as function of displacement x from center measured arc signal as function of displacement x from center discrete time sampling interval, s voltage drop across arc, V melting rate or burnoff rate expressed as a velocity, mm/s wire feedrate, mm/s voltage along the electrode extension, V constant component of arc voltage, V open-circuit voltage of welding power supply, V action, the time integral of current squared

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Arc sensors in weld monitoring WL WT yc ye ye(t) z εc εW φ ρ ζ ωn

2.10

43

action of electrode at distance L from the contact tube, typically at the electrode tip action defining transition from quadratic part to linear part of resistivity versus action model time-delayed signal signal difference between arc and model voltage, V discrete transform symbol error criterion for template matching centering control width control criterion for template matching effective anode potential, V resistivity, Ω mm damping ratio natural resonant frequency, rad/s

References

allum, c. j. (1983), ‘MIG welding: time for reassessment’, Metal Construction, 15 (6), 347–353. barborak, d. et al. (1999), ‘Through-arc process monitoring techniques for control of automated gas metal arc welding’, in Proceedings of International Conference on Robotics & Automation, Detroit, MI, IEEE, 3053–3058. bingul, z. et al. (2001), ‘Dynamic model for electrode melting rate in gas metal arc welding process’, Science and Technology of Welding and Joining, 6 (1), 41–50. bingul, z. et al. (2000), ‘Application of fuzzy logic to spatial thermal control in fusion welding’, IEEE Transactions on Industry Applications, 36 (6), 1523–1530. bjorgvinsson, j. b. (1992), ‘Adaptive Voltage Control in Gas Tungsten Arc Welding’, MS Thesis, Vanderbilt University, Nashville, TN. cook, g. e. (1983), ‘Modeling of Electric Welding Arcs for Adaptive Feedback Control’, in Conference Record, IEEE/IAS Annual Meeting, Mexico City, IEEE, 1234–1240. cook, g. e. et al. (1997), ‘Statistical process control application to weld process’, IEEE Transactions on Industry Applications, 33 (2), 454–463. craig, j. j. (1988), Adaptive Control of Mechanical Manipulators, Reading, MA, Addison-Wesley. dilthey, u. and gollnick, j. (1998), ‘Through-the arc sensing in GMA-welding with high speed rotating torch’, in Proceedings of the 24th Annual IECON Conference, New York, IEEE. fujimura, h. et al. (1994), ‘Arc sensing using fuzzy control’, in Nomura, H. and Middle, J. E., Sensors and Control Systems in Arc Welding, London, Chapman & Hall, 238–246. goldman, k. (1966), ‘Electric arcs in argon’, in Physics of the Welding Arc, London, Institute of Welding, 17–22. halmoy, e. (1979), ‘Wire melting rate, droplet temperature, and effective anode melting potential’, in Proceedings of International Conference on Arc Physics and Weld Pool Behavior, Cambridge, The Welding Institute, 49–57.

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halmoy, e. (1986), ‘Electrode wire heating in terms of welding parameters’, in Lancaster, J. F., The Physics of Welding, Oxford, Pergamon Press, 330–336. kim, c. h. and na, s. j. (2001), ‘A study of an arc sensor model for gas metal arc welding with rotating arc – part 1: dynamic simulation of wire melting’, Proceedings Instruments in Mechanical Engineering, 215, Part B, 1271–1279. kim, y.-s. and eagar, t. w. (1993), ‘Metal transfer in pulsed current gas metal arc welding’, Welding Journal, 72 (7), 279–287. koseeyaporn, p. (1999), ‘Adaptive Voltage Control in Gas Tungsten Arc Welding’, MS Thesis, Vanderbilt University, Nashville, TN. koseeyaporn, p. et al. (2000), ‘Adaptive voltage control in fusion arc welding’, IEEE Transactions on Industry Applications, 36 (5), 1300–1307. krepp, p. g. et al. (2005), ‘Dynamic response of electrode extension to step changes in average current for pulsed GMAW’ in Proceedings of 7th International Conference on Trends in Welding Research, ASM International, Materials Park, OH. lesnewich, a. (1958), ‘Control of melting rate and metal transfer in gas-shielded metal-arc welding, part 1, control of electrode melting rate’, Welding Journal, Research Supplement, 37 (8), 343s–353s. needham, j. c. (1966), ‘Control of transfer in aluminum consumable electrode welding’, in Physics of the Welding Arc, London, Institute of Welding. nomura, h. et al. (1994), ‘Development and application of arc sensor control with a high-speed rotating-arc process’, in Nomura, H. and Middle, J. E., Sensors and Control Systems in Arc Welding, London, Chapman & Hall, 247–256. ohshima, k. et al. (1994), ‘Application of a fuzzy neural network to welding line tracking’, in Nomura, H. and Middle, J. E., Sensors and Control Systems in Arc Welding, London, Chapman & Hall, 147–153. passino, k. m. and yurkovich, s. (1998), Fuzzy Control, Addison-Wesley Longman Inc., Menlo Park, CA. quinn, t. p. et al. (1994), ‘An electrode extension model for gas metal arc welding’, Welding Journal, 73 (6), 241–248. richardson, m. i. et al. (1994), ‘The influence of power source dynamics on wire melting rate in pulsed GMAW welding’, Welding Journal, 73 (2), 32s–37s. schellhase, m. and weinschenk, h. e. (1979), ‘Dynamic behavior of consumable metal arcs’, in Proceedings of International Conference on Arc Physics and Weld Pool Behavior, Cambridge, The Welding Institute, 59–66. shepard, m. e. (1991), ‘Modeling of Self-Regulation in Gas-Metal Arc Welding’, PhD Dissertation, Vanderbilt University, Nashville, TN. shi, y.-h. et al. (2005), ‘Signal patterns of high speed rotational arc sensor for gas metal arc welding’, in Proceedings of Sensors for Industry Conference, Houston, TX, 9–14. smithmaitrie, p. et al. (2000), ‘Adaptive fuzzy voltage control in GTAW’, in Proceedings of the 7th Mechatronics Forum International Conference, Amsterdam, Elsevier Science Ltd. sugitani, y. et al. (1994), ‘An intelligent arc-welding robot with simultaneous control of penetration depth and bead height’, in Nomura, H. and Middle, J. E., Sensors and Control Systems in Arc Welding, London, Chapman & Hall, 390–399. wells, a. m. (1988), ‘Through-the-arc Sensing and Control Methods in Robotic Arc Welding’, PhD Dissertation, Vanderbilt University, Nashville, TN. yoo, w. s. (2004), ‘A Study on Sensors for Automatic Welding of 3-D Seam in Ship Hull Assembly’, KAIST PhD Thesis, 93–97.

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3 Optical sensors in welding G. M. SAEED, Schlumberger, USA and H. SONG, University of Kentucky, USA

The most substantially successful technology that has emerged is the use of electro-optics, which has been expanded into the realm of ‘machine vision’ in industry with the rapid advance of optical sensor technology and computer processing power. In the welding community, various optical sensors and sensing systems, especially charge-coupled devices (CCD), have been applied widely to realize real-time monitor of welding processes. In this chapter, a brief overview is given to introduce the optical sensor technologies and the techniques used to sense the weld pool state in different applications.

3.1

Introduction

As an important measurement and monitoring technology, optical sensors have been widely applied in many areas, such as fiber-optic communication and the biomedical, chemical and manufacturing industries. For instance, in manufacturing processes optical sensors can be used for contactless detection, counting or positioning of parts since they offer the advantage of a larger detection range. The basic operating principle is the transmitting and receiving of light, which is blocked or reflected by the detected objects. There are many kinds of optical sensors according to differing principles and applications, such as photodiodes, phototransistors and photoresistors based sensors, photomultipliers, infrared sensors and CCD sensors. In the welding process, various types of optical sensors have been incorporated in the sensing of weld pools based on the parameter being measured in different applications, such as arc length detection, weld pool oscillation analysis, and weld pool measurement. The most commonly used industrial vision sensor is the monochrome camera, which acquires visible light intensities from black to white in various shades of gray. Monochrome cameras are used in industrial applications that do not need color information to perform the required task. Examples include determining the edge of the workpiece for seam tracking purposes and making measurements of weld pool size 45 WPNL2204

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Real-time weld process monitoring Sensed signal Dynamic control model

Optical sensor

Control signal Inputs (welding parameters)

S

Welding process (nominal welding conditions)

Outputs (properties of weld)

Disturbances

3.1 Optical sensor in welding control system.

using neutral density filters to block arc light. If color information is critical for proper monitoring, a color-imaging sensor such as a red–green–blue (RGB) three-channel camera can be used. Both monochrome and RGB cameras are commercially available in various configurations, sizes, and resolutions to meet the demands of most industrial applications. Usually in these approaches, one or more characteristic parameters of the welding process measured by optical sensors are sent to an established control model to realize process control for various welding applications. The light source sensed by the optical sensor can be either the strong arc light radiation in the welding process or the external illumination light for imaging purposes, such as the laser light. Figure 3.1 shows a typical welding control system using an optical sensor. As can be seen, the optical sensor in fact is functioning as a bridge to convey the output of welding process to the control model as an input signal. For example, vision-based sensing is used to determine the condition of the weld pool. Generally a human welder uses vision to look at the weld pool, makes judgments about the welding process and adjusts the parameters accordingly. Similarly vision sensors can be used to sense the weld pool state and in conjunction with a control algorithm adjusts the welding parameters (current or speed). Moreover, an optical sensor is a necessary part of the machine vision system, which plays an important role in developing the next-generation intelligent welding machine.

3.2

Knowledge of optics

Optical sensors work by sensing light. Thus the characteristics and principles of the light should be illustrated first. Here some related topics of optics are discussed, such as optical spectrum, light reflection, light refraction, and principle of thin lens.

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3.2.1 Optical spectrum Electromagnetic radiation exists everywhere in our lives, from daily sunshine to medical diagnostics using X-rays. Electromagnetic waves can either be conceptually deemed as propagating sinusoidal waves of varying wavelengths or be thought of as a stream of massless particles, traveling in a wavelike pattern and moving at the speed of light. This is known as the principle of wave–particle duality. Light is also an electromagnetic wave (radiation), like a radiowave, but with a very short wavelength, which means a very high frequency. The relationship between wavelength and frequency for an electromagnetic wave can be expressed as: λν = c

[3.1]

where λ represents wavelength in meters, ν is the frequency in Hz, and c is the light speed in free space (vacuum), which is a constant 299 792 458 m/s or generally 3 × 108 m/s. Electromagnetic energy at a particular wavelength λ (in vacuum) has an associated frequency ν and photon energy E: E = hν

[3.2]

where h is Planck’s constant (h ≈ 6.626 069 × 10−34 J·s). So high-frequency electromagnetic waves have a short wavelength and high energy; lowfrequency waves have a long wavelength and low energy. The electromagnetic (EM) spectrum is defined as the frequency range of all possible electromagnetic radiations in nature with wavelengths from thousands of kilometers down to small fractions of the size of an atom. Figure 3.2 shows the structure of the electromagnetic spectrum. Here the wavelengths of EM radiation are usually quoted in terms of the vacuum wavelength.

1019 1018 1017 1016 1015 1014 1013

Frequency (HZ) Gamma rays

X-rays

Ultraviolet

Infrared

Microwave

Near-IR Thermal Far-IR IR

Wavelength 0.1 A

1012 1011 1010

Radar

109

108

107

106

TV radio

105

Long waves AM

1 A 1 nm 10 nm 100 nm 1 um 10 um 100 um 1 mm 1 cm 10 cm 1 m 10 m 100 m 1000 m

Visible light 400 nm 500 nm

600 nm

700 nm

3.2 The electromagnetic spectrum.

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Intensity, arb. unit (calibrated)

As can be seen, the EM spectrum can be divided into several bands (parts) according to the wavelength (or frequency). In the ascendant order of wavelength range, they are gamma-rays, X-rays, ultraviolet (UV), visible light, infrared (IR), microwaves, radio frequency and long waves. In each spectral band, the wave resource can be utilized in different applications, for example radio frequency band for television, radio signal transmission and X-ray band for medical use. Accordingly different sensors are used to detect the EM wave in different bands. Here the optical sensors mentioned mainly work in the band of visible light (roughly from 400 to 760 nm), but are not limited to this range. The infrared sensor, which is discussed in Chapter 4, can also be classified as an optical sensor. Arc light radiation is a fundamental phenomenon associated with the arc welding process. In the welding process, not only intense UV radiation but also intense visible and IR radiation are emitted from the plasma arc. Thus eye protection is mandatory for this harsh environment. Arc light sensing has been studied for arc length control, joint tracking and droplet transfer detection in the arc welding process. In particular, optical sensors are used to study the spectrum of arc light to find the relationship of the change in arc light to the welding process. One of the arc light radiation experiments was conducted to perform spectral analysis for gas tungsten arc welding (GTAW) process using a grating monochrometer [1], and the system spectral response range was from 320 to 820 nm. Figure 3.3 shows the achieved spectral distribution of gas tungsten arc light. As can be seen, arc light emission consists of continuum and spectral lines. In addition, a light radiation model was derived based on the result [1]. Another experimental study was carried out to obtain spectral maps of 24 combinations of weld parameters for the gas tungsten arc light emissions

2000

Mild steel, 150 A Arc length: 5 mm

1500

1000

500

0

300

400

500

600

700

800

900

Wavelength (nm)

3.3 One example of spectral distribution for gas tungsten arc light [1].

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in the wavelength range from 488 to 730 nm using a calibrated intensity scale [2]. The emissions were found to be dramatically different with different shielding gases, welding current and base metals. Spectral windows, where the external sensors will have the least disturbance from welding arc emission, were also determined. These results provide a valuable reference for designing a welding process imaging system to achieve clear images, which will also be addressed in Section 3.4.

3.2.2 Reflection and refraction Nearly all objects in the universe emit, reflect or transmit some light, whose distribution in the EM spectrum is determined by the object’s composition. For the transmission of light in media, reflection and refraction are two important concepts (phenomena) in geometrical optics. They also contribute to the design of optical sensor (system) for welding process observation. Reflection and its equation In physics, reflection is the change in direction of a wave front at an interface between two dissimilar media so that the wave front returns into the medium from which it originated. Common examples include the reflection of light, sound and water waves. Reflection of light may be specular (mirrorlike) or diffuse (not retaining the image, only the energy) depending on the nature of the interface. Specular reflection is the perfect, mirror-like reflection of light from a surface, in which the light from a single incoming direction is reflected into a single outgoing direction. Such behavior is described by the reflection law, which states that the direction of reflected light and the direction of incident light make the same angle with respect to the surface normal. The reflection law is commonly stated as: θi = θr

[3.3]

where θi refers to the incident angle and θr to the reflection angle, which can be seen in Fig. 3.4(a). In the figure, PO is the incident ray and OQ is the reflected ray. During the welding process, the molten weld pool surface is just like a mirror and the specular reflection may happen when external rays, such as laser light, are projected onto it. When light strikes a rough or granular surface, it bounces off in many directions due to the microscopic irregularities of the interface. This phenomenon is called diffuse reflection, which is shown in Fig. 3.4(b). However, for each incident ray, the reflection law is still obeyed in diffuse reflection.

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Real-time weld process monitoring Parallel incident rays Normal

P

Q Reflected rays scatter in all directions

Incident ray

qi

qg

Reflected ray Mirror

O

(b)

(a)

3.4 Different types of reflection: (a) specular reflection and (b) diffuse reflection.

Even when a surface exhibits only specular reflection, not all the light is necessarily reflected. Some of the light may be absorbed by the materials or transmitted through the surface as refraction. If the light is propagating in a material with a higher refractive index than the other material whose surface it strikes, total internal reflection may occur, called total reflection. Refraction and its equation Refraction is defined as the directional change of a wave (light ray) due to a change in its speed, which is commonly seen when a wave passes from one medium to another at an angle (not perpendicular) to their interface. In optics, refraction of light occurs at the interface between two media of different refractive indices. At the interface, the wave’s phase velocity and its direction are changed. Meanwhile, its wavelength also changed but its frequency remains constant. Their relationship can still be described by Eq. 3.1. Figure 3.5 shows an example in which light refraction happened between media A and B with different refractive indices (n2 > n1). Since the velocity is lower in the second medium B (ν2 < ν1), the refractive angle θ2 is less than the incident angle θ1. Their relationship can be expressed by Snell’s law shown in Eq. 3.4: n1 sin θ1 = n2 sin θ 2

or

sin θ1 n2 ν1 = = sin θ 2 n1 ν2

[3.4]

Snell’s law, also known as the law of refraction, describes the relationship between the incident angle and the refractive angle. It can be seen that the

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n2 index ν2 velocity

n1 ν1

Normal O

q2 Q Medium B

Medium A Interface

3.5 Illustration of refraction.

ratio of the sines of the incident and refractive angles is a constant that depends on the media. In geometrical optics, the law can be used in ray tracing to compute the incident angle or refractive angle, and to find the refractive index of a material. The law of refraction is also a basis for the study of lenses’ optical characteristics.

3.2.3 Thin lens equation In optics, a thin lens refers to a lens with a thickness that is negligible compared with the focal length of the lens. The thickness is defined as the distance along the optical axis between the two surfaces of the lens. If the thickness is not negligible, the lens is called a thick lens. The thin lens approximation ignores optical effects due to the thickness of lenses and simplifies ray tracing calculations. Thus it is studied and used widely in designing optical systems. For a thin lens, in the paraxial ray approximation, the object (u) and image (v) distances can be related by Eq. 3.5: 1 1 1 + = u v f

[3.5]

where f is the focal length of the lens and u (or n) is the distances between the object (or image) and the center point O of the thin lens. Figure 3.6 shows a case using a thin positive lens. According to knowledge of thin lenses, incoming rays parallel to the optical axis pass through the focal point on the other side after the refraction of thin lens, and rays passing through the center point of thin lens remain in the propagation directions. Since the thickness of the thin lens is negligible, Eq. 3.5 can thus be deduced by using several similar triangles in Fig. 3.6. In geometrical optics, ray tracing is a general technique based on this principle to model the light paths as they interact with optical surfaces. It is also used in the design of optical systems such as camera lenses, microscopes, and telescopes.

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Real-time weld process monitoring v u

f Focal point

Object

o

Image

Optical axis

Thin positive lens

3.6 Illustration of thin lens principle.

3.3

Optical sensor component

Based on the characteristics and spectrum of light, different optical sensors have been developed for applications in different situations. In the welding processes, the applied optical sensor components in the visible light band include electro-optic sensor (photodetectors), CCD/CMOS (complementary metal oxide semiconductor) sensor, and high-speed camera and so on. In this section, their basic principles are discussed.

3.3.1 Electro-optic sensor Electro-optic sensors, also called photosensors or photodetectors, are sensors of light or other electromagnetic energy. They can be further divided into several types: photoresistors, photodiodes, photomultiplier, phototransistors, etc. Most optical detectors are quantum devices in which an individual photon produces a discrete effect. The photoelectric effect is the basis for the electro-optic sensors. Two related processes are used for the detection of photons [3]. Both are based on the transfer of the photon energy to an electron in a collision. When a photon impinges on the surface of any material, it can liberate an electron if the energy of the photon is higher than the photoelectric work function φ. This phenomenon was first formulated by Albert Einstein as: Wk = hν − φ

[3.6]

where Wk is the kinetic energy of the electron, h is Planck’s constant, ν is the frequency of the incident photon, and φ = hν0 is the work function, the minimum energy required to remove a electron from the surface with the threshold photon frequency ν0. Usually semiconductors have a very small work function φ and consequently the threshold frequency of the incoming photon can be in the near-infrared band. During the process, a

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Photon

53

Metal contact (−)

P-type region Intrinsic region

Hole Electron

Electron–hole pair N-type region

PIN diode symbol

Metal contact (+)

3.7 Basic structure of PIN photodiode and its symbol in a circuit [3].

single photon can eject only a single electron since its energy may be absorbed by only one electron. The electrons that are emitted are often called photoelectrons. The other process needs less energy, but it should be sufficient to lift an electron in a semiconductor from the valence band to the conducting band so as to create an electron–hole pair. For example, a silicon crystal whose band gap is 1.1 eV (electronvolt) can be a very efficient photon detector in the whole range of visible light. When the electron cannot recombine with the hole in the conductive band due to the impact of electric field, it can be collected and the signal amplified electrically. Thus the incident photon can be detected. Silicon photodiodes devices just exploit this property to sense light. The silicon PIN diode is a very successful device, which has a wide undoped intrinsic semiconductor region between p-type and n-type semiconductor regions. It has a rather simple structure as shown in Fig. 3.7, and its operation is simple and reliable. The great benefit of a PIN diode lies in the large depletion region regardless of the applied reverse bias, which increases the area for generating electron–hole pairs. Thus many photodetector devices include at least one PIN diode in their construction, for example PIN photodiodes and phototransistors (in which the base– collector junction is a PIN diode). However, since PIN diode has no gain it needs a charge-sensitive amplifier that adds to the cost and creates noise in the readout system.

3.3.2 CCD sensor A charge-coupled device (CCD) is a kind of pixelated metal oxide semiconductor (photodiodes) made from silicon. It is the key component in digital cameras, which are widely applied in scientific, astronomical, biomedical, and other commercial imaging areas. Its sensitivity range covers

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both the visible and near-IR spectrum. In CCD, the metal oxide semiconductor (MOS) capacitor is the basic unit (pixel). The light that falls on it is converted into electrons and then changed into a voltage signal. In fact, ‘charge-coupled device’ is a description of the technology used to move and store the electron charge. Under the control of an external circuit, each capacitor can transfer its electric charge to one or other of its neighbors. A CCD sensor can sense the intensity level (amount) of light but not its color. Colored images can only be created either by coating each pixel with a colored filter (red, green or blue) and then interpolating the missing information or making three exposures by using a colored filter (again red, green and blue) each time. Here the structures and principles of charge-coupled devices are introduced. Structure of the CCD sensor There are three basic techniques to realize image sensing: point scanning, line scanning and area scanning. A CCD image sensor can take the forms of line and area scanning. Here CCD area image sensors are introduced since they are mainly used in welding process monitoring. There are various architectures of CCD area image sensors. According to their transfer methods, they can be classified into four types [4,5]: fullframe transfer (FFT), frame transfer (FT), interline transfer (IT), and frame interline transfer (FIT). The primary CCDs in use today are FFT and FT devices, which use MOS photocapacitors as detectors and IT devices which use photodiodes and photocapacitors as detectors. Each of them is described below, although other image sensing structures, such as FIT, are not discussed here. Full-frame transfer (FFT) CCD FFT-CCD has the simplest architecture and is the easiest to fabricate and operate. It consists of a parallel CCD shift register (CCD pixel array), a serial CCD shift register and a signal sensing output amplifier, which is shown in Fig. 3.8. In FFT-CCD, the signal charge is collected in a potential well in the photosensitive section of pixels during the signal integration period. Then the resulting rows in pixel array are shifted in parallel to the serial register which subsequently shifts the row of information to the output as a serial stream of data with the corresponding clocks. The process repeats until all rows are transferred off the CCD (off chip) to the output during the closed period of an external shutter. The image is then reconstructed in external circuit as dictated by the system. Because the parallel register is used for both scene detection and charge shifting (readout), a

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One pixel (photon-to-electron conversion)

External parallel clocks

Parallel shift register (pixel array) Analog voltage

External serial clocks

Output

Output amplifier Serial shift register

Electron-to-voltage conversion

3.8 Structure of full frame transfer CCD.

timing mechanism (mechanical shutter or synchronized strobe illumination) must be used to preserve scene integrity. The simplicity of the FFT design yields CCD imagers with the highest resolution and highest density. However, FFT-CCD is not very suitable for use in video cameras because of its operating principle, so it is primarily used in quantitative measurement applications where a slow frame rate is desired. Frame transfer (FT) CCD Frame transfer CCD, shown in Fig. 3.9, comprises two parallel shift registers in the photosensitive and storage sections, one serial shift register, and an output section. As can be seen, FT-CCD is very like FFT in structure. The difference is a separate and identical parallel register, called a storage array, which is not light sensitive. The idea is to shift a captured scene quickly from the pixel array (photosensitive) to the storage array, and readout off chip from the storage register is then performed as the FFTCCD while the storage array is integrating the next frame at the same time. The advantage of this architecture is that a continuous or shutterless operation is achieved, resulting in faster frame rates. But its performance is compromised because integration is still occurring during the image dump to the storage array, which will ‘smear’ the image. Because twice the silicon area is required to implement this architecture, FT-CCDs have lower resolutions and much higher costs than FFT-CCDs.

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Pixel (image) array

Pixel array parallel clocks

Parallel shift register

Storage array parallel clocks

Storage array Serial clocks

Analog voltage Output Serial shift register

Output amplifier

3.9 Structure of frame transfer CCD.

Interline transfer (IT) CCD IT-CCD is incorporated to overcome the shortcomings of the FT devices. This is achieved by separating the photodetecting and readout functions by forming isolated photosensitive regions in between lines of a light-shielded (non-sensitive) parallel readout shift register, as shown in Fig. 3.10. After integrating a scene, the signal collected in every pixel is immediately transferred into the light-shielded parallel shift register, and then transferred to the output section, as in FFT and FT-CCDs. During readout, like the FTCCD, the next frame is being integrated at the same time of readout, thus achieving a continuous operation and a higher frame rate. Because of this architecture, the image smear phenomenon during readout using FT-CCD is significantly improved through using IT-CCD. The major disadvantage of the IT-CCD structure is its complexity, which leads to higher unit costs and lower sensitivity. Lower sensitivity occurs because a smaller photosensitive area is present at each pixel site due to the associated light shielded readout shift register. Moreover, in IT-CCD quantization or sampling errors are greater because of the reduced photosensitive area. Such a situation can be improved by placing a little lens on top of each pixel to gather more light into the photosensitive area [4].

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57

Photodiode One pixel

External parallel clocks

Readout shift register

Parallel shift register

Analog voltage

External serial clocks

Output Serial shift register

Output amplifier

3.10 Structure of interline transfer CCD.

Principle of the CCD sensor Different structures of CCD sensors have different transfer methods to convert the light signal into an output analog voltage for each pixel in the image array. However, the principles behind them are the same. Usually CCD imaging is performed in a three-step process: 1 2 3

Exposure, which converts light (photon) into an electronic charge at discrete sites (pixels). Charge transfer, which moves the signal charges in the silicon substrate. Charge to voltage conversion and output amplification.

Here the three procedures are discussed in detail. Light (photons) to signal charge conversion An image is acquired when incident light, in the form of photons, falls on the pixel array. The energy associated with each photon is absorbed by an electron in the silicon such that an electron–hole charge pair is created. The number of electrons collected at each pixel is linearly dependent on intensity level of incident light and exposure time and it is non-linearly dependent on wavelength of light. Figure 3.11 shows a three-phase CCD pixel structure and the formed potential well/barriers. CCDs follow the principles of a basic MOS device. A CCD MOS structure consists of a vertically stacked conductive doped

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Real-time weld process monitoring Incident light P3

P2

P1

+V

−V

−V

Polysilicon

Silicon dioxide N-type Potential barrier Silicon substrate

Potential barrier Potential well

P-type

Photogenerated electrons

3.11 CCD pixel structure and potential well/barriers.

polysilicon and silicon semiconductor (p-type and n-type), and they are separated by a highly insulating silicon dioxide. When a voltage is applied to the polysilicon (gate), the electrostatic potentials within the silicon can be changed. With an appropriate voltage, a potential well can be formed which has the capability of collecting the localized electrons that were created by the incident light. The electrons can be confined under this gate by the higher potential barriers, which are surrounding the well. Depending on the voltage, each gate can be biased to form a potential well or a barrier to the integrated charge. Charge transfer technique Once charge has been integrated and held locally by the bounds of the pixel structure, it should be transferred to the output amplifier. The common methods used today involve four differing charge transfer techniques: four phase transfer, three phase transfer, two phase transfer, and virtual phase transfer. Here only two phase charge transfer process is discussed as a representative. Figure 3.12 shows the operating principle of a two-phase CCD in which the signal charge is transferred by applying two clock signals with different levels. In two-phase CCD, the signal charge is transferred in the direction determined by the potential difference created during wafer process. In Fig. 3.12(a), a pair of gate electrodes P1 and P2 are used to define a single pixel, and the shift registers are formed by the electrodes, which form a long chain of gates along one axis. If a high-level voltage is applied to one of these gates, a potential well is formed beneath that gate while a low-level voltage forms a potential barrier. As the timing diagram shows in Fig. 3.12(c) during

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N

Pixel n+1 P1 P2

-

Pixel n+2 P1 P2

59

Pixel n+3 P1 P2

N P

(a) Structure of two-phase CCD Transfer direction Qn+1

Qn

t1

Signal charge Qn

t2 Qn−1 t3

Qn−1

Qn+3

Qn+2 Qn+1 Qn+1

Qn

Qn+3

Qn+2

Qn+2

(b) Potential diagram

P1 P2

t1 (c) Timing diagram

t2

t3

3.12 Charge transfer in two-phase CCD [4].

integration if the voltage on P1 gate is high while the voltage is low on the P2 gate at time t1, potential wells can form to integrate and collect photoinduced charge for pixels, such as Qn at pixel n. If P1 and P2 change their polarity (for example, P1 goes from high to low and P2 goes from low to high at time t2) the signal charge is forced by electrostatics to move beneath P2, which is shown in Fig. 3.12(b). Then at time t3, P1 and P2 reverse their polarity again and the charge is moved further to shift to a neighbor pixel position. For example, at time t3 Qn is occupying the well formed by pixel n + 1, and one transfer cycle is thus completed. Hence, the cycle is repeated until all signal charges have reached the output. One thing should be mentioned is that when the charge associated with one pixel is moved, all the pixels at that row or column also move. Charge readout technique As can be seen, the signal charges should be shifted to the output section in a CCD to convert electrons to a voltage since it is easier to be processed by external circuit. The most popular readout technique is called the floating diffusion amplifier (FDA) method, which usually employs a floating

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Real-time weld process monitoring Output amplifier Output OG

P1

Pr

RD

Floating diffusion

(a) Readout structure t1

Q1

t2

Q1

P1

t3 t4

t1

t2

t4 t3

Pr Output Signal from Q1

Q1 Q2

(c) Clock voltage timing diagram

(b) Potential diagram

3.13 Floating diffusion readout structure [4].

diffusion sense node followed by a charge to voltage output amplifier. The amplifier is used to preserve the linear relationship between light in electrons generated and voltage output. The floating diffusion readout structure is shown in Fig. 3.13. First the floating diffusion is reset by a reset gate and reset drain (Pr and RD in Fig. 3.13a) which dictates the reset potential by applying a voltage. This reset signal level is converted to an output voltage which is processed as a reference level. Then the charge is shifted from the last phase within the CCD to the floating diffusion from t1 to t3. Finally, it is converted into an output voltage. The difference between the reference (reset) level and the potential shift of the floating diffusion level determines the signal, just as shown in Fig. 3.13(c).

3.3.3 CMOS sensor CMOS is also a kind of pixelated metal oxide semiconductor made from silicon. It has basically the same sensitivity in the visible and near-IR spectrum as CCD. It converts the light that falls on it into electrons by the same process as CCD and can be considered basically similar in operation. In fact, ‘complementary metal oxide semiconductor’ is the name of the technology used to make a transistor on a silicon wafer. The fundamental difference between the CMOS and CCD is the architecture of the image sensor within the chip and camera, which is shown in

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Fig. 3.14. Within a CCD sensor all image processing is done off-chip on the external circuit board. Only the ‘photon-to-electron’ conversion is done within the pixel and the ‘electron-to-voltage’ conversion is done on the chip. The output data leaving the CCD is still in analog form and is digitized in the external supporting camera circuitry. Within the CMOS sensor both the ‘photon-to-electron’ conversion and the ‘electron-to-voltage’ conversion is done within the pixel, which leaves less room for the lightreceptive part of the sensor. This means the CMOS chip has less area to actually receive light. Normally some form of micro-lens is needed in

Charge-coupled device

Camera circuit board Clock drivers

Oscillator

Timing generation

Bias generation

Analog voltage

Gain

Analog-to-digital conversion

Digital signal Image out

Photon-to-electron Electron-to-voltage conversion conversion (a) Complementary metal oxide semiconductor device

Column amplification

Row driver/access

Photon-to-electron conversion

Connector

Clock drivers

Oscillator

Timing generation

Bias generation

Gain

Camera circuit board

Bias decoupling

Analog-to-digital conversion

Digital signal

Image out

Electron-to-voltage conversion

(b)

3.14 Comparison of CCD and CMOS architectures [6]: (a) chargecoupled device (CCD) and (b) complementary metal oxide semiconductor (CMOS).

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each pixel to help capture the light that would be missed because of the limited receptive area.

3.3.4 High-speed camera A camera is a device used to capture images. It generally consists of some kind of enclosed hollow, with an opening or aperture at one end for light to enter, and a recording or viewing surface for capturing light at the other end. The diameter of the aperture is controlled by a diaphragm mechanism. Most cameras have a lens positioned in front of the camera’s opening to gather the incoming light and to focus the image on the recording surface. Monochrome and color cameras acquire images based on light intensities in the human visible wavelength spectrum of 400–700 nm. Over the past decade several imaging sensors have also been developed that are sensitive to light beyond the visible spectral wavelength range. These ‘spectral’ cameras can acquire images in light wavelengths on either side of the human visible spectrum. UV cameras are sensitive to light in the wavelength range of 100–400 nm. At the other end of the visible spectrum several near-IR and thermal IR imaging cameras have also been realized, which are sensitive to light in the 900–2500 nm and 2500–14 000 nm wavelength ranges, respectively. Thermal IR cameras find use in several heat sensing applications for determining temperature of the weld pool surface. This is further elaborated in Chapter 4. A high-speed camera is a device used for recording slow-motion playback films, or used for scientific study of transient phenomena. Usually a normal motion picture is filmed at 24 frames per second, while television uses 25 frames per second (PAL) or 29.97 frames/s (NTSC). High-speed cameras can film up to 250 000 frames/s by running the film over a rotating prism or mirror instead of using a shutter. They are generally used in scientific research, military evaluation, and industry. In the welding process, the highspeed camera can be used to help monitor and study quickly changed phenomena, such as weld pool variation and metal droplet transfer. For example, an Olympus i-SPEED camera is a high-speed monochrome camera. The speed of camera ranges from 60 to 33 000 frames per second. Thus the minimal changes in studied applications within a very short period can still be captured by it.

3.4

Optical sensing system

In order to effectively monitor and sense welding processes, some optical sensing systems have been developed and applied. Special algorithms and models are also applied with the sensing systems, some of which are

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discussed herein. Some typical application examples include the laser strobe system, structure light system, and stereo vision system. In this section, their principles and applications are described.

3.4.1 Stroboscopic video camera system As a specially designed optical sensing method, the stroboscopic video camera system can be used to sense high-temperature/high-luminosity industrial processes that are normally quite difficult to monitor with human eyes, such as the welding process. The system captures a video image using intense lighting from a strobe illumination unit instead of utilizing the light from the process itself. Typically the stroboscopic video camera system consists of laser unit, camera unit, and system controller. Figure 3.15 shows a diagram of the LaserStrobeTM stroboscopic video camera system made by Control Vision Inc. The laser unit can produce a high-power pulsed illumination laser at a certain wavelength, which is projected onto the object to be sensed. The term laser is an acronym for light amplification by stimulated emission of radiation. In physics, a laser is a device that emits light through a specific mechanism. A laser consists of a gain medium (gas, liquid, or solid) inside an optical cavity. In its simplest form, a cavity consists of two mirrors placed on two end sides such that light bounces back and forth. Light of a specific wavelength that passes through the gain medium is amplified, and part of the light goes through the partially transparent mirror and appears as a beam of light. During the process, the energy needed for the amplification is typically supplied as an electrical current or as light at a different wavelength. For LaserStrobeTM, the gas discharge laser produces an optical pulse of about 3 ns duration in the near-ultraviolet portion of the optical spectrum (337 nm optical wavelength).

Lase

r unit

Fibe

r opt

it un era m Ca

ic ca

ble

System controller

3.15 LaserStrobeTM system diagram.

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The camera unit is usually composed of a focus lens, optical filter, and CCD camera equipped with an image intensifier tube in front of it. The optical filter works at a certain wavelength range and it can filter the illumination laser light out. The intensifier tube provides electronic shuttering capability which is synchronized with the period of the pulsed laser and it also amplifies the intensity of the image. Thus a clear image can be recorded by the CCD camera. The system controller unit sends appropriate trigger commands to the laser unit and camera unit to synchronize the pulse and shutter. Also it works as a power supply for the camera unit and provides the functions to adjust the sensitivity, shutter speed and frame rate of the camera. The LaserStrobeTM stroboscopic video camera system was applied to measure the two-dimensional (2D) weld pool boundary and observe the three-dimensional (3D) pool surface in some early experiments, which were done by Zhang et al. in the Welding Research Laboratory at the University of Kentucky [7,8]. Figure 3.16(a) shows the designed measurement system diagram, where the camera observes the weld pool at an angle to the direction of the weld and the laser is projected in the opposite direction. In the LaserStrobeTM system, the high-speed shutter of the camera is synchronized with a short duration pulsed laser [7], which allows the strong arc light to be eliminated from the captured image. As can be seen from Fig. 3.16(b), the 2D boundary of the weld pool is clearly imaged. Furthermore the complete weld pool edge can be extracted by means of the gray difference and directional gradient of grayness between the weld pool and the HAZ zone and the base metal. To use this camera in 3D weld pool surface imaging, a special technique was used that projected the structured illumination laser through a frosted glass to produce laser strips. As a result, a clear image with 3D information (deformed laser stripes) of weld pool surface can be acquired in Fig. 3.16(c) [8]. However, the stroboscopic video camera system used in the above approaches is extremely expensive and the frame rate of the camera can reach only 30 frames/s because it uses a pulsed illumination laser to suppress the arc light while the average power of the laser is relatively low.

3.4.2 Structured light system A structured light technique is the projection of a one-dimensional or twodimensional laser light pattern (line, grid, or more complex shape) at a known angle onto an object, and the deformation of the pattern on the subject is imaged by the camera fitted with a band-pass filter centered at the wavelength of the structured laser light. StockerYale’s LasirisTM SNF uniform intensity laser projector [9] is used for such applications and it can be attached with different diffractive lenses. By using the lens, different

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486 PC

Interface

286 PC

Frame grabber

3-pole positioning system

Power supply Laser Optical Current fiber Video signal Torch Trajectory Camera Travel direction

X Y Monitor

X Y

Weld pool

(a)

(b)

(c)

3.16 2D and 3D weld pool measurement: (a) 2D experimental setup; (b) 2D shape [7]; and (c) 3D shape [8].

projection patterns can be produced, such as parallel lines, dot matrix, crosshair, and single circle. The structured light can also be used to scan the objects so as to quickly reconstruct the 3D shape of the object. This technique is very useful for imaging and acquiring dimensional information in machine vision applications. In the welding process, a structured light system can also be used to observe the dynamic weld pool. The structured light used is a laser light with a specific wavelength. Since the laser used is continuous and low power (usually less than 100 mW), the selection of laser wavelength and filter

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should be determined by the radiation spectrum of arc light so as to avoid strong arc in the welding process and to capture a clear image. The structured light system was first applied to acquire the depth (profile) of the weld pool surface by Saeed et al. [10], which is shown in Fig. 3.17. In their system, a laser line projected on to the center of weld pool surface is observed directly by a compact CCD sensor fitted with a band-pass filter, which shows the shape of a weld pool profile. Figure 3.17(b) shows some of the data captured by the compact sensor. An interesting finding was that the shape of the weld pool is like a convex mirror (blob) in the first image when there is no metal penetration. When the welding current is increased and the weld penetrates the metal, the weld pool is like a concave mirror and deepens as the penetration increases which can be seen in the following three images. It is easy to see that the interference of a strong weld arc degrades the quality of the acquired images.

Image z Reflected laser

Laser Torch CCD sensor Electrode o

Weld pool

y

x (a)

(b)

3.17 Structured light technique used in weld pool profile extraction [10]: (a) profile extraction experiment and (b) captured images.

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Another structured light technique-based system was developed by Song and Zhang [11] to measure the 3D GTAW pool surface. In the designed system shown in Fig. 3.18, a structured laser pattern (5-line or 19-by-19 dotmatrix) is projected on to the weld pool surface and its reflection from the specular weld pool surface is imaged on an imaging plane and recorded by a filter-fitted high-speed camera. The deformation of the molten weld pool surface distorts the reflected pattern, which is shown in Fig. 3.18(b). To derive the deformed surface of the weld pool, an image processing algorithm is first developed to detect the reflection points in the reflected laser pattern (19-by-19 dot matrix). The reflection points are then matched with their respective incident rays according to the findings of correspondence simulations. As a result, a set of matched incident ray and reflection point is obtained and an iterative surface reconstruction scheme is proposed to derive the 3D pool surface from this set of data based on the reflection law. The reconstructed results proved the validity of the system.

Z Reflected image Laser Torch

Reflected laser dots Camera

Weld pool Imaging plane

O Y Laser dot matrices Work piece

X (a)

(b)

3.18 Structured light technique used in 3D weld pool measurement [11]: (a) 3D measurement system diagram; (b) reflected image (5-line and dot-matrix pattern).

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3.4.3 Stereovision system A stereovision system is a research branch of the computer vision field. In this system, two cameras take pictures of the same scene, but they are separated by a distance, just like our eyes. A computer compares the images while shifting the two images together over top of each other to find the parts that match. The shifted amount is called the disparity, and is used by the computer to calculate the depth. The stereovision method is also used to study the 3D shape of the weld pool surface. In an experiment two paired cameras have been used to capture weld pool images [12]. The process used for welding was pulsed GMAW, and the paired images of the weld pool were captured during the ‘off’ period of the arc. Therefore no external illumination was used. A computer was used to trigger the cameras simultaneously so that the images are concurrent and the cameras are also synchronized with the robotic welder to snap the images when the arc is off. The paired images are rectified using calibration parameters obtained through the stereo calibration procedure. As the weld pool surface was highly patterned in the experiment, an image correlation-type measure was used to match points between the two rectified images. Then by using stereo processing algorithms the weld pool shape is rendered in three dimensions, which can be seen in Fig. 3.19. The complication of this method makes it unsuitable for practical application.

–16 –18 –20 –22 –24 –26 –28 –30 –58

–56 –54

635

638

642 640

(b)

(a)

3.19 Stereovision method to measure 3D weld pool surface [12]: (a) stereo image pair and (b) rendered weld pool 3D plot.

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Application of optical sensors

In the welding processes, different optical sensor components (systems) can be used for different applications. According to the sensed light sources, the applications can be further divided into two classes: arc-light-based (passive) and external-light-based (active) applications.

3.5.1 Arc-light-based application Welding arc radiation during the welding process holds valuable information which can be analyzed using optical sensors and used as a feedback to control the welding process. Apart from the 3D weld pool measurements that use stereovision system, arc-light-based applications may include detecting oscillations of the weld pool [13], measuring the plasma electron temperature [14], tracking weld seam and sensing arc force/pool depression. Some of them are discussed here. The optical sensor is used to detect the pool oscillation which is accomplished by collecting the specular reflection from the mirror-like weld pool surface. Specular reflection of the pool surface provides modal information of the pool’s vibrations, thus enhancing the detection and controllability of the process [13]. A schematic of the optical probe relative to the welding torch and workpiece is illustrated in Fig. 3.20. An optical sensor can also be used to perform spectral analysis of the arc plasma light, and to calculate the plasma electron temperature [14]. An optical sensor used in this configuration is shown in Fig. 3.21. The optical collecting system points towards the arc welding plasma, and is

Planar rotation GTAW torch Fiber optic cable

Probe elevation

Elevation rotation

Travel

Optical sensor Workpiece

Weld pool

3.20 Weld pool oscillation sensing system [13].

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Real-time weld process monitoring Manual gas tungsten arc welding torch Optic fiber Spectrometer

Filling wire

Light collecting system

Data storage reference signal

A/D converter Defect detection algorithm

Arc plasma Data storage

Defective weld flagging

Workpiece Root pass welded joint

3.21 Schematic diagram showing optical sensor for plasma temperature monitoring [14].

lodged into the nozzle of the torch and coupled to a 50 μm core diameter optical fiber. The radiation, collected from the arc–metal interaction zone is transmitted to a 10 μm entrance slit of a miniature spectrometer, equipped with a diffraction grating of 1800 lines/mm and a CCD detector array. The spectral range detected in this configuration is from 390 to 575 nm with an optical resolution of 0.3 nm. The spectrometer is interfaced with a computer and thus the real-time plasma electron temperature can be calculated. A different type of research was done by Wu et al. from the Institute of Material Joining at Shandong University, China using a regular CCD sensor to measure a 2D weld pool boundary [15]. The CCD was fitted with a narrow band filter centered at 610 nm, bandwidth 20 nm and transparency of 27%. A series of image processing algorithms were then performed and the two-dimensional size (length and width) of the weld pool is thus determined. The research performed had practical implementations in the industry since the equipment used was cheap, and was based on passive sensing (no laser).

3.5.2 External-light-based application In the external-light-based applications, other light sources, such as laser light and X-ray, are introduced as the external active illumination light source in the welding process because of some specific characteristics. For example the advantages of laser light include high intensity, specific wavelength and coherent directionality. As the external-light-based systems, stroboscopic video camera system and structured light system are used to

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X-ray source

Welding torch

Shutter

(–) (+)

Electrode

I

Liquid gallium Workpiece

Pivot

Shutter

Digital scale

R5232

Image intensifier and camera

Image processing board

Balance beam

Balance weight

VCR Welding interface

IBU-AT

(b) 15

15

10

10

5 Thickness (mm)

0

5 (a) 0

225 A 5 10

15

0

15

15

10

10

5 0

5

10

15

5

300 A 10

15

5 (c) 0

275 A 5 10

15

0

15

15

10

10

5 0

250 A

(b) 0

(d) 0

5 325 A

(e) 0

5

350 A (f) 0 10 15 0 5 10 Cross-pool location (mm)

15

3.22 Weld pool penetration experiment using X-ray [16]: (a) schematic of experimental and (b) typical shapes of pool depression.

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measure 2D and 3D weld pool surfaces. Other possible external-light-based applications include monitoring metal transfer process and sensing weld pool penetration. Next generation GMAW machines require the rapid metal transfer process to be accurately monitored using a high-speed vision system and to be feedback controlled. In order to make locating and measurement of droplets more accurate in the existence of a strong arc, laser light is introduced and it is projected on to the metal transfer area from the backside. Since the droplets block the laser light, the filter-fitted CCD camera placed in the frontside can clearly record the metal transfer process. Corresponding image processing algorithms are developed to process the results. An X-ray source, image intensifiers and a camera are used to compute the depth of penetration in an experiment [16]. The real-time radiographic apparatus setup is shown in Fig. 3.22. The X-ray tube produces a collimated beam which penetrates the material. The modulated X-ray field by the weld pool is acquired using an image intensifier and a video camera. From the image the depth of depression can be computed. The images taken are usually positive, which means that thinner sections are lighter than thicker ones. Therefore the weld pool surface topography can be determined from radiographic images using the image brightness to thickness relationship [16]. A perfectly linear relationship between arc force and pool depression exists within a certain current range. The weld penetration and pool depression are closely related and therefore the weld depression is useful for controlling weld penetration.

3.6

References

1 p. j. li and y. m. zhang, ‘Analysis of arc light mechanism and its application in sensing of GTAW process’, Welding Journal, 79(9): 252s–260s, 2000. 2 e. w. kim, c. allemand and t. w. eagar, ‘Visible light emissions during gas tungsten arc welding and its application to weld image improvement’, Welding Journal, 66(12): 369s–377s, December 1987. 3 d. renker, ‘Photosensors’, Nuclear Instruments and Methods in Physics Research A 527: 15–20, 2004. 4 kodak ccd primer, ‘Charge-coupled device (CCD) image sensors’ (http://www. kodak.com/us/en/digital/pdf/ccdPrimerPart2.pdf). 5 hamamatsu technical information, ‘Characteristics and use of FFT-CCD area image sensor’ (http://sales.hamamatsu.com/assets/applications/SSD/fft_ccd_ kmpd9002e06.pdf). 6 technical advisory service for image (tasi) advice paper, ‘Digital cameras’, www.tasi.ac.uk. 7 r. kovacevic, y. m. zhang and s. ruan, ‘Sensing and control of weld pool geometry for automated GTA welding’, ASME Journal of Engineering for Industry, 117(2): 210–222, 1995.

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8 r. kovacevic and y. m. zhang, ‘Real-time image processing for monitoring of free weld pool surface’, ASME Journal of Manufacturing Science and Engineering, 119(2): 161–169, 1997. 9 http://www.olympusindustrial.com/index.cfm/page/products.index.cfm/ cid/1324/navid/185/parentid/1 10 g. saeed et al., ‘Weld pool surface monitoring and depth extraction using a calibrated CCD sensor’, Trends in Welding Conference, ASM International, Pine Mountain, GA, 16–20 May 2005. 11 h. song and y. zhang, ‘An image processing scheme for measurement of gas tungsten arc weld pool surface’, IEEE Conference on Industrial Electronics and Applications (ICIEA 2007), May 2007. 12 c. mnich, f. al-bayat, c. debrunner, j. steele and t. vincent, ‘In situ weld pool measurement using stereovision’, ASME, Proceedings 2004, Japan – USA Symposium on Flexible Automation, Denver, Colorado, 19–21 July 2004. 13 d. a. hartman, d. r. delapp, g. e. cook and r. j. barnett, ‘Intelligent fusion control throughout varying thermal regions’, Proceeding of the IEEE Industry Applications Conference, Phoenix, AZ, 3–7 October 1999, Vol. 1. 14 a. ancona, p. m. lugara, f. ottonelli and i. m. catalano, ‘A sensing torch for on-line monitoring of the gas tungsten arc welding process of steel pipes’, Measurement Science and Technology, 15: 2412–2418, 2004. 15 c. s. wu, j. q. gao, x. f. liu and y. h. zhao, ‘Vision-based measurement of weld pool geometry in constant-current gas tungsten arc welding’, Proc. Instn Mech. Engrs Part B: Journal Engineering Manufacture, 217(6): 879–882, 2003. 16 s. i. rokhlin and a. c. guu, ‘A study of arc force, pool depression, and weld penetration during gas tungsten arc welding’, Welding Journal, 72(8): 381s–390s, 1993.

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4 Infrared sensors in welding H. YANG, H. C. WIKLE, S. NAGARAJAN, M. JOHNSON, P. BANERJEE and B. A. CHIN, Auburn University, USA

4.1

Introduction

Infrared sensing is a type of non-contact thermal measurement technique which has been widely used in many applications relating to such diverse areas as industrial processing, environmental monitoring, astronomical investigations, and military purposes, as well as in our daily lives. In this chapter, we will discuss the use of infrared sensors for real-time welding process monitoring and weld quality control. In order to better understand the measurement principles of infrared sensors, a general description and the physics of infrared sensors are presented first. Next, the use of thermal distributions to monitor weld characteristics are explained, along with a comparison of the thermocouple and thermography techniques for thermal distribution measurement. Variations in the weld process parameters, such as current, voltage, and welding speed, distinctly alter the thermal distributions. Therefore, the weld characteristics, which are controlled by the process parameters and material characteristics, can be tracked by monitoring changes in the thermal distributions. In the main part of this chapter, the applications of infrared sensing techniques to monitor and control in real time the production of arc welds are discussed. This section covers the use of infrared sensors in the monitoring and control of four specific weld parameters: weld bead width, penetration depth, weld position, and cooling rate. The relationships between weld characteristics and thermal distributions are discussed, and the application of observed changes in thermal distributions to rectify weld process variations in real time is described.

4.2

Theory of infrared sensors

Infrared sensors are thermal measurement devices that detect infrared radiation emitted by materials. All objects at a temperature greater than absolute zero emit electromagnetic radiation randomly in every direction and the intensity of the radiation depends on the temperature and material properties. The emissive power, defined as the radiative heat flux from a 74 WPNL2204

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10 8 Spectral emissive power, eλ,b (W/m2 ⋅μm)

10 7 10 6 10 5

4000 K 1810 K

10 4

996 K

10 3 10 2 10 1

300 K

10 0 10 –1 10 –2 10 –3 10 –4 0.1

1.0

10 .0

100.0

Wavelength, λ (μm)

4.1 Planck’s spectral distribution for a blackbody. Plotted temperatures represent the approximate temperature of the welding arc (4000 K), the melting point of pure iron (1810 K), the eutectoid temperature from the Fe–C phase diagram (996 K) and room temperature (300 K). The shaded areas correspond to the visible spectrum (0.4–0.7 μm) and the bandwidth of common infrared cameras.

surface, is distributed over a wide range of wavelengths for an object at a given temperature. An ideal blackbody’s emissive power, described by Planck’s law, is shown in Fig. 4.1 for several different temperatures. A peak in the blackbody emissive power occurs at a specific wavelength and is described by Wien’s displacement law. For temperatures that range from normal environmental conditions to those encountered in welding processes, the peak emissive power occurs at wavelengths in the range from the mid-infrared to the visible spectrum. Real surfaces differ from ideal blackbodies in that they exhibit emissivities less than one and may reflect and/or transmit the incident electromagnetic radiation. The surface condition is affected by the degree of surface roughness and the presence of foreign coatings such as oxides, oils or greases, water and dust. Despite these difficulties, both visible and infrared sensors have been investigated by numerous researchers for use in welding process measurement and control. An infrared sensor system normally comprises three basic components: (1) an optical lens to collect and focus the incident radiation emitted by an object; (2) the infrared sensitive detector material, which can generate an electrical signal by its interaction

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with the radiation; and (3) a signal processor connected with a computer for analog to digital signal conversion [1]. The detector is the main part of an infrared sensor system and is of two types: thermal detectors and photonic detectors. For thermal detectors, such as thermistors and thermopiles, their signal output is proportional to the absorbed energy, while the signal output by photonic detectors is related to the excitation generated by the incident photons [1]. Infrared sensors that operate in the band from 0.7 to 20 μm are normally used for practical measurement; for wavelengths greater than 20 μm, the power of the incident radiation is too small to be detected except for special high-performance instruments [1]. In the area of welding process measurement and control, infrared cameras have been investigated and the results show that this method has the capability of detecting penetration depth variations through changes in surface temperature distribution. These changes in temperature distribution have been used for adaptive process control; however, a high initial cost, delicate optical components, and arc radiation interference have contributed to the slow implementation of this class of sensors. Recent advances in infrared detection technology have produced low-cost, rugged infrared thermopile detectors that are not susceptible to arc radiation effects. Among these, studies performed using point infrared sensors for welding process control show that these may eliminate the disadvantages of infrared cameras. To briefly explain the physics of infrared sensing, a derivation of the radiative exchange of heat between two surfaces is presented for the case of a point sensor with the detector surface parallel to the weld surface. The following assumptions were employed throughout the analysis: (1) all surfaces are treated as blackbodies; diffuse emitters, emissivity of unity; (2) constant temperature over all designated surfaces; and (3) absorption by gases and other materials in the optical path are neglected. The notation of Siegel and Howell [2] was used in this analysis (in Table 4.1). Table 4.1 Symbol definitions used in net heat exchange analysis Symbol

Definition

Units

i b′ λ T A S L D r n σ C1 C2

Blackbody radiant intensity Wavelength Temperature Surface area Distance between two points Target distance, separation distance Diameter Radial distance Surface normal vector Stefan–Boltzmann constant First radiation constant Second radiation constant

W/m2 sr μm K m2 m m m m

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77

dA1 at T1

θ1 n1

S n2

dω1

θ2

dA2 at T2

4.2 Radiative exchange between two differential surface area elements.

Referring to Fig. 4.2, the rate at which energy dq leaves a differential element d1 (with the differential surface dA1) and is incident upon differential element d2 (with the differential surface dA2) is given by: dqd →d = i′b,1dA1 cos θ1dω1 1

[4.1]

2

where dω1 is the solid angle subtended by dA2 when viewed from a point on dA1: dω1 =

dA2 cos θ2 S2

[4.2]

The blackbody radiant intensity i′b,1, is found by integrating Planck’s spectral distribution of emissive power by a blackbody,

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Real-time weld process monitoring eλb ( λ ) = πiλ′ b =

2 πC1 λ 5 ( eC2 /λT − 1)

[4.3]

over all wavelengths to give the familiar Stefan–Boltzmann law for the total hemispherical emissive power: ∞

eb = πib′ = ∫ eλb ( λ ) dλ = σT 4 0

[4.4]

Rearranging and substituting Eq. (4.2) and (4.4) into Eq. (4.1) gives the following equation for the rate at which energy leaves dA1 and is incident upon dA2: dqd1 →d2 =

dA1 cos θ1dA2 cos θ2 σT 14 πS 2

[4.5]

The total amount of energy per unit time that leaves surface A1 and is incident upon A2 is found by integrating Eq. (4.5) over the two surfaces: q1 →2 = σT 14

cos θ1 cosθ2 dA1dA2 πS 2 A2 A1

∫∫

[4.6]

The integrand in Eq. (4.6) is solely a function of the geometry of the configuration of surfaces A1 and A2. A view factor, dFd1–d2, which takes into account the geometry of the configuration, can be defined as the fraction of the radiation leaving surface dA1 that is incident upon surface dA2. This is expressed as: dFd1 →d2 =

dqd1 →d2 cos θ1 cos θ2 = dA2 dqd1 πS 2

[4.7]

An integrated view factor can also be defined; the fraction of the total radiation leaving surface A1 that is incident upon surface A2 is given by: F1 →2 =

q 1 →2 1 = q1 A1

cos θ1 cos θ2 dA1dA2 πS 2 A2 A1

∫∫

[4.8]

so that the total rate at which energy leaves surface A1 and is incident upon A2 can be written as q1→2 = A1F1–2σT 41

[4.9]

The above general derivation was for an arbitrary geometry. For complex geometries the double integral can be difficult to integrate. For the case of a point infrared sensor aligned normal to the weld surface, the configuration can be approximated by a simple geometry representing the exchange between a differential element and a finite circular area. A circular disk of diameter A1 exchanges radiation with a differential area dA2 where A1 corresponds to the weld and dA2 corresponds to the sensing element. The

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D dr r dA1

A1

θ1

L

S

θ2 dA2

4.3 Geometry for view factor Fd2−1 calculation between a differential area d2 and a circular surface of area A1.

double integral can be simplified by noting that the total energy radiated from dA2 is given by: qd = dqd = σT 42dA2 2

[4.10]

2

and the relevant view factor is: Fd2 −1 =

∫ dF

d2 − d1

A1

=

dqd2 →d1 cos θ1 cos θ2 = ∫ dA1 dqd2 πS 2 A1 A1

∫

[4.11]

With the aid of the geometry shown in Fig. 4.3, the last integral of Eq. (4.11) can be evaluated to give: Fd2 −1 = 2 L2 ∫

D/2 0

r

(r2 + L )

2 2

dr =

D2 D + 4 L2 2

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so the total rate at which energy leaves dA2 and is incident upon A1 is: qd →1 = dA2Fd −1σT 42 2

[4.13]

2

To determine the total energy leaving A1 that is incident upon surface dA2, the view factor reciprocity relationship, given by Eq. (4.14), can be used to determine the second view factor, dF1−d : 2

dA2Fd −1 = A1dF1−d 2

[4.14]

2

The net exchange of heat between A1 and dA2 is then the difference in the total energy transfer from one surface to the other: q1⇔d = q1→d − qd →1 2

2

[4.15]

2

This gives for the total heat exchange between surface A1 and dA2 q1⇔d = A1dF1−d σ(T 41 − T 42) 2

2

[4.16]

This expression allows the approximation of the net energy exchange between the target surface and the detector. Each term on the right side of Eq. (4.16) can now be calculated from either the geometry of the configuration or a welding heat transfer model. Based on the analysis of the radiation heat exchange between the weld and the sensor, the characteristics of the infrared sensor’s response can be calculated to provide a theoretical prediction for the optimum position of the sensor about the welding arc. A detailed description of using infrared sensing techniques in the welding process will be presented in Sections 4.3 and 4.4.

4.3

Measurement of weld temperature distribution

Arc welding is inherently a thermal processing method. In this regard, infrared sensing is a natural choice for weld process monitoring. In many respects, it is similar to optical sensing methods in that non-contact measurement of the emissions of electromagnetic radiation from the surface of the weld is used to quantify some of the weld bead and weld characteristics. Surface temperature estimation, cooling rate, seam tracking, bead width, and penetration depth have been investigated using infrared sensors for monitoring and control. For the case of penetration depth control though, this method suffers from the fact that it is an indirect method of measuring the depth of penetration. The penetration depth control systems that have been reported are based on the assumption that the surface temperature distributions are indicative of the conditions below the surface of the weld. Real-time welding process monitoring techniques can either be direct, where variations in the actual weld characteristics are measured, or indirect, where the effects of the weld characteristics on closely related parameters

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are monitored. In the past half century, several parameters have been studied for their potential use in the control of the various weld characteristics [3–37]. During the welding operation, thermal distributions are determined by such process parameters as welding speed, voltage, and current, as well as the material properties of the weld, such as surface tension and thermal conductivity. Since thermal distributions are distinctly altered by variations in these parameters, they can be used to track weld characteristics which are determined by material properties and process parameters. Temperature may be measured by contact sensors such as thermocouples, resistance temperature detectors (RTDs) and thermistors, or by non-contact infrared sensors such as photodetector diodes, thermopiles, and infrared thermography. Thermocouples have been used in various configurations to measure temperature distributions during the welding process [38–40]. Extremely fine-gauge thermocouples have been welded to the edges and surfaces of weld in order to measure weld cooling rates [38]. Thermocouples have also been immersed into the weld pool to measure the temperature as it cools [39]. The temperature at interior locations in plates has been measured by inserting and welding thermocouples into drilled holes [40]. A significant problem with all these cases is that the thermocouples tend to alter the thermal field being measured. In addition, since the thermal changes that occur during the welding process are rapid, the inherently slow response and low spatial resolution of thermocouples present significant disadvantages in their use for process control. Non-contact infrared thermography can provide a two-dimensional image of the infrared emissions in an area of the weld. This is particularly useful for providing a sense of the overall flow of heat during the welding process. Line scan and point sensing techniques are especially attractive for real-time control applications due to their lower computational requirements. Point infrared sensors have advantages due to their small size, low cost, ruggedness and lack of sophisticated optics that allow their direct placement in the harsh environment near the welding arc. The early application of infrared sensing techniques to welding process monitoring and control was initially investigated by Ramsey et al. [41] where a thermistor and a PbS-based scanning sensor were evaluated for use as a frontface sensor to detect electromagnetic emissions during gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). Smith [42] employed a silicon photodiode array to monitor backface emission to control penetration depth. Farson et al. [43] also employed a silicon diodebased optical pyrometer to measure frontface surface temperatures during GTAW. Backface emissions were also monitored by Song and Hardt [44] to control the penetration depth. Frontside thermal emissions were monitored with a thermopile sensor for controlling penetration depth in

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submerged arc welding by Wikle et al. [45]. Baik et al. [46] described a technique for discriminating between power variations and focus shift during pulsed Nd:YAG laser welding by measuring the thermal radiation from the weld pool at two different wavelengths using silicon photodiodes and applying chromatic filtering. A feasibility study was conducted by Chin et al. using a thermographic infrared camera that showed that various disturbances to the welding process produced signature effects on the surface temperature field [47]. Based upon this work, several penetration control schemes have been developed. Khan et al. [48] proposed a control strategy based upon the radii of the surface isotherms. Nagarajan, Chen and coworkers [49–51] proposed and implemented a control strategy based upon the size and area enclosed by the surface isotherms. Wang and Chin [52] proposed a control strategy based on the width and height of a line scan through the molten weld pool. In an extension of this idea, Banerjee et al. [53] and Govardhan [54] implemented a penetration control system based upon the thermal gradient across the weld pool. Other investigators have utilized thermographic infrared sensing techniques to monitor and control the penetration depth during the welding process. Bangs [55] correlated the surface temperature distribution to the weld bead geometry. Bates and Hardt [56] and Doumanidis and Hardt [57–59] reported a system that controlled the weld bead geometry and the heat affected zone using infrared thermal profile measurements. Marburger and coworkers [60,61] described a penetration control system that operated by measuring the emissions from the backface of the weld. Raj and coworkers [62,63] have recently investigated bead width and penetration depth control using infrared thermography. Infrared sensing of the welding process is not without its difficulties, however; the primary issue being the interference with the desired measurement due to arc radiation and welding electrode emissions. These interferences may be mitigated by using either mechanical or optical means to filter the unwanted thermal emissions.

4.4

Infrared sensors for real-time weld quality control

The monitoring and control of four important weld characteristics – weld bead width, penetration depth, weld position, and cooling rate – are described in the following subsections. Thermographic infrared sensing is by far the most extensively used technique for monitoring and control of the weld process. In addition, point infrared sensors also show great promise in welding process control. Because of their compactness and ruggedness, the sensors can be placed close to the welding arc but are not susceptible to arc radiation effects.

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4.4.1 Weld bead width Weld bead width and penetration depth are the two main characteristics that influence the properties of the welding joints. In additional, variations in the size and shape of the joint to be welded are produced by the low tolerance in joint preparation. During the welding process, weld bead depth and penetration depth arc significantly altered by variations in voltage, current, arc length welding speed, and electrode parameters. Hence, it is important to monitor and control these parameters in real time so as to adjust the variations in weld process parameters and joint configuration. Weld bead width can be measured by observing the sharp temperature gradients at the weld pool edge and the difference in emissivity between the liquid and solid metal. However, the interference of radiation from the arc must be overcome in order to measure these changes. Ramsey et al. monitored regions behind the arc to avoid interference from its radiation [41]. A spot infrared sensor was used to measure variations in bead width induced by changing weld process parameters such as voltage, current, and travel speed. The measured intensity of the infrared radiation was found to vary with changes in the welding parameters, but these intensity values were not a unique function of the expected temperature. These above experiments were repeated using a scanning infrared sensor, where the thermal distribution was measured along a line 15.24 mm behind the arc and transverse to the weld. Bead width was found to be proportional to half-height and base width in all experiments (see Fig. 4.4). It was determined that arc radiation continued to affect the output of the sensor, even though regions behind the arc were monitored. Experiments performed with and without mechanical shielding of the arc radiation showed that interference from the arc affects half-height, but not base width. From this, a conclusive relationship between the changes in weld thermal field and bead width could be drawn. However, with this technique, bead width is measured by monitoring regions behind the arc, and so it is thus not applicable to on-line bead width measurement and control. Rider used a linear array of silicon photodiodes as the sensor for measuring weld bead width [64,65]. Arc interference problems were eliminated by momentarily interrupting the arc during the measurement. Weld pool edge temperatures have a maximum emission wavelength of approximately 1.6 μm. Since these photodiodes have a spectral response that decreases in the range 0.9–1 μm, their output becomes saturated when the weld pool is monitored. The array measures weld pool thermal energy when the arc is off, with the weld bead width given by the width of the relatively flat peak in the output. The only real drawback of this technique is, of course, that the arc must be momentarily shut off, and then re-ignited.

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Scanner signal output (v)

20 15 10 Half-height

5 0

Base width 0

3

6

9

Scanner distance (inches) (a)

0.8 se)

l (ba

Width (inches)

0.6

0.4

na r sig

nne

Sca

Scanner

lf-height)

signal (ha

Weld bead 0.2

0 120 (b)

140

160

180

200

Welding current (A)

4.4 (a) Distribution of thermal radiation along a line transverse to the welding direction and behind the arc. (b) Effect of changes in allied current on weld bead width and features of thermal distribution [41].

The effects of arc radiation may also be overcome by selecting sensors that will be able to ignore this wavelength range. Nagarajan et al. used a scanning infrared sensor with a spectral response greater than 2 μm [66]. Figure 4.5 shows the intensity distribution of a line across the center of the weld pool, as well as its first derivative. A change in the slope of the intensity

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Infrared sensors in welding 255 240 220 200 180 160 nt 140 Gradie 120 100 Line scan 80 60 40 20 0 4 24 44 64

85

Bead width 84 104 124 144 164 184 204 224 244 Distance (pixels)

4.5 Intensity distribution of a line across the weld pool and its first derivative.

profile, caused by the difference in emissivity between the liquid and solid metal, was found to correspond to the edges of the weld pool. These edges can be more clearly seen in the plot of the first derivative, and the weld bead width can be determined by taking the distance between the local minimum to the left of the zero derivative, and the local maximum to the right. The accuracy of this measurement technique was tested by varying the weld bead width, which was accomplished by changing the welding current. Figure 4.6 shows excellent agreement between the predicted and measured weld bead widths in this experiment. The size and shape of the weld pool front as it advances have also been mapped using this technique [66]. Boillot et al. reported similar changes in intensity at the weld pool edges [67]. They used a fiber optic device for discrete point sensing to measure the intensity distribution of a line across the weld pool. Weld bead width was controlled in real time by using the observed changes in intensity.

4.4.2 Penetration depth From the previous section, it can be seen that the measurement of bead width is fairly straightforward since it involves detecting the edges of the weld pool at the surface of the plate. This is not the case with penetration depth, however, since direct measurement would involve observation of the interior of the part being welded. Therefore, weld penetration depth must be related to a parameter that can be measured at the surface of the plate.

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Real-time weld process monitoring 5.00

Computed bead width (× 2.54 mm)

4.50

4.00

3.50

3.00

2.50 2.50

3.00 3.50 4.00 4.50 Measured bead width (× 2.54 mm)

5.00

4.6 Comparison of predicted and measured weld bead width.

Weld penetration measurement with infrared techniques Among the first to identify such a parameter were Malmuth et al., who related the thermal distributions of a plate to weld penetration depth [68]. They studied the effect of high-conductivity backing plates on the isotherms and weld penetration depth in the plane normal to the welding direction. Changes in the isotherms of this vertical plane were observed using a vidicon infrared sensor with cinematographic attachments. The penetration depth of the weld was found to be limited by the presence of a copper backing plate, corresponding to a restriction in the isotherms of the vertical plane (Fig. 4.7a). Conversely, a deeper weld was obtained in the absence of the backing plate for the same welding conditions, with the vertical plane isotherms exhibiting a corresponding elongation (Fig. 4.7b). The possibility of controlling weld penetration depth by monitoring thermal distributions was raised by these observations. The two basic methods for controlling penetration depth based on the measurement of thermal radiation are those that observe the backside of the plate being welded, and those that observe the front. Boughton et al. developed a real-time penetration depth controller using the backface technique, where radiation from a fully penetrated weld was measured with photodiodes [69]. For the ideal case, where complete weld penetration depth is achieved, the photodiodes detect the maximum amount of

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Plate being welded

Backing plate

(a)

Arc Weld penetration direction

Plate being welded

(b)

4.7 Schematic of vertical plane temperature profiles in the (a) presence and (b) absence of copper backing [68].

radiation. For penetration depths of less than 100%, the photodiodes detect a lower amount of radiation than in the ideal case. This technique was used successfully to control the welding process such that 100% weld penetration depth was produced automatically, even in a plate with step changes in thickness. An alternative method was used for controlling partially penetrated welds [64,65]. This technique computes the penetration depth by measuring voltage, current, welding speed, and weld pool width, and then developing a semi-empirical relationship between these variables. Voltage, current, and travel speed are, of course, easily measured, but, as explained in the previous section, measurement of weld pool width must be synchronized with momentary extinction of the arc. This method was found to successfully maintain a fixed penetration depth in a plate with step changes in thickness. In the case of monitoring weld penetration depth using the frontface method, a technique was developed by Chen et al. [70]. In their work, a plate with changes in thickness (both step and gradual) was welded using constant process parameters. The resulting thermal radiation was measured, yielding thermal distributions corresponding to the step and gradual changes in weld penetration depth. Figure 4.8 shows the thermal distributions measured from the frontside of a plate and displayed as isotherms, with each isotherm depicted schematically as a region with a unique pattern. This data were processed by using the least squares method to fit an ellipse to the lower part (ahead of the weld) of a selected isotherm. By bounding this ellipse with its minor axis and summing all the intensities contained within

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Fitted ellipse

Minor axis of ellipse

Thermal volume = sum of all intensity values in the shaded area

4.8 Weld thermal distributions and penetration depth indicators.

this area, a thermal volume was obtained which was observed to be proportional to penetration depth (see Fig. 4.9). The primary advantage of this technique is that changes in penetration depth can be monitored from the front-face of the plate without the need to interrupt the welding process by temporarily shutting off the arc. A point sensor was developed by Wikle et al. [45] to demonstrate that weld bead penetration depth could be monitored and controlled during both GTAW and submerge arc welding processes to eliminate or reduce weld detects. Changes in the plate geometry (gap size, plate thickness, and cooling sinks representing stiffeners) were introduced in the experiment to perturb the welding process. Using the sensor, a constant depth of penetration was maintained in the presence of these perturbations by feedback control of the welding process parameters. Minor element effects on weld penetration The critical variable that affects bead geometry is base metal composition. Normally, the minor element composition rather than the major component in the alloy drastically affects the weld penetration and is the most difficult to control. Variations in the weld bead depth-to-width (D/W) ratio as high as 200% can result from changes in sulfur concentrations of less than 100 ppm [71]. Though resulfurization may be expected to increase the D/W ratio, it also increases the chances of hot cracking. Besides sulfur, other

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100 80 60 40 20 0

350 300 Minor axis

250 200

Volume 150 100 6.4 mm 0

Cross-section of the steel plate 3.2 mm 4.7 mm

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100 150 Frame No.

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350

100 80 60 40 20 0

300 Minor axis / volume

250

250 Minor axis

% Penetration

Minor axis / volume

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% Penetration

450

200 150

Volume

100

Cross-section of the steel plate 3.2 mm

6.4 mm 0 (b)

50

100

150

200

250

Frame No.

4.9 Effect of (a) step changes in penetration depth on the parameters of the fitted ellipse; and (b) gradual changes in penetration depth on the parameters of the fitted ellipse.

elements, such as oxygen, aluminum, selenium, titanium, tellurium, and cerium, may also influence weld penetration [72,73]. Banerjee et al. [74] conducted experiments to relate penetration depth changes caused by minor element content of the plates being welded to the surface temperature distribution obtained using an infrared sensor. A 25.4 mm wide coating of the desired impurity element (titanium dioxide or boron nitride) was applied transversely to the line of motion of the welding electrode. The obtained thermal distributions clearly showed the region of

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4.10 Thermal distributions for different locations with applied TiO2 coating.

altered penetration. Contamination by titanium dioxide was found to increase penetration; however, the boron nitride impurity caused the opposite effect. Four isothermal data frames for the run using a titanium dioxide coating are shown in Fig. 4.10. Of these, frames 1 and 60 are situated before and after the coating strip; frames 24 and 50 are situated at the start and towards the end of the strip, respectively. As seen from frame 24, there is a short vertical segment on the outermost isotherm on both sides of the torch. This clearly identifies the start region of altered penetration. The movement and the size of the coating can be estimated from the two vertical segments seen in frame 60. The changes in isothermal shape compare well with actual penetration measurement as shown in Fig. 4.11. This figure clearly shows an increase in penetration at a distance between 76.2 and 101.6 mm from the start of the weld, which corresponds to the coating strip. Experiments using two types of minor element inserts were also conducted by Banerjee et al. [74]. One type contained an element of sulfur with a positive surface tension coefficient; the other contained an element of aluminum with negative surface tension coefficient. As shown in Fig. 4.12 for the weld passing through the aluminum inserts, the temperature gradient was found to decrease at the locations containing inserts. This can be explained by considering what happens to the weld pool at these locations. Owing to surface tension effects, aluminum increases the width of the weld pool. Therefore, given the same amount of heat input from the welding

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70 60 Depth/width ratio (%)

50 40

TiO2 coating

30 20 1

10 0

0

1

25 50 Frame no.

75

2 3 4 5 6 Distance from start of weld (× 25.4 mm)

7

Slope at given row and column (1/pixels)

4.11 Penetration versus distance for a weld using a TiO2 coating.

3

2

1

Aluminum inserts 0 0

8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 Frame number

4.12 Temperature gradient for aluminum insert experiments.

torch, a wider pool would mean a smaller temperature gradient across the weld pool. The results for the weld passing through iron sulfide inserts are also shown in Fig. 4.13. Unlike aluminum, sulfur decreases the width of the weld pool and increases the depth. This increases the temperature gradient sharply. It must be remembered that this type of change in the gradient at a point is unique to minor element effects and cannot be expected to result from other perturbation such as plate thickness changes.

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92

2

1

0

-1 0

Iron sulfide inserts 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 Frame number

4.13 Temperature gradient for iron sulfide insert experiments.

4.4.3 Weld position control For proper welding process control, it is not enough to monitor weld bead width and depth, but the weld pool itself must be placed at the center of the intended joint. Errors in the position of the weld pool with respect to the joint result from errors in the positioning systems that align the torch and the joint, the low tolerance in part preparation and fixturing techniques, as well as part distortion during welding due to thermal expansion. These positioning errors may be dynamically tracked by monitoring the effects of weld pool offset from the joint, or by monitoring the position of the joint itself. Several studies have been performed to identify and adjust for these weld pool-joint offsets by monitoring the changes in weld thermal distributions [67,70,75,76]. Joints with gap The presence of a gap in a joint has been shown theoretically to produce a unique drop in the temperature distributions. The feasibility of this method for on-line identification of the position of such joints was investigated by welding plates with joint gaps. Boillot et al. used the thermal distributions in front of the arc for joint position identification [67]. The infrared intensity distribution of a line transverse to the welding direction, and leading the weld pool by 2 mm, was measured using an array of eight optical fibers. The result showed that a finite root gap in the joint was found to cause a sharp decrease in the measured infrared intensity of the middle two sensors. Nagarajan and Chin

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Infrared sensors in welding 255 240 220 200 180 160 140 120 100 80 60 40 20 0 4

24 44

64

93

84 104 124 144 164 184 204 224 244 Distance (pixels)

4.14 Temperature distribution of the joint gap.

reported a similar drop in infrared intensity at gaps in butt weld joints [76]. The temperature distribution measured along a line transverse to a joint with a 0.762 mm gap is shown in Figure 4.14. The observed drop in the temperature at the joint gap was produced by two factors. The principal reason is that the thermal conductivity of the joint gap is lower than the thermal conductivity of the plate. The drop in thermal conductivity reduced the heat flow rate and thus resulted in lower temperatures at the gap. The second factor is the difference in emissivity of the joint. The emissivity of the edges of the plate is higher than the emissivity of air in the joint gap. Thus the intensity of the infrared radiation from the edges was higher than the intensity from the joint gap. The temperature drop measured at the gap resulted from a combination of these two factors. The effect of the joint gap on the temperature gradients due to difference in thermal conductivity was also confirmed by the theoretical analysis (see Fig. 4.15). The gap position was characterized by three changes in sign of the temperature gradient profile. The second change can be used to locate the joint gap position and the distance between the first and third changes can be used to determine the size of the join gap. By locating this characteristic decrease in infrared intensity, the position of the joint may be determined at any instant during the welding process. Curved joint traces have been successfully tracked through the application of this joint position identification technique [76]. During the tracking process, the temperature distribution of a line normal to the joint and at 7.5 mm from the weld pool leading edge was measured periodically. The coordinates of the joint gap were determined from the computed temperature gradients using the analysis techniques explained previously. The

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Temperature gradient (Volts/pixels)

3.4 2.4 1.4

Gap center

0.4 -0.6 -1.6 -2.6 -3.6 4

Gap 24

44

64

84 104 124 144 164 184 204 224 244 Distance (pixels)

4.15 Temperature gradients of the joint gap.

(a)

(b)

4.16 (a) Joint contour, (b) Tracking performance of the controller (joint gap size = 1.016 mm).

calibrated position of the torch was assumed as the coordinates of the weld pool. Then the gap and weld pool coordinates were compared to determine whether the weld is located on or off the joint gap center. When the weld was offset from the gap center, the torch was moved to rectify the detected position error. The performance of the system is shown in Fig. 4.16 for

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tracking of a joint contour with a gap of 1.016 mm. The reliability of the tracking system and the tracking of joints with lower gap sizes were also investigated. The result shows that the system can be used reliably to track joints with a gap as low as 0.635 mm. Perfectly fitted joints Groom et al. observed that perfectly fitted V-groove joints produced a distinct anomaly in the thermal distributions [75]. The thermal distributions in and around the weld pool were measured with a scanning infrared sensor and then represented using an isotherm display (see Fig. 4.17) similar to that of Chen et al. [70]. In the lower part of the isotherms (ahead of the weld), a notch-type anomaly can be seen, which was found to correspond to joint position. Radiation reflected from the V-groove of the joint was found to be the cause of this anomaly. Position errors between the torch and the joint were able to be identified and corrected in real time by knowing the position of the torch relative to this notch in the thermal distribution profile. Figure 4.18 shows the accuracy of this joint tracking system in following a curved seam contour.

4.17 Weld thermal distributions in V-groove joints.

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4.18 On-line tracking of curved joint contour.

The most difficult challenge for joint tracking systems is presented by perfectly fitted butt weld joints. A different approach to identify and adjust for weld position errors in these types of joints was used by Nagarajan and coworkers [75]. In this research, the effects of weld pool offset on the resulting thermal distributions was monitored as opposed to trying to locate actual joint position. The thermal distributions of the weld pool and its vicinity were measured for both on-joint and off-joint weld pool locations, and were displayed using the isotherm scheme introduced by Chen et al. [70]. For the on-joint welding condition, there is equal distribution of the heat input from the arc to the plates being welded, producing symmetrical thermal distributions (Fig. 4.19(a)). For the case of off-joint weld pool location, there is uneven distribution of the arc’s heat input to the plates, with the plate towards which the weld pool is offset receiving more heat than the plate that is further away. This results in asymmetrical thermal distributions as can be seen in Fig. 4.19(b), where the weld pool offset is to the left of the joint. For an asymmetrical thermal distribution, such as the one in Fig. 4.19(b), the left and right halves of any isotherm will be unequal. The degree of asymmetry of the thermal distribution may be described by the difference between the two halves of an isotherm. In order to simplify this half-size isotherm measurement, the isotherm radius is measured for each half by taking the distance between the isotherm edge and the calibrated torch position. The difference between the left and right isotherm radii along a selected angle can now be used to express the degree of asymmetry. Returning to Figure 4.19, it can be seen that isotherm radii measured along 45° angles have been superimposed on both thermal distributions. For the onjoint case, the isotherm radius of each half-size is nearly equal while, for the off-joint condition, the radius of the left half (toward which the weld pool is skewed) is nearly twice that of the right half. This technique provides a simple method for dynamically identifying position errors between the weld and the joint, and was used for real-time identification and correction of an initial error in weld position (see Fig. 4.20).

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(a)

(b)

4.19 Weld thermal distributions in perfectly fitted butt joint.

4.4.4 Cooling rate When welding materials with a closely specified thermal history, such as high-strength steels, the measurement and control of weld cooling rate are extremely important. In the original manufacturing process of these steels, specific heat treatment cycles are designed and used to produce microstructures that yield superior mechanical properties. During the welding process,

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4.20 On-line correction of initial weld-joint position errors.

cycles of rapid heating and cooling induce solid-state transformations in these steels, potentially destroying the desired microstructure and, hence, weakening the material. Since it is obviously crucial to limit these transformations during welding, it is therefore important to control the rate at which the weld cools. In most cases, weld cooling rate is estimated from heat input, which partially depends on such process parameters as voltage, current, and welding speed. As the initial step in achieving the desired cooling rate, the appropriate process conditions must first be selected. However, cooling rate is also affected by other factors, such as plate thickness variations and fixturing, so these must also be taken into account. Since plate thickness variations will alter heat transfer rate, and the specific fixturing method may also alter the heat transfer mechanisms, the actual weld cooling rate must be measured for any given process and setup instead of merely being estimated from process parameters. Weld cooling rate may be measured by several techniques, the advantages and disadvantages of which were discussed briefly earlier in this chapter. Because of its sensitivity, fast response, and non-contacting nature, infrared thermography is best suited for weld cooling rate measurement. Specific examples of this are provided by the work of Lukens and Morris [77] and Doumanidis and Hardt [58]. In particular, Lukens and Morris used

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infrared thermography to measure cooling rates during gas/metal and tungsten arc welding of HY-130 steel, a high-strength steel, the yield strength of which is very sensitive to weld cooling rate. A scanner sensitive in the spectral range of 2–5.6 μm was used to overcome interference from the arc. There were no emissivity-related variations in the temperature measurements due to the formation of an oxide layer on the HY-130 steel, which provided a uniform emissivity surface. The measured weld cooling rates were found to be reproducible and sensitive to variations in voltage, current, travel speed, and plate thickness. As a result, they were able to produce welds in HY-130 steels with high yield strengths.

4.5

Summary

Infrared sensors are capable of detecting changes in weld temperature distributions produced by variation in weld characteristics. Monitoring and control of four weld characteristics, weld bead width, penetration depth, weld position, and cooling rate, were described in this chapter. The motivation for controlling these characteristics was first explained, then the relationships between thermal distributions and weld characteristics were discussed, and finally the real-time rectification of weld process variations by application of observed thermal distribution changes were described. To date, infrared thermographic techniques have been mainly used to monitor and control the welding process. However, many intrinsic properties of these sensors, such as expense, fragility in the harsh welding environment, and too great a size to be placed close to the welding arc, have limited their practical industrial applications. The recently developed point infrared sensing techniques are able to overcome the above problems and therefore have obtained a lot of research interest in the areas of welding process monitoring and control. To improve the infrared sensing techniques for monitoring and control of the welding process, future work should focus on optimizing the infrared sensors’ performance, expanding radiation heat transfer models to accurately predict the real-time welding operations, and developing corresponding computer control of the infrared sensors to enable constant weld characteristics during the welding process.

4.6

References

1 a. r. jha, Infrared Technology, John Wiley & Sons, New York, 2000. 2 r. siegel and j. r. howell, Thermal Radiation Heat Transfer, Hemisphere Publishing, Washington, DC, 1981. 3 p. m. gonseth and p. blanc, ‘Optiguide: A new optical joint tracking device’, Welding Journal, 62, No. 9, 27–29, 1983.

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4 j. e. agapakis, j. m. katz, m. koifman, g. n. epstein, j. m. friedman, d. o. eyring, and h. j. rutishauer, ‘Joint tracking and adaptive robotic welding using vision sensing of the weld joint geometry’, Welding Journal, 65, No. 11, 33–41, 1985. 5 h. w. mishler and r. e. monroe, An Optical Sensor for Adaptive Control of the Welding Arc, International Institute of Welding (IIW) Document XII-K-575–73, Battle Memorial Institute, Columbus Laboratories. 6 l. m. sweet, a. w. case, n. r. corby, and n. r. kuchar, ‘Closed-loop joint tracking, puddle centering and weld process control using an integrated weld torch vision system’, in Proc. Control of Manufacturing Processes and Robotic Systems, ASME, New York, 1983. 7 j. doherty, s. j. holder, and r. baker, ‘Computerized guidance and process control’, in Proc. Third International Conference on Robot Vision and Sensory Control, Cambridge, MA, 1983. 8 ANON., ‘Optical welding torch’, NASA Tech Briefs, pp. 68–69, March 1987. 9 r. w. richardson, a. gutow, r. a. anderson, and d. f. farson, ‘Coaxial weld pool viewing for process monitoring and control’, Welding Journal, 63, No. 3, 43–50, 1984. 10 s. s. gordon, l. a. flanigan, and g. e. dyer, ‘Development of a CCTV system for welder training and monitoring of space shuttle main engine welds’, Welding Journal, 66, No. 3, 47–54, 1987. 11 w. a. wall and d. l. stephens, ‘Automatic closed circuit television electrode guidance for welding’, Welding Journal, 48, No. 9, 713–720, 1969. 12 r. niepold and f. bruemmer, ‘Optical sensor system controls arc welding process’, in Proc. Second International Conference on Robot Vision and Sensory Controls, London, 1982. 13 m. a. burke, h. b. james, and r. n. wells, ‘The robotic adaptive welding system: raws’, in Proc. First International Conference on Advanced Welding Systems, London, 1985. 14 y. arata and k. inoue, ‘Automatic control of arc welding: optical sensing of joint configuration’, Trans. Japanese Welding Research Institute, 3, 87–101, 1973. 15 p. g. davey, j. w. barratt, and a. r. vidler, ‘Laser sensors for arc welding robots’, in Robotic Welding, ed. J. D. Lane, IFS Publications, London, 1987. 16 r. vires, ‘A laser-based vision system for arc welding automation’, in Proc. Conference on Advances in Automation for Precision Arc Welding, Edison Welding Institute, Columbus, Ohio, 2–3 December 1987. 17 j. bolstad, ‘Stroboscopic machine vision techniques for arc welding’, in Proc. Conference on Advances in Automation for Precision Arc Welding, Edison Welding Institute, Columbus, Ohio, 2–3 December 1987. 18 h. r. castner, r. w. richardson, j. g. buck, and j. o. bolstad, ‘Strobe laser enhanced coaxial vision system’, Abstract of Papers, 69th American Welding Society Annual Meeting, New Orleans, 1988. 19 a. r. vorman and h. brandt, ‘Feedback control of GTA welding using puddle width measurement’, Welding Journal, 55, No. 9, 742–749, 1976. 20 r. j. renwick and r. w. richardson, ‘Experimental investigation of GTAW weld pool oscillations’, Welding Journal, 62, No. 2, 29S–35S, 1983. 21 e. p. vilkas, ‘Automation of gas tungsten arc welding process’, Welding Journal, 45, No. 5, 410–416, 1966. 22 g. a. vorontsov, ‘Automatic in-process control of penetration depth in electric plug welding’, Automatic Welding, 23, No. 2, 28–31, 1970.

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23 y. h. xiao and g. den ouden, ‘A study of GTA weld pool oscillation’, Welding Journal, 69, No. 8, 289S–293S, 1972. 24 d. j. kotecki, d. l. cheever, and d. g. howden, ‘Mechanism of ripple formation during welding solidification’, Welding Journal, 51, No. 8, 386S–391S, 1972. 25 m. zackenhouse and d. e. hardt, ‘Weld pool impendence identification for size and control’, Journal of Dynamic Systems Measurement and Control, 105, 179–184. 26 r. j. salter and r. t. deam, ‘A practical front face penetration control system for TIG welding’, in Developments in Automated and Robotic Welding, ed. D. N. Waller, The Welding Institute, Cambridge, pp. 38.1–38.12, 1987. 27 r. t. deam, ‘Weld pool frequency: a new way to define a weld procedure’, in Proc. 2nd International Conference on Trends in Welding Research, Gaitlinbourgh, TN, ASM International, 14–18 May 1989. 28 f. j. king and p. hirsch, ‘Seam tracking systems with the arc as sensor’, in Proc. of Conference on Advances in Welding Processes, The Welding Institute, Cambridge, England, 1978. 29 g. e. cook, ‘Through-the-arc sensing for arc welding’, in Proc. Tenth Conference on Production Research and Technology, National Science Foundation, pp. 141– 151, 1983. 30 l. a. lott, ‘Ultrasonic detection of molten/solid interfaces in weld pool’, Materials Evaluation, 42, No. 3, 337–341, 1984. 31 d. e. hardt and j. m. katz, ‘Ultrasonic measurement of weld penetration’, Welding Journal, 63, No. 9, 273S–281S, 1984. 32 l. a. lott, j. a. johnson, and h. b. smartt, ‘Real-time ultrasonic sensing of arc welding processes’, in Proc. 1983 Symposium on Nondestructive Evaluation Applications and Materials Processing, Metals Park, OH, ASM International, pp. 13–22, 1983. 33 n. m. carlson and j. a. johnson, ‘Ultrasonic sensing of weld pool penetration’, Welding Journal, 67, No. 11, 239S–246S, 1988. 34 r. fenn, ‘Ultrasonic monitoring and control during arc welding’, Welding Journal, 64, No. 9, 18–22, 1985. 35 g. e. cook, k. anderson, and r. j. barrett, ‘Feedback and adaptive control in welding’, in Recent Trends in Welding Science and Technology, eds S. A. David and J. M. Vitek, ASM International, Cleveland, OH, 891–903, 1989. 36 j. hanwright, ‘Robotic arc welding under adaptive control: A survey of current technology’, Welding Journal, 65, No. 11, 19–24, 1985. 37 r. w. richardson, ‘Seam tracking sensors: improving all the time’, Welding Design and Fabrication, 77–82, September 1982. 38 j. m. barry, z. paley, and c. m. adams jr, ‘Heat conduction from moving arc in welding’, Welding Journal, 42, No. 3, 97S–104S, 1963. 39 k. e. dorschu, ‘Control of cooling rate in steel weld metal’, Welding Journal, 50, No. 11, 49S–62S, 1968. 40 e. kannatey-asibu jr, n. kikuchi, and a. r. jallard, ‘Experimental finite element analysis of temperature distribution during arc welding’, Journal of Engineering Materials and Technology, 111, 9–18, 1989. 41 p. w. ramsey, j. j. chyle, j. n. kuhr, p. s. meyers, m. weiss, and w. groth, ‘Infrared temperature sensing systems for automatic fusion welding’, Welding Journal, 42, No. 8, 337s–346s, 1963.

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42 c. j. smith, ‘Self-adaptive control of penetration in a tungsten inert gas weld’, in Advances in Welding Processes, ed. J. C. Needham, The Welding Institute, Cambridge, 272–277, 1974. 43 d. farson, r. richardson, and x. li, ‘Infrared measurement of base metal temperature in gas tungsten arc welding’, Welding Journal, 77, 396s–401s, 1998. 44 j. b. song and d. e. hardt, ‘Estimation of weld bead depth for in-process control’, in Automation of Manufacturing Processes, eds. K. Danai and S. Malkin, ASME, New York, 39–45, 1990. 45 h. c. wikle, s. kottilingam, r. h. zee, and b. a. chin, ‘Infrared sensing techniques for penetration depth control of the submerged arc welding process’, Journal of Materials Processing Technology, 113, 228–233, 2001. 46 s. baik, m. kim, s. park, c. chung, c. kim, and k. kim, ‘Process monitoring of laser welding using chromatic filtering of thermal radiation’, Measurement Science and Technology, 11, 1772–1777, 2000. 47 b. a. chin, n. h. madsen, and j. s. goodling, ‘Infrared thermography for sensing the arc welding process’, Welding Journal, 63, 227–234, 1983. 48 m. a. khan, n. h. madsen, j. s. goodling, and b. a. chin, ‘Infrared thermography as a control for the welding process’, Optical Engineering, 25, 799–805, 1986. 49 s. nagarajan, w. h. chen, and b. a. chin, ‘Infrared sensing for adaptive arc welding’, Welding Journal, 68, No. 11, 462S–466S, 1989. 50 w. h. chen and b. a. chin, ‘Monitoring joint penetration using infrared sensing techniques’, Welding Journal, 69, 181s–185s, 1990. 51 s. nagarajan, p. banerjee, w. chen, and b. a. chin, ‘Control of the welding process using infrared sensors’, IEEE Transactions on Robotics and Automation, 8, 86– 93, 1992. 52 y. wang and b. a. chin, ‘On line sensing of weld penetration using infrared thermography’, in Optical Techniques for Industrial Inspection, ed. P. G. Cielo, Proc. SPIE 665, SPIE, Bellingham, WA, 314–320, 1986. 53 p. banerjee, s. govardhan, h. c. wikle, j. y. liu, and b. a. chin, ‘Infrared sensing for on-line weld process monitoring and control’, in Manufacturing Science and Engineering, ed. K. F. Ehmann, ASME, New York, 907–918, 1993. 54 s. govardhan, Ph.D. Dissertation, Auburn University, Auburn, AL, 1996. 55 e. r. bangs, ‘Infrared signature analysis: real time monitoring of manufacturing processes’, in Thermosense X: An International Conference on Thermal Infrared Sensing for Diagnostics and Control, ed. R. D. Lucier, Proc. SPIE 934, SPIE, 111–119, 1988. 56 b. e. bates and d. e. hardt, ‘A real-time calibrated thermal model for closed-loop weld bead geometry control’, ASME Journal of Dynamic Systems, Measurement, and Control, 107, 25–33, 1985. 57 c. c. doumanidis and d. e. hardt, ‘A model for in-process control of thermal properties during welding’, ASME Journal of Dynamic Systems, Measurement, and Control, 111, 40–50, 1989. 58 c. c. doumanidis and d. e. hardt, ‘Simultaneous in-process control of heataffected zone and cooling rate during arc welding’, Welding Journal, 69, No. 5, 186S–196S, 1990. 59 c. c. doumanidis and d. e. hardt, ‘Multivariable adaptive control of thermal properties during welding’, ASME Journal of Dynamic Systems, Measurement, and Control, 113, 82–92, 1991.

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60 a. e. bentley and s. j. marburger, ‘Arc welding penetration control using quantitative feedback theory’, Welding Journal, 71, 397s–405s, 1992. 61 e. a. fuchs, l. a. bertram, a. e. bentley, and s. j. marburger, ‘Evaluation of feedback parameters for weld process control’, in Thermosense XIV: An International Conference on Thermal Sensing and Imaging Diagnostic Applications, ed. J. Eklund, Proc. SPIE 1682, SPIE, Bellingham, WA, 155–159, 1992. 62 m. menaka, m. vasudevan, b. venkatraman, and b. raj, ‘Estimating bead width and depth of penetration during welding by infrared thermal imaging’, Journal of the British Institute of NDT, 47, 564–568, 2005. 63 b. venkatraman, m. menaka, m. vasudevan, and b. raj, ‘Thermography for online detection of incomplete penetration depth estimation’, in Proceedings of 12th A-PCNDT 2006 – Asia-Pacific Conference on NDT, 2006. 64 g. rider, ‘Control of weld pool size and position for automatic and robotic welding’, in Proc. 3rd International Conference on Robot Vision and Sensory Controls, Cambridge, MA, November, 1983. 65 g. rider, ‘Measurement of weld pool size by self scanned photodiode arrays’, in Proc. International Conference on Low Light and Thermal Imaging Systems, 3–5 March, 1975, IEE, London. 66 s. nagarajan, p. banerjee, and b. a. chin, ‘Thermal imagingfor weld quality control in arc welding processes’, Transport Phenomena in Materials Processing, Vol. 146, ASME, New York, 171–178, 1990. 67 j. p. boillot, p. ceilo, g. begin, c. michel, m. lessard, p. fafard, and d. villemure, ‘Adaptive welding by fiber optic thermo graphic sensing: An analysis of thermal and instrumental considerations’, Welding Journal, 64, No. 7, 209S–217S, 1985. 68 n. d. malmuth, w. f. hall, b. i. davis, and c. d. rosen, ‘Transient thermal phenomena and weld geometry in GTAW’, Welding Journal, 53, No. 9, 388S–400S, 1974. 69 p. boughton, g. rider, and c. j. smith, ‘Feedback control of weld penetration’, Welding Institute Conference Proceedings: Advances in Welding Process, pp. 203–215, 1978. 70 w. h. chen, s. nagarajan, and b. a. chin, ‘Weld penetration sensing and control’, Proc. SPIE, Infrared Technology XIV, Vol. 972, pp. 268–272, 1988. 71 r. e. sundell, l. harris, d. w. walsh, h. solomon, s. correa, and w. f. savage, Minor Element Effects on Gas Tungsten Arc Weld Penetration, General Electric Report No. 86SRD013, pp. 3–1 to 4–24, 1986. 72 w. f. savage, e. f. nippes, and g. m. goodwin, ‘Effect of minor elements on fusion zone dimensions of Inconel 600’, Welding Journal, 56, No. 4, 126s–132s, 1977. 73 c. r. heiple and j. r. roper, ‘Mechanism for minor element effect on GTA fusion zone geometry’, Welding Journal, 61, No. 4, 97s–102s, 1982. 74 p. banerjee, s. govardhan, h. c. wikle, j. y. liu, and b. a. chin, ‘Infrared sensing for on-line weld geometry monitoring and control’, Journal of Engineering for Industry, Transactions of the ASME, 117, No. 3, 323–330, 1995. 75 k. n. groom, s. nagarajan, and b. a. chin, ‘Automatic single V-groove welding utilizing infrared images for error detection and correction’, Welding Journal, 69, No. 12, 441S–445S, 1990. 76 s. nagarajan and b. a. chin, ‘On-line identification and control of part preparation and fixturing errors’, Proc. 1991 ASME Winter Annual Meeting, Atlanta, GA, 1993. 77 w. e. lukens and r. a. morris, ‘Infrared temperature sensing of cooling rates for arc welding control’, Welding Journal, 61, No. 1, 27–33, 1982.

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5 Ultrasonic sensors in welding J. SHAO and Y. YAN, University of Kent, UK

5.1

Introduction

Various real-time systems for monitoring welding processes have been developed for maintaining high-quality products at high production rates. Ultrasonic techniques are one of the options that have been developed to replace radiography in detecting buried defects in welds because of the safety hazards associated with the use of X-ray or gamma-ray radiation. Prior to World War II, sonar, the technique of sending sound waves through water and observing the returning echoes to characterise submerged objects, inspired early ultrasound investigators to explore ways to apply the concept to industry and medical fields. In 1929 and 1935, Sokolov studied the use of ultrasonic waves in detecting metal objects. Mulhauser, in 1931, obtained a patent for using ultrasonic waves, using two transducers to detect flaws in solids. Firestone (1940) developed pulsed ultrasonic testing using a pulse-echo technique. With the advances of electronics and ultrasonic sensors, ultrasonic sensing was applied to non-destructive testing during welding processes in the 1960s. After developments over several decades, ultrasonics has become a major technique for inspecting a variety of manufactured components including welded parts. Ultrasonic sensors are used to convert electrical pulses/other forms of energy into mechanical vibrations or vice versa. On the basis of different principles, capacitive, piezoelectric, electromagnetic acoustic transducer (EMAT), ultrasonic phased-array and laser ultrasonic sensors have been developed to generate and detect ultrasound. One or a combination of two types of the above sensors have been used to inspect welding processes. Among the different principles, piezoelectric and EMAT are most common. Ultrasonic phased-array and laser ultrasonic are also becoming widespread. The descriptions of these four types of sensors and their applications will be given in this chapter. For the applications to the real-time monitoring of welding processes, ultrasonic sensors have been used in two distinct ways, i.e. acoustic emission 104 WPNL2204

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(AE) testing and ultrasonic testing (UT). AE testing is normally in the ultrasonic regime, typically within the range between 100 kHz and several megahertz. AE is a passive, receptive technique analysing the ultrasound pulses emitted from welding processes. In contrast, UT actively probes the structure. It measures the response to an artificial and repeatable acoustic excitation of the welding zone. The commonly used UT techniques are pulse echo and time-of-flight (TOF). For the pulse echo technique, sound is introduced into the welding zone and reflections (echoes) are returned to a receiver with welding process information. TOF monitors the welding process using the signals diffracted or attenuated from the welding zone. Normally, two angled compression wave probes are used in a transmit– receive mode, one each side of the welding zone. The purpose of this chapter is to review the ultrasonic sensing techniques developed for real-time monitoring of welding processes. It begins with a brief description of the ultrasonic sensing principle including general information of ultrasound and ultrasonic sensors before moving on to the ultrasonic techniques, i.e. AE and UT, and their applications to real-time monitoring of welding processes. The advantages and limitations of each technique are also discussed in addition to a summary on ultrasonic sensors for welding process monitoring.

5.2

Principles of ultrasonic sensing

5.2.1 Sound wave and propagation Sound waves are organised mechanical vibrations travelling through a medium, which exist only in a solid, a liquid or gas but not in a vacuum. These waves travel through a given medium at a specific speed in a predictable direction. When the waves encounter a boundary with a different medium, they will be reflected or refracted. All sound waves oscillate at a specific frequency. The human ear can hear sound waves that have a frequency of 20 Hz to 20 kHz. Ultrasound refers to waves that have a frequency higher than 20 kHz and are therefore outside our hearing range. The majority of ultrasonic sensing applications utilise frequencies between 100 kHz and 10 MHz. At this frequency range, a sound wave does not travel efficiently through air or other gases, but it travels freely through most liquids and common engineering materials. Sound waves can be characterised in space by oscillatory patterns that are capable of maintaining their shape and propagating in a stable manner. In solids, molecules can support vibrations in different directions, and therefore a number of different types of sound waves are possible. Compression waves and transverse waves are the most common modes employed in ultrasonic testing. Rayleigh and Lamb waves are also used on certain

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occasions. A compression wave is also known as a longitudinal wave and is characterised by particle motion in the same direction as wave propagation in the same plane. Audible sound exists as longitudinal waves. A transverse or shear wave is characterised by particle motion perpendicular to the direction of wave propagation. A surface or Rayleigh wave has an elliptical particle motion and it travels across the surface of a material, penetrating to a depth of approximately one wavelength. A Rayleigh wave is useful because it is very sensitive to surface defects and it follows the surface around curves. Because of this, a Rayleigh wave can be used to inspect areas that other waves might have difficulty in reaching. A Lamb or plate wave is a complex mode of vibration in thin plates where material thickness is less than one wavelength and the wave fills the entire cross-section of the medium.

5.2.2 Ultrasonic sensors The conversion of electrical pulses to mechanical vibrations and the conversion of returned mechanical vibrations back into electrical energy is the basis for ultrasonic sensing. In this section, a brief description of piezoelectric, EMAT, ultrasonic phased-array and laser ultrasonic sensors will be given. Piezoelectric sensor The active element of a piezoelectric sensor is the heart of the sensor as it converts the electrical energy to acoustic energy, and vice versa. The active element is basically a piece of polarised material with electrodes attached to two of its opposite faces. When an electric field is applied across the material, the polarised molecules will align themselves with the electric field, resulting in induced dipoles within the molecular or crystal structure of the material. This alignment of molecules will cause the material to change dimensions. In addition, a permanently polarised material such as quartz (SiO2) or barium titanate (BaTiO3) will produce an electric field when the material changes dimensions as a result of an imposed mechanical force. This phenomenon is known as the piezoelectric effect. The active elements of most acoustic transducers used today are ceramics such as lead zirconate titanate (PZT). Crystalline quartz, one of the first piezoelectric materials to be discovered, is still used for transducer construction. Above approximately 300 °C high-temperature materials such as lead metaniobate and lithium niobate are employed. More recently, piezoelectric polymers such as polyvinylidene fluoride (PVDF) have been discovered. They are highly sensitive, flexible, rugged, light weight and have low impedance and high signal bandwidth. These characteristics contrast with more

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Electrical connector Backing material Casing Piezoelectric element

Couplant

Workpiece

5.1 Schematic of a piezoelectric transducer.

conventional ceramic piezoelectric materials which are at least 20 times less sensitive. The main disadvantage of the PVDF sensor is that it is limited to temperature environments less than 100 °C. Figure 5.1 shows the schematic of a typical piezoelectric transducer. A piezoelectric sensor is robust and more sensitive than other sensor techniques, such as capacitive, electromagnetic and laser-optical sensors. However, there must be some medium to transmit/couple the ultrasound from the piezoelectric transducer to the workpiece and vice versa. A piezoelectric sensor must be in contact with the workpiece. This quite often causes difficulties in practice due to the hostile environment of the weld zone, and possibly, an inadequate coupling between the sensor and the workpiece. Electromagnetic acoustic transducer (EMAT) An EMAT is a non-contact device that generates and detects ultrasound in electrically conducting or magnetic materials. The EMAT consists of a magnet and a coil of wire and relies on electromagnetic acoustic interaction for elastic wave generation. Using Lorentz forces and magnetostriction, the EMAT and the metal test surface interact and generate an acoustic wave within the material. Figure 5.2 shows the typical configurations of piezoelectric and EMAT transducers for ultrasonic testing. Although EMAT is not an efficient generator of ultrasound, it can be designed and built as an excellent receiver for both longitudinal and shear waves. As EMAT eliminates the need for liquid couplant, it could make inspection more rapid, more practical and less prone to measurement error. It also could be made to operate at elevated temperatures. However, EMAT must be in near-contact with the surface of the workpiece. This limits the application of EMAT only to flat or mildly curved surfaces.

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Real-time weld process monitoring EMAT sensor

Magnet

Couplant

Piezoelectric sensor Lorentz force Eddy currents Ultrasonic wave

Coil circuit

Magnetic field Ultrasonic wave

5.2 Piezoelectric and EMAT transducer for ultrasonic testing.

Ultrasonic phased-arrays Ultrasonic phased-arrays have been widely used in sonar and medical imaging and have also been applied to non-destructive evaluation. With the advances of this technique, the application to real-time welding quality assurance has also been reported (Neuenschwander et al., 2004). The phased-array concept is based on the use of ultrasound probes made up of a number of individual elements that can each function separately as transmitter and receiver. Ultrasonic arrays can be loosely classified as linear (1D), 2D matrix, annular and annular matrix as shown in Fig. 5.3. Compared with standard monolithic sensors, an ultrasonic array is able to undertake a range of different inspections from a single location. It is more flexible than a single element transducer. An array can generate ultrasonic fields of almost infinite variety. Most types of array can be used to produce images at each test location. This allows rapid visualisation of the internal structure of a component (Drinkwater and Wilcox, 2006). Laser ultrasonic Methods for generation and detection of the ultrasound are quite different from the piezoelectric and EMAT sensors. A pulsed laser is often used to generate an ultrasonic wave and a separate continuous wave (CW) laser interferometer is used for detection of the ultrasonic wave (Fig. 5.4). All types of waves including compression, shear, surface and guided waves, can be generated through a thermoelastic process or by ablation when laser strikes on the surface of the workpiece. A wide range of ultrasonic amplitudes can be generated by varying the incident laser power. Within the non-destructive thermoelastic regime, the maximum compression-wave

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(b)

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Pulsed laser

Laser interferometer

5.3 Schematic diagram of typical geometries of phased array: (a) linear; (b) 2D matrix; (c) annular; (d) annular matrix.

Weld pool

Wave generation and propagation

5.4 Schematic of laser ultrasonic for online inspection of a welding process.

amplitude is in the range 1–10 nm at a distance of approximate 25 mm from the source without surface constraint. In the ablation regime, amplitudes up to around 40 dB are realisable, subject to the maximum laser power (Scruby and Drain, 1990). Interferometers for the detection of ultrasonic moments of surfaces often work in two distinct approaches. In the first, light scattered or reflected from

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a surface is made to interfere with a reference beam, thus giving a measure of optical phase and hence instantaneous surface displacement. The second approach is designed as a high-resolution optical spectrometer to detect changes in the frequency of the scattered or reflected light. It thus gives an output dependent on the velocity of the surface. The first approach is widely used and most practical at lower frequencies and with reflecting surfaces. The second one offers a potentially higher sensitivity with rough surfaces, in particular, at high frequencies. Although the methods of generation and detection are very different from contact ultrasonics, the ultrasonic wave interactions inside the workpiece are very similar. The advantages of laser ultrasonic are clear: it is entirely couplant-free, non-contact and remote from the workpiece. It also has a flat spectral response over wide bandwidths. Light beams can be made extremely small to give good access in confined spaces and give high spatial resolution, and can also be scanned readily across the workpiece. The shortfalls of laser ultrasonic are also obvious: it is likely to be more costly and complex to use than other ultrasonic sensors.

5.3

Ultrasonic techniques and their applications

5.3.1 Acoustic emission testing Acoustic emission (AE) is a term describing a phenomenon whereby transient elastic waves are generated by the rapid release of energy from localised sources within a material. It appears that AE is generated by weld plastic deformation, fracture, crack formation and martensitic transformation during or immediately following the welding operation. AE signals are directly related to the weld characteristics. AE can therefore be used for real-time monitoring of welding processes as the signals are generated while the process is undergoing changes. Jolly (1969) investigated crack growth in stainless steel welds using AE testing. AE sensors were mounted parallel to the weld coupon surface by means of a phenolic support block and acoustically coupled with uncured room temperature vulcanisation (RTV) sealant compound. Defects were induced at specific locations along the weld by the addition of small amounts of titanium to the weldment. This study showed that low-level signals and more sizeable bursts of AE during welding processes were related to the growth of microfissures and larger cracks respectively. The result demonstrated that AE testing could detect crack growth in a weld as soon as the weld metal solidifies. Romrell (1973) developed further use of AE for the detection of cracks for monitoring nuclear reactor fuel pin end closure welds. A comparison study of the AE data and the metallurgical microscopy from fuel pin end

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closure welds was conducted. The weld samples that produced high emission rates were found to have several areas in which there were small microcracks. Weld samples that produced low emission had no microcracks. These results showed that AE testing appeared to be very sensitive to detect microcrack formation and could provide valuable non-destructive testing data unobtainable with other NDT methods at that time. Bolotin and Belov (1976) studied weld quality by AE during electronbeam welding. Piezoelectric transducers were clamped against the specimen at a distance from the weld axis such that the temperature in the places of clamping did not exceed 100–150 °C. They analysed pulse rate, amplitude and pulse power of AE signals during welding against known welding defects. They found that the nature of variation of these parameters of AE from defective and defect-free specimens differed markedly. It was concluded that it was possible to obtain a result in the presence of defects in a weld within almost 1 min of welding being completed through AE testing. Vahaviolos et al. (1981) developed an adaptive resistance spot-weld feedback control loop via acoustic emission. They identified the typical AE response signals during resistance spot welding process. Their system showed that AE could fill in the missing link in a control loop including detection, feedback and control with instantaneous analysis. AE monitoring could be automatic and integrated with numerical control. Fang et al. (1995) investigated cold crack formation during gas metal arc welding of steel by examining the frequency response of the AE signal. Results from bead on plate welds showed that AE signals were concentrated in the frequency range below 200 kHz. Reasonable agreement was obtained when compared to the theoretical results. Real-time monitoring of laser welding processes has been investigated by a number of researchers. Figure 5.5 illustrates the typical setups of AE sensing using piezoelectric sensors for laser welding processes. Several applications have been reported since the mid-1970s (Saifi et al., 1976; Jon, 1979; Whittakter et al., 1985). However, they were difficult for mass production due to the lack of reproducibility of the mechanical contact between workpieces and sensors. Jon (1985) and Hamann et al. (1989) subsequently proposed non-contact approaches to overcome the above problem. Jon (1985) put a metal plate between a laser and a workpiece bearing the AE sensor and a hole to transmit the laser power. Hamann et al. (1989) mounted the AE sensor at the housing of the focusing optics. They claimed that the methods could be used to monitor the depth as long as the intensity of laser beam was high enough to form plasma. Steen and Weerasinghe (1986) monitored keyhole formation during laser welding using an acoustic mirror for back-reflection signals. They found there was a slight rise in the acoustic signal as the keyhole began to fail. Based on the study of signals detected using piezoelectric sensors mounted on the back of a beam guide mirror

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Real-time weld process monitoring AE transducer for acoustic mirror Mirror Laser beam

Lens AE transducer for acoustic nozzle

Metal vapour and plasma

AE transducer

5.5 Typical setups of acoustic emission sensing using piezoelectric sensors for laser welding.

(acoustic mirror) during laser welding, Li and Steen (1992), and Li (1998) reported the acoustic emissions detected just above the laser processing nozzle – ‘acoustic nozzle’. They concluded that plasma and vapour ejection and the associated shock waves were possible mechanisms. Li (2002) compared two types of ultrasonic acoustic emission systems, i.e. acoustic mirror and acoustic nozzle, for the laser processing. The results showed that the acoustic mirror signal was generated mainly by the melt pool modulated laser beam back-reflection between 100 and 600 kHz. The signal was strongest on the beam guide mirror closest to the workpiece. When a keyhole was generated, the acoustic mirror signal became weaker, while the acoustic nozzle, principally around 100 kHz, signal became stronger. This indicated that the acoustic nozzle signal was dominated by the vapour/plasma ejection. The relationship between the AE signal and martensite formation was investigated by Liptai et al. (1969) and Speich and Fisher (1972). Their studies showed that martensitic transformations generated AE. The fraction of material transformed was proportional to the emission summation and the emission rate was a measure of transformation kinetics. KannanteyAsibu and Pingsha (1986) reported that the AE signal generated during martensite formation had a strong dependence on carbon content and the root mean square (RMS) signal value was found to depend on the volume

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Mirror

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Welding head

Interferometer

Electrode Weld

Testpiece

5.6 System setup – laser ultrasonic of acoustic emission (Scruby and Drain, 1990).

of martensite formed. Liu and Kannantey-Asibu (1990) published their research on AE monitoring of martensite during arc welding of steel. They claimed that AE testing could monitor the martensite formed from a welding process which radiography and ultrasonic testing failed. Using linear discriminant function analysis, classification results obtained were 96.4% for martensite formation, 70% for porosity and 100% for normal welds. The applications of laser ultrasonic to AE were also reported. Scruby and Drain (1990) mounted a compensated reference-beam interferometer via adjustable jacks on a steel table as shown in Fig. 5.6. A mirror was attached to the end of a boom so that the light beam could be directed vertically downwards onto the surface of the steel plates being welded. For the preliminary trials, it was found that insufficient light was returned from the rough oxidised surface of the specimen plates for adequate sensitivity. To overcome this problem, they attached a small mirror to the surface of the plate. Then, there was sufficient light returned to detect AE signals associated with the formation of weld defects such as slag inclusions. The weld defect emission signals detected by the interferometer correlated well with those detected by the piezoelectric transducer. Scruby and Drain (1990) pointed out that the use of a reflecting device such as mirror would not be possible in practice. However, for their tests the laser used in the interferometer was low-power helium–neon. If the laser were replaced by a more powerful laser, e.g. argon, the sensitivity could be considerably increased. It was also suggested that both the quadrature and long-path interferometers would be better suited for these applications. A number of patents have been filed on AE testing of welding processes. Vahaviolos (1976) described a real-time evaluation of welds by monitoring emitted stress waves. Stress wave emissions (SWE) emitted from the weld area during the solid-to-liquid phase transformation and the liquid-to-solid phase transformation were measured. The stress wave energy emitted

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during the liquid-to-solid phase transformation was subtracted from the stress wave energy emitted during the solid-to-liquid phase transformation and that value was compared with a predetermined value to determine the acceptability of the weld. Vahaviolos (1977) measured SWE energy during the compression phase of an adhesion bond. The value measured was compared with a predetermined substantially linear relationship between the emitted stress wave energy and then the strength of the particular adhesion bond being formed. The techniques filed in the above patents worked successfully in determining the quality or the strength of bonds. However, at times, undesirable noise vibrations were caused by the head of the welding tool or bonder or the like. Such mechanical vibrations resulted in relatively high-amplitude oscillating signals, which lasted for an extended period of time and which were incorrectly interpreted by the stress wave detection equipment as SWE signals, causing incorrect evaluation and attendant loss of acceptable product. Jon et al. (1978) developed an effective method to determine whether a detected signal was a mechanical noise or an SWE signal. Clark et al. (1977) invented a system for flaw detection during welding. The system used AE transducers to detect acoustic signals from the weld and detect flaws using acoustic emission counts. Wehrmeister (1979) patented an apparatus for on-line detection of weld defects by monitoring the acoustic emissions and identifying frequency bands where faults occur. Hein and Oertle (1981) invented an acoustical emitter to monitor the initiation and propagation of cracks in materials. Steen et al. (1987) developed a laser monitoring system using an acoustic mirror to measure the total power in a high-powered laser beam.

5.3.2 Ultrasonic testing Ultrasonic testing (UT) uses ultrasound energy to conduct examinations and make measurements. Ultrasonic inspection has a wide range of applications including welding quality assurance, flaw detection/evaluation, dimensional measurements and material characterisation. This section focuses on the applications of UT on real-time monitoring of welding processes. Ultrasound penetrates with little attenuation through most engineering materials and is reflected at boundaries where the acoustic impedance of the material changes abruptly, e.g. the solid/liquid interface. In steel, the acoustic mismatch at solid/liquid interface in the weld pool is 13–15% for compression (longitudinal) waves at normal incidence. This gives an amplitude reflection coefficient of approximately 0.1 which is easily detectable using modern ultrasonic sensors. Shear (transverse) waves cannot propagate through liquids and therefore would be expected to undergo 100% reflection at the solid/liquid interface. However, in reality due to

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R

Steel

5.7 Typical setup of a UT welding assessment system for root-pass welds; S is pulse echo shear probe, T/R is pulse echo compression probe and R is transmission receiver probe (Stares et al., 1990).

attenuation in the materials substantially less than 100% reflection is observed. Pulse echo and TOF are widely used in welding monitoring. Figure 5.7 illustrates a typical setup of a UT welding assessment system for root-pass welds using pulse echo and TOF. In pulse echo/reflection mode, only one transducer is required to send pulsed waves through the weld zone, and receive the sound reflected back to the device. Reflected ultrasound comes from an interface such as the solid/liquid interface of the weld pool, the back wall of the workpiece or from a defect. In TOF mode, at least two sensors are required. A transmitter sends ultrasound through one surface, and a separate receiver detects the ultrasound with real-time information that has reached it on another surface after travelling through the weld zone. The transit time of the ultrasonic signal includes the time spent in the heated region of the weld pool so that an estimate of the weld pool width can be achieved. By analysing the variations of the transmitted ultrasonic density, the defects of the weld such as inclusion, porosity and lack of sidewall fusion can be detected and identified. Fenn (1985) proposed an ultrasonic method for the simultaneous controlling weld joint penetration and joint tracking during arc welding. He reported that overall beam path length accuracy could be achieved at ±1 mm using a high-performance crystal at 5 MHz. For weld depth measurement, two shear probes were placed on the upper surface of the workpiece either symmetrically or asymmetrically about the electrode centre line as shown in Fig. 5.8. It was claimed that the depth of the weld pool and its shape could be monitored and controlled within very small limits. By positioning the ultrasonic probes for weld penetration monitoring, beam spread could be employed for joint tracking as given in Fig. 5.9. Careful selection of ultrasonic frequency, crystal size and approach angle would ensure that the ultrasonic leading edge signal is millimetrically in front of the weld pool and thus achieved a joint tracking signal with minimal delay between the joint and the weld pool. He also pointed out that a trailing edge signal as shown in Fig. 5.9 contained possible weld quality information. Fenn (1989)

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Angled probe

Ultrasonic sensor

Weld pool

5.8 Weld pool depth measurement using two shear wave probes asymmetrically disposed around the electrode centre line (Fenn, 1985).

Ultrasonic angle probe

B A C

Welding direction

5.9 Schematic diagram of the signals generated due to ultrasonic beam spread. A – penetration control signal; B – joint tracking signal; C – possible quality indication signal (Fenn, 1985).

developed a real-time control device following his early proposed method. It was demonstrated that the ultrasonically controlled welding equipment was able to weld both butt and fillet welds successfully. Fenn and Lu (1997) reported a real-time welding quality control and assessment system for gas tungsten arc welding (GTAW) following Fenn’s early work. The system could not only control the weld penetration and follow weld seams but also detect welding defects in real time by a multifunctional ultrasonic sensor at real welding speed. The experimental results showed that all commonly existing defects in welding, such as lack of root fusion, porosity, undercut, blow holes and some hot cracks, were successfully detected through introducing known defects into welds on purpose.

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Welding head

Transmitter

Weld spot Receiver

5.10 Ultrasonic sensor arrangements for spot welding monitoring (Rokhlin et al., 1985).

Rokhlin et al. (1985) introduced an ultrasonic method for prediction of spot weld strength. The method was based on real-time weld monitoring by measuring ultrasonic Lamb (or plate) wave signals transmitted through the weld region as illustrated in Fig. 5.10. The transmitted signal was affected by temperature changes in the welded region during solidification and cooling of the melted pool. It was proposed that the minimum of the transmitted signal corresponded to the austenite-to-pearlite transformation, which corresponded to the strong maximum in the attenuation of the ultrasonic signal. The time from weld current termination to this minimum depended on the heat capacity of the welding pool and was therefore related to the nugget size and the weld strength. The experimental results showed that there was good correlation between this time period and the shear strength of the welded specimens. Therefore, cold welds, which formed good acoustical contact between the welded plates but weak bonds, could be revealed. Carlson and Johnson (1986, 1988) investigated ultrasonic sensing of weld pool penetration. They revealed some relationships between A-scan signals and the flawed and good weld. An expert system also was developed to discriminate weld pool geometry with 71% accuracy. Stares et al. (1990) investigated the feasibility of ultrasonic means for real-time defect detection. Compression waves were used to interrogate the molten weld pool either in transmission mode or pulse echo mode. Shear waves were also applied to the joint as a comparative study. During welding trials, the transducers moved with the welding head to act as an on-line weld monitor and ultrasonic coupling was achieved with a water irrigation system. The results showed that the lateral width of the weld pool could be measured with ±0.8 mm accuracy together with any changes in its dimensions due to

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external disturbances by means of transmitted compression waves. Pulse echo ultrasound was less sensitive to changes in weld pool size. Transmitted compression waves had clear advantages over pulse echo compression waves and pulse echo shear waves for detecting and distinguishing the lack of side-wall fusion, inclusions and porosity. Bull et al. (1993) also concluded that both tungsten inert gas (TIG) and resistance spot welding could be monitored ultrasonically. Goswami et al. (2003) proved the feasibility of the ultrasonic waves for on-line monitoring of laser welding processes. Sutter (2004) reported a commercial in-process ultrasonic inspection system (PSQ 6000 system, from Bosch Rexroth) for resistance spot welding. The resistance spot welding gun was equipped with an ultrasonic transducer. The system sent ultrasonic pulses from a piezoelectric ‘sending element’ to a ‘receiving element’ during the welding process. The system could send up to 100 ultrasonic pulses during a single weld. After passing through the workpiece, the received sound wave was plotted on a graph by the ultrasonic software. By comparing a set of ultrasonic signature curves from good welds to a new weld, the quality of the new weld could be determined. It is claimed that the system could detect bad welds with 100% success rate. However, the signature curves were unique for different metals, different stackups, different shapes of metal and different coatings on metals, etc., and therefore, the system had to be set with different programs for each unique condition. Recent developments in phased array ultrasonic technique offer great potential for radical improvements in non-destruction inspection performance and economy (Jones et al., 2000; Gros et al., 2002; Drinkwater and Wilcox, 2006; Messer et al., 2006; Raj et al., 2006). The great advantage of this technique is the use of multi-element transducers in conjunction with high-speed electronics, which allows a fast steering and scanning of the ultrasonic beam without any mechanical movement. Neuenschwander et al. (2002, 2004) successfully conducted quality monitoring during the electron beam (EB) welding of the compact muon solenoid (CMS) conductor using ultrasonic phased array inspection. The inspection unit was placed directly after the EB welding unit. A 128-channel phased-array system by R/D Tech (F-Villebon sur Yvette) together with two multi-element probes by Imasonic (F-Besancon) was used in this application. The probes had a nominal frequency of 11 MHz and contained 64 piezoelectric elements with a pitch of 0.65 mm and a mechanical focal distance of 40 mm in water. The conductor passed through a water-filled chamber which contained the ultrasonic probes. At the end of the inspection unit the conductor was dried with pressurised air. During the inspection, a two-dimensional image is generated (C-scan). Welding flaws were immediately detected and visualised during conductor production. They reported that a total of over 45 km of CMS conductor had been successfully welded and inspected.

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Conventional piezoelectric transducers were used in the UT system for the welding process monitoring presented above. Some systems developed could inspect specific welds in real time and yield useful results. However, there are two major disadvantages over the contact sensors for this kind of application. Firstly, the temperatures experienced by the sensors are likely to be too high for normal operation, unless they are placed so far away from the hot material. Thus, they can be insufficiently sensitive. Secondly, they are not readily scanned across a hot metal surface without creating concern for the maintenance of adequate coupling between specimen and transducer. Non-contact sensors which can scan the workpiece in synchronisation with the weld head are clearly desirable. Therefore, EMAT, laser optical sensor and their combinations (i.e. ultrasound generation by a pulsed laser and reception by an EMAT) have attracted a lot of research efforts. The EMAT consists of a wire conductor and a strong magnetic field source and is able to send and receive ultrasound emissions from a surface in close proximity. Its characteristics can easily be changed by simply interchanging the coil geometry or the magnetic field direction. By sending an ultrasonic wave through the workpiece, EMAT can detect macroscopic defects. Fortunko and Schramm (1982) used an EMAT to detect weld defects during butt welding. Ultrasonic probing signals that were polarised horizontally with respect to the surface of the weldment were used. The results showed that incomplete fusion, inadequate penetration and crater shrink were detectable during shielded metal arc welding. Oursler and Wagner (1995) successfully detected porosity and cracks in the weld state for pulsed laser welding. Hogberg (1993) also used EMAT detectors for testing dissimilar joints. Laser ultrasonics (Scruby and Drain, 1990; Monchalin, 1993) can, in principle, solve the problems associated with contact transducers by remote (large stand-off distance) generation and reception of the ultrasound without physical contact with the workpiece, as shown in Fig. 5.4. A laser ultrasonic system typically consists of four main components: a laser ultrasonic transmitter used to remotely excite ultrasonic waves in the workpiece under inspection; a laser ultrasonic receiver used to remotely sense the ultrasonic disturbances at the surface of the workpiece; a transporter unit used to manipulate either the workpiece or equivalently the laser beams in order to select the region under inspection and a computer unit used to automatically control the part transporter, acquire the process data, interpret and provide the results. Obviously, the first two components are our focus. For ultrasound generation, White (1963) discovered that laser could be used to produce ultrasound. Scruby et al. (1980) investigated lasergenerated ultrasound in metals. Aussel et al. (1988) studied both thermoelastic and ablative laser generation. Jarzynski and Berthelot (1989) used fiber optics for spatial and temporal control for beam steering. Ing et al.

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(1989) used a Bragg cell for beam steering of laser ultrasound and in 1991 investigated ultrasonic directivity patterns of a moving source. In most of the laser ultrasonic receivers (Scruby and Drain, 1990; Wagner, 1990; Monchalin, 1993), the surface displacement of the workpiece is transformed by an interferometer and detectors into a voltage signal using the change in phase or frequency of the probe laser beam reflected from the workpiece. However, it was difficult to achieve their potential in the manufacturing environment because of two problems. Firstly, low-frequency large-amplitude phase fluctuations created by ambient vibrations can bias conventional interferometric receivers out of quadrature, reducing their sensitivity. Secondly, multiple speckles produced when laser beams reflect from rough-machined surfaces can wash out the ultrasonic signal or significantly reduce the signal-to-noise ratio through phase mixing. To solve these problems, Dunning et al. (1996) developed a much complex system which utilised wave-front scrubbing and heterodyne detection to compensate for optical distortions. They incorporated the laser receiver (CLUETM laser receiver) into a laser-based ultrasonic inspection system to determine the fusion widths of production lap-weld. Klein and Bodenhamer (2004) also applied the laser ultrasonics for in-process monitoring of laser weld integrity. Another solution is to replace the problematic laser receiver with an EMAT and form a laser-EMAT hybrid ultrasonic system. Carlson and Johnson (1988, 1992) in Idaho National Engineering Laboratory (INEL) developed several ultrasonic sensing techniques to determine weld quality with non-contact ultrasound generation and receiver techniques (laserEMAT). Oursler and Wagner (1995) inspect a steel-plate weld specimen for cracks using a multiple cavity pulsed laser/meander line EMAT system offline. Dixon et al. (1999) used a hybrid laser–EMAT system to detect weld defects using the B-scan technique. However, this system was unable to detect all defect types or sizes. The main reasons for this are due to the low sensitivity of the EMAT and low strength of ultrasound generated by a single laser beam. Carlson and Johnson (1992) generated the ultrasound in the ablation regime by Q-switching the laser to meet the requirement of low-sensitivity EMAT. However, this created a new set of problems. Ablation caused surface damage, making the system destructive rather than non-destructive. Beginning in 1990, Ume and his group investigated directional laser generation of ultrasound with optical fibre arrays (Umeagukwu et al., 1991; Yang et al., 1993). Using principles of constructive wave interference, optical fibre array could generate stronger ultrasound than single laser source schemes without causing ablation. Fibre arrays can also be used for beam steering, i.e. controlling the direction of generated ultrasound. Yang et al. (1996) used an optical fibre laser phased array to measure solidified weld

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pool penetration depth in butt and V-groove joints. Graham and Ume (1997) reported a system using a laser array generation source and an EMAT receiver. They demonstrated the feasibility of applying this technique to the on-line measurement of weld penetration in the solidified weld region. Graham et al. (2000) further applied the laser array/EMAT ultrasonic system to simulated liquid welds. The results demonstrated the feasibility of using similar equipment for real-time weld quality control. Miller et al. (2002) developed an automated real-time data acquisition system for monitoring and controlling gas-metal arc welding (GMAW) process based on laser–EMAT hybrid system. However, ablation was employed because high-temperature specimens required strong signals to compensate for attenuation within the bulk of the material. Mi and Ume (2006) monitored weld penetration depth in real-time using laser ultrasonic sensing system. A laser phased array was used to generate focused and steered ultrasound. An EMAT was used as a receiver. They claim this system was capable of operating under high-temperature conditions. A signal-processing algorithm based on a cross-correlation technique was developed to estimate the TOF of the ultrasound. The relationship between the TOF and the penetration depth of the weld was established experimentally and analytically. Mi and Ume claimed that the analytical relationship between the TOF and the penetration depth, which was obtained by the ray-tracing algorithm and geometric analysis, agreed well with the experimental measurements. They believed that the calibrated information on the weld penetration depth extracted from the real-time ultrasonic signals could be used as a feedback to control welding parameters. A number of patents have been assigned on UT for real-time welding inspection. Hurlebaus (1973) developed a monitoring system wherein a welding operation could be monitored during the actual welding. This was achieved by the penetration of shear waves into the pieces being welded with the transmitted wave from the transducer being comparable to the size of the weld area. Alers et al. (1981) developed a non-destructive testing system using EMAT detectors for detecting flaws in the welded seam of a pipe. Tsao and Mancuso (1984) proposed a real-time ultrasonic weld inspection method. This ultrasonic weld inspection method comprises transmitting an ultrasonic wave through the molten metal portion of the weld while the weld is being formed and determining if a weld defect is present in the molten metal. If no defect is detected in the molten metal, the welding process continues. However, if a defect is detected in the molten metal, the welding electrodes remain at that position until the defect has dissipated. In this manner, defects are removed from the weld before the weld metal solidifies. Nied (1984) invented an acoustic wave spot welder adaptive control method. The thickness of the molten weld nugget is continuously monitored

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during resistance spot welding, and when proper penetration is achieved the applied current is turned off. An ultrasonic transducer housed in the water-cooled lower welding electrode transmits ultrasonic pulses into the workpiece and receives waves reflected at each solid/solid and liquid/solid interface. The position of the latter is tracked by pulse time discrimination of reflected signals; weld nugget penetration is computed using a ratio of times or a pulse time difference. Cantor et al. (1992) designed an EMAT system to generate ultrasonic shear waves for detecting depths in welding. Geier et al. (1995) developed an automated system designed for non-destruction inspection using EMAT sensors for weld inspection. Davidson et al. (1996) created an apparatus and method for monitoring butt welds. The method used horizontal shear EMAT detectors to transmit and receive signals to detect defects. Pepper et al. (1996) developed a laser-ultrasonic non-destructive, non-contacting inspection system which is provided for on-line and off-line inspection of a workpiece. The system utilises an optical acoustic wave generation and detection system with relatively high spatial resolution. Lee et al. (2006) developed a system for assessing quality of spot welds. An ultrasound transducer was used for probing a spot weld joint. An image reconstructor connected to the ultrasound transducer transforms the electrical signals into numerical data representing an ultrasound image. A neural network connected to the image reconstructor analyses the numerical data and an output system presents information representing the quality of the spot weld joint.

5.4

Future trends

With the advances in ultrasonic sensing, some ultrasonic inspection systems developed are capable of detecting welding defects or monitoring essential welding parameters in real time. A number of aspects of this technique are still under investigation. A brief commentary on likely future trends is given. Phased array technology has been developed rapidly for non-destructive testing due to its advantages of rapid inspection, single-pass scan, lateral scanning, examination of complex shapes and beam focusing. In order to apply this technology to monitor welding processes in real time, some additional efforts still have to be made. Specialised probes have to be designed to travel alongside the welding head and retain complete acoustic contact with the plates being joined. The flexibility of the system would be elevated remarkably if the probes could generate adaptive wavefronts according to the test structure. One of the advantages of phased array is that it offers the ability to obtain many different signals/images from each transducer location. Therefore, the signal analysis and interpretation are more complex.

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A comprehensive strategy to make full use of the massive amount of information related to the welding processes is highly desirable. Piezoelectric sensors have been used to monitor welding in-process and perform well in some cases. However, the piezoelectric transducers need to be mechanically coupled to the test specimen and it is often difficult for use for real-time applications where samples are in fast motion, at high temperature or in harsh environments. Laser ultrasonics is considered to be a promising technique to solve the problems associated with contact transducers. There needs to be further work to improve the sensitivity of laser techniques for measuring ultrasonic fields. In particular, further developments on the laser interferometer systems which can perform satisfactorily on poorly reflecting rough surfaces are needed. Laser phased array is becoming the ad-hoc area for laser generation of ultrasound. It will be useful to design and test laser systems that can generate focused ultrasonic waves with high spatial resolution and defect-detection abilities. There is also a general need to make these laser ultrasonic systems more compact, simpler to operate and less expensive. It is worth pointing out that other sensors, such as optical, audible sound, vision-based sensors and X-ray imaging, have been used for real-time monitoring of welding processes. To fully exploit the advantages of every sensor technology, more and more multi-sensor systems have been investigated. It is believed that sensor fusion techniques in conjunction with latest advances in statistical and artificial intelligence would play an important role in welding monitoring and inspection.

5.5

Summary

This chapter reviewed the developments and applications of ultrasonic techniques for real-time monitoring of welding processes. Every ultrasonic sensor with various configurations is capable of detecting certain states of welding processes. It is noted that every sensor and inspection approach has its advantages and limitations. Piezoelectric sensor is robust and sensitive and has been widely used in ultrasonic non-destructive testing. However, it is difficult to use in hightemperature and hostile welding environments as this sensor needs to be in contact with the workpiece. EMAT eliminates the couplant problem as it is a non-contact device. In addition, EMAT is capable of producing various wave modes. Therefore, EMAT could make real-time welding inspection more rapid and practical. Ultrasonic phased-array has been developed rapidly over the past several years. Phased-array probes are generally manufactured using high-precision cut piezo-ceramics elements. With the advent of laser phased-array, a few applications have been reported. Laser ultrasonics provides a real non-contact and remote access approach

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for real-time welding monitoring although it is normally complex and costly. Acoustic emission testing can be used to inspect welding quality in realtime. In particular, it is able to detect martensite formation which other non-destruction testing methods fail. AE testing can also be used to inspect irregular shape welds. Ultrasonic testing has a wide range of applications to welding quality assurance. There are commercial systems available for real-time weld inspection.

5.6


Some books and major professional bodies regarding welding, non-destruction testing are listed below for further information. Other relevant literature can be found in the reference list. Books Bindal, V, Transducers for Ultrasonic Flaw Detection, Narosa Publishing House, New Delhi, 1999. Blitz, J and Simpson, G, Ultrasonic Methods of Nondestructive Testing, Chapman & Hall, London, 1995. Charlesworth, J and Temple, J, Engineering Applications of Ultrasonic Time-of-flight Diffraction, Research Studies Press, Baldock, 2003. Duley, W, Laser Welding, John Wiley & Sons, Inc., New York, 1999. Scott, I, Basic Acoustic Emission: Nondestructive testing monographs and tracts, Gordon & Breach Science Publishers Ltd, New York, 1990. Scruby, C and Drain, L, Laser Ultrasonics: Techniques and applications, Adam Hilger, New York, 1990. Professional bodies The British Institute of Non-Destructive Testing, http://www.bindt.org/ The Welding Institute (TWI) – Materials Joining Technology, http://www. twi.co.uk/ Welding Research Council, http://www.forengineers.org/wrc/ American Welding Society, http://www.aws.org/w/a/ European Confederation of Organisations for Testing, http://ceoc.com/ The NDT Database & Journal, http://www.ndt.net/

5.7

References

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Part II Monitoring of welding processes

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6 Weld seam monitoring S.-J. NA, Korea Advanced Institute of Science and Technology, Korea

6.1

Introduction

Arc welding is a joining method chosen mainly for assembling large metal structures such as ships, bridges, pipelines, heavy construction machinery, rolling stocks and cars. Because of the harsh environments resulting from the intense heat and fumes and because of the extreme physical load demanded of manual welders for manipulating the welding torch, arc welding is a prime candidate for process automation. Among the varieties of arc welding processes, gas metal arc welding (GMAW) is one of the most frequently used methods, primarily because it is highly suited to a wide range of applications, and also to automation using robots. The use of conventional robots in arc welding is restricted by the difficulty of maintaining accurate fit-up and fixture. Dimensional variations are introduced by pressing tools, fixtures, and thermal distortions during welding. Therefore, it is necessary to introduce an automatic device to observe the actual location and orientation of the welding line and its detailed surface geometry. Many types of sensors have been developed for line following and seam tracking in arc welding, from simple tactile sensors to sophisticated vision sensors (Nomura, 1994). Among various sensors of the contact and non-contact types, some special applications of the laser vision sensor, which is based on optical triangulation, the through-arc sensor using the electric signal in GMAW, and the inductive sensor are discussed in this chapter.

6.2

Methods of weld seam monitoring

For successful closed-loop control in automatic welding, appropriate feedback signals must be obtained, which generate a suitable error signal to correct the welding system. Feedback signals may be obtained by monitoring the normal welding parameters or by employing appropriate sensors. Types of welding sensors can be effectively classified according to the working principles based on each component, as shown in Table 6.1. 131 WPNL2204

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Table 6.1 Classifications of sensors for weld seam monitoring Sensor type

Working principle

Contact

Contact probes Electrode contact

Temperature Non-contact

Temperature Arc phenomena

Laser vision Electromagnetic Sound

6.3

Microswitches, potentiometers, differential transformers Voltage and current for contact detection applied to the tungsten electrode or welding wire Thermocouples, thermistors Photo-thermometers, infrared thermometers Welding current, arc voltage, wire feed rate, number of short circuits, number of peak current anomalies Point sensors, linear sensors, area sensors Induction coil Detection of sound pressure or ultrasonic sound pressure

Through-arc monitoring of weld seams in gas metal arc welding

The arc sensor eliminates the use of separate detectors around the welding torch, and can detect the groove position directly or provide the information on the surface position of molten pool almost in real time. Knowledge of the weld joint geometry relative to the welding torch permits the manipulator arm to maintain the desired tip-to-workpiece distance and the torch to trace the welding line in real time without any other external devices. This is the main reason why the arc sensor is currently the most popular of the various sensors for weld seam monitoring. GMAW systems are often used in self-regulating mode where the power source has flat or constant voltage characteristics, thereby producing a current change in response to the variation of tip-to-workpiece distance. It has been revealed in much previous work that the welding current shows an almost linear relationship with the tip-to-workpiece distance. Armed with a means of measuring the tip-to-workpiece distance such as an arc sensor, the weld joint geometry can be obtained by weaving the welding arc transversely across the line of travel in V-groove butt welding or horizontal fillet welding.

6.3.1 Arc sensor with torch weaving Extensive efforts have been made to model and control the various arc welding systems. The relationship between tip-to-workpiece distance and welding current in GMAW was established by ignoring the variation of arc

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length (Cook, 1983). A mathematical model that is able to predict the welding current change in response to the variation of tip-to-workpiece distance was proposed for GMAW by analyzing the welding arc behavior and by considering the characteristics of welding power source (Kim and Na, 1991a). The current change in GMAW with constant voltage power source was derived by using the equations of voltage across the electrode extension, electrode, extension, and arc voltage as follows: dI − ( κ1 + κ 2 I ) γ 2 I 2 = dt ( κ 2 L + κ 4 + n ) γ 2 I 2 − ( κ1 + κ 2 I ) ( γ 2 I − 2 Aω) A − ( Aω − γ 1I ) κ 2 IA + ( γ 2 λ 2 − λ 1 ) AωI dL [6.1] dt

where I is the current, t is the time, L is the tip-to-workpiece distance, κ1, κ2, κ3 and κ4 are constants related with the arc voltage, n is the slope of power source characteristics, λ1 is a constant equal to the effective resistivity at the end of electrode extension, λ2 is a constant depending upon the room temperature resistivity of wire, A is the cross-sectional area of electrode wire, ω is the wire feed rate, and γ1 and γ2 are constants related with the wire feed rate. From the above equation the change in current was calculated according to the variation of tip-to-workpiece distance, by using the Runge–Kutta method for the case of linearly decreasing and increasing tip-to-workpiece distance. Figure 6.1 shows the experimental and calculated welding current change in response to the variation of tip-to-workpiece distance. The tip-toworkpiece distance was varied from 30 to 20 mm for the upward inclined workpiece and vice versa for the downward one. It was noted that the fluctuation of welding current signal is more severe for the tip-to-workpiece distance of 30 mm than that for 20 mm, probably because 30 mm distance was out of the proper range for the used welding condition. The mean value of current is, however, steadily increasing or decreasing for the upward or downward welding respectively. For the power source with a flat characteristic (n = 0.0 volt/ampere), the variation of the calculated current in response to the change of tip-to-workpiece distance is larger than that for n = 0.02 V/A, which is a typical slope of GMAW power source. By comparing the calculated and measured curves, it could be seen that the calculated welding current signals coincide well with the experimental ones, especially in the case of n = 0.02 V/A. In the case of downward welding, the experimental welding current signals show a tendency to start decreasing before the starting point of inclination. This might be due to the fact that molten metal flows downward before the electrode tip reaches the inclination point. The difference between the calculated and experimental current value is also greater than that of the upward welding, because this molten metal flow affects the current signal after inclination. The calculated and measured

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Real-time weld process monitoring 350 Measured Calculated (n = 0.00) Calculated (n = 0.02)

Welding current (A)

330

310

290

270

250

230 0

1

2

3

4

Time (s) (a) 320 310

Measured Calculated (n = 0.00) Calculated (n = 0.02)

Welding current (A)

300 290 280 270 260 250 240 230 0

1

2

3

4

Time (s) (b)

6.1 Measured and calculated welding current signals (Kim and Na, 1991a): (a) current signals in upward welding; (b) current signals in downward welding.

current signals showed an almost linear variation for the linear change of tip-to-workpiece distance and the slope of the volt–ampere curve of GMAW power sources had a considerable influence on the welding current signals.

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320

Welding current (A)

V = 31 V w = 8.1 m/min 280

Model Experimental data

240

200

160 10.0

V = 29 V w = 7.1 m/min

15.0

V = 27 V w = 6.1 m/min

20.0

25.0

30.0

Tip-to-workpiece distance (mm)

6.2 Relationship between welding current and tip-to-workpiece distance (Kim and Na, 1991b).

Figure 6.2 shows the experimental linear relationship between arc current and tip-to-workpiece distance in GMAW with a mixed shielding gas of 80% Ar and 20% CO2 for a wide operating range of welding voltage and wire feed rate (Kim and Na, 1991b). The linearized model, which is able to determine the relationship of welding current to tip-to-workpiece distance, was established for an arbitrary welding voltage and wire feed speed by using the 23 factorial experiments and least-square method. The current in GMAW can be expressed as a linear equation of tip-to-workpiece distance, the constants of which are the functions of welding voltage and wire feed speed. It could be shown that the variation of welding current becomes large as the welding voltage and wire feed speed are increased. Using the simulation and experimental model, the position information of the welding torch can be obtained from measuring the welding current and vice versa. This information permits the manipulator arm to maintain the desired tip-to-workpiece distance and the torch to trace the welding line, when a predetermined torch height produces a control signal to turn the weaving direction of the welding torch. Another method of seam tracking is to control the torch position along the center of weld joints, by comparing the current values at the two ends of weaving motion with a specific width.

6.3.2 Arc sensor with rotating arc Arc sensors have been successfully employed in automation of GMAW for about 30 years. The arc sensor system is inexpensive and free from tracking

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6.3 Photograph of rotating arc torch (Kim and Na, 2001b).

errors since the measuring point and joining location coincide with each other. The conventional arc sensor systems with torch weaving motion, however, have a relatively poor resolution, because process variations impinge directly on the signal quality and lead to erroneous information. Moreover the weaving speed is strictly limited by the inertia mass of automatic welding systems. Arc rotation mechanisms which rotate the electrode nozzle by an external motor have been developed to improve the arc sensor sensitivity and welding quality (Nomura et al., 1982; Dilthey et al., 1996). To improve the accessibility of joints and adaptability to the conventional welding torch system, a new rotation mechanism using a hollow shaft motor was developed and analyzed (Kim and Na, 2001b), Fig. 6.3. Figure 6.4 shows the definition of arc positions and the arc images taken by a high-speed camera at corresponding arc positions for a rotating frequency of 20 Hz. The arc length seems to be shorter at Cr than at Cf, because the molten pool is previously formed at the position of Cr. In rotating arc welding, therefore, the tip-to-workpiece distance is considerably affected by the weld pool shape, especially for the rotation period along the rear half circle, where the arc moves above the previously formed pool. Analyses of dynamic three-dimensional GMAW pools are regarded as of major importance to accurately predict the tip-to-workpiece distance, but are highly sophisticated and time consuming. Kim and Na (2001b) adopted a simplified 3D quasi-steady conduction model of molten pool under the assumption of very rapid arc rotation, no convective heat flow in molten pool, constant physical properties except thermal conductivity, specific heat and surface tension, and Gaussian shape of heat flux and pressure distribution of welding arc.

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Welding direction

L Cr

Cf R

Weld line (a) R

Cf

L

Cr

(b)

6.4 Arc shapes at various positions in GMAW with rotating torch (Kim and Na, 2001b): (a) position of welding arc; (b) arc images taken by high-speed camera.

GMAW with rotating arc was modeled by considering the power source with constant voltage characteristics and the dynamic wire melting phenomenon which is inevitable in rotating arc welding, where self-regulation of arc length is not fully performed (Kim and Na, 2001a). Consequently the welding current and electrode vary rapidly in a high-speed arc rotating operation. The wire melting phenomenon was dynamically simulated using the variable space network method and by modeling the heat flux from the molten end of wire into the electrode. It was found that the arc sensor sensitivity depends strongly on the internal resistance and inductance of welding power sources. At low rotation frequencies, the arc sensor sensitivity is affected largely by internal resistance, while it is affected mainly by internal inductance at high frequencies. Low internal resistance and inductance are generally recommended to improve the sensitivity of arc sensors, but may have to be selected by considering the metal transfer mode and arc stability at the same time. The welding current was supplied by an inverter-type power source with constant voltage characteristics and was measured by using a Hall sensor. For detecting the arc position, a proximity sensor was used which generated

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the output signals at every L position of the arc. Typical experimental and simulated results of welding current in V-groove gas metal arc welding are shown in Fig. 6.5 for a rotation speed of 25 Hz. Although the experimental welding current signals contain some high-frequency fluctuations, it can be concluded that their basic waveforms can be predicted reasonably well by simulations. Because of the previously formed weld pool, the welding currents at Cr seem to be higher than at Cf, which was clearer at higher rotation frequencies. At low-frequency rotation, simulation results differed more from experimental ones than at high frequencies. This is probably due to the fact that the quasi-steady weld pool model was adopted for weld pool simulations, under the assumption of a very high rapid rotation of welding arc. In the setup of GMAW with rotating arc for a fillet structure, it was assumed that the torch inclines by 45° both to the horizontal base metal and vertical sidewall (Shi et al., 2005). Welding arc burns between the wire and the horizontal base metal or vertical sidewall alternately. Figure 6.6(a) illustrates the geometry and definition of fillet welding with a rotating torch. In the paper by Shi et al., the welding torch was thought to be at a centered position, if its rotation axis was positioned at the angle bisector of the fillet structure. Otherwise, the welding torch was thought to be offset. Figure 6.6(b) and (c) shows the cross-section of the fillet welding and their corresponding current waveforms with a centered or offset rotating torch, respectively. The current waveform shows the symmetry at left and right half-cycle when the torch is at the centered position (Fig. 6.6b), while the current becomes asymmetric (Fig. 6.6c) if the welding torch is deviated from the angle bisector of the fillet joint. The difference between the values of welding current integrated with respect to time over the left and right half cycle showed a linear relationship with the offset distance and is used for the torch offset detection during seam tracking, Fig. 6.7, and was adopted for the error criterion. The rotating arc sensor could track the weld seam by using the correction data proportional to the area difference of current waveforms which is equivalent to the difference of average currents of the left and right half cycle. The weld seam could be tracked with an accuracy of 0.07 mm maximum error and 0.035 mm standard deviation of error in the welding of mild steel. The rotating arc sensor showed a fairly good performance also in the aluminum welding in spite of the slightly increased error.

6.3.3 Rotating arc sensor for end point detection in fillet welding In addition to seam tracking, through-arc sensing can be applied to detecting the end point of weld joints because the joint shape is reflected on the

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L Cr R Cf L

350 300

Current (A)

250 200 150 100

40 ms L

L

50 0 0.0

0.1

0.2

Time (s) (a) Raw signal Filtered signal

L Cr R Cf L

350 300

Current (A)

250 200 150 100 L

40 ms

L

50 0 0.0

0.1

0.2

Time (s) (b)

6.5 Simulated and measured welding current waveforms at rotation frequency of 25 Hz (Kim and Na, 2001a): (a) simulated results; (b) experimental results.

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L

Cf

Cr R

(a) 305

L

L

R

300 Current (A)

Contact tip to workpiece distance

Welding torch

L

295 290 285

Workpiece

280 R 0.50

(b)

Cf

Cr

Left Right half- halfcycle cycle 0.55

350 Welding torch

Current (A)

CTWD

L R

0.65

0.70

0.65

0.70

L R

330 320 310 300 290

Workpiece

0.60 Time (s)

L

340

offset

Cf

280 0.50

Cf

Cr Cf

Left Right half- halfcycle cycle 0.55

0.60 Time (s)

(c)

6.6 Geometry of fillet welding with rotating torch and the current signals (Shi et al., 2005): (a) geometry of fillet welding; (b) cross-section of fillet weld (centered torch) and its current signal; (c) cross-section of fillet weld (offset torch) and its current signal. WPNL2204

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Experimental results Linear fit 99.7% confidence interval

2

SL–SR (A s)

1 0

–1 –2 –3 –4

–2 0 2 Offset distance (mm)

4

(a) Experimental results Linear fit 99.7% confidence interval

0.8 0.6

SL–SR (A s)

0.4 0.2 0.0 –0.2 –0.4 –0.6 –0.8 –6

–4

–2

0

2

4

6

Offset distance (mm) (b)

6.7 Correlation between offset distance and area difference of current waveforms in gas metal arc welding with rotating arc (Shi et al., 2005): (a) welding of mild steel; (b) welding of Al 5052.

electrical signals such as welding current and voltage, which are related to the tip-to-workpiece distance. Rotating arc welding has two prominent features in detecting the end point of weld seams: one is that the effect of weld pool can be reduced when the arc rotates along the front half circle path, while the other is that its enhanced responsiveness ensures the rapid detection of end points (Yoo et al., 2006). The following topics were investigated by Yoo et al.: (a) a mathematical model to analyze the GMAW system, (b) a geometrical sensing model of the rotating arc sensor in fillet welding with open and closed ends, and (c) current simulations and experiments in steel welding of fillet structures with open and closed ends. It adopted the dynamic simulation model of welding wire melting which used

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the variable space network method and by modeling the heat flux from the molten end of welding wire into the electrode (Kim and Na, 2001a). To simulate the waveform of welding current during actual fillet welding, geometrical models of the fillet weld have to be developed. It is necessary to know the bead geometry to simulate the welding current from various equations. To model the weld bead shape, the following two conditions were assumed: (a) volume of the welding wire fed equals the volume of bead formed on the base metal surface and (b) cross-sectional view of the bead is triangular. After the welding gun had passed, the cross-sectional view of weld bead from the welding direction was approximated as a right-angle triangle. When the weld bead is forming under the rotating arc, the crosssectional area was approximated as linearly decreasing along the direction of welding gun motion. After the end of fillet weld, bead-on-plate welding with inclined rotating arc was assumed to form the weld bead in triangular shapes. The mathematical model of GMA welding system, the electrode melting model, and the geometrical model of weld beads were used to simulate the current waveform of rotating arc. A fourth order Runge–Kutta method was applied to solve the differential equations. Figure 6.8 shows the fillet structure with open end adopted for simulations, and the simulation results of the welding current waveform for this structure. Before approaching the fillet end, the welding current fluctuates twice during a cycle of arc rotation. After passing the fillet end, however, the welding current fluctuates only

Current

Current (A)

350

300

250

200 0.5

1.0

1.5 Time (s)

2.0

2.5

6.8 Fillet structure with open end and simulation results of current waveform (Yoo et al., 2006).

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once during a cycle of arc rotation. The overall frequency of current waveform is 40 Hz in the fillet joint region, but decreases in the transition region, and finally reaches 20 Hz after the end of the fillet joint, which is the frequency of torch rotation. The variation of arc length due to arc rotating, the bead geometry and fillet joint geometry with an open end probably causes the different fluctuations of the simulated welding current. To validate the simulation results, comparisons between the current waveform of simulations and that of experiments were made. The current waveform measured in rotating arc welding showed a higher value than the simulated one, which is probably because the actual bead height is somewhat higher than the assumed one in the analysis. All the comparisons during fillet joint area, transition area, and after the end of the fillet joint showed that the simulation results were well suited to the experiments. Figure 6.9 shows the fillet structure with a closed end adopted for simulations, and simulation results of current waveform for this structure. Before approaching the fillet end, the welding current fluctuates twice during one cycle of arc rotation. As the fillet end is approached, however, the welding current tends to fluctuate three times during one cycle of arc rotating. The reason for these different current waveforms is also probably due to the variation of arc length during arc rotating, the bead geometry, and fillet joint geometry with a vertical end plate. The change of the current waveform is, however, less distinct than that of the fillet structure with an open end,

Current (A)

350

Current

300

250

200 0.0

0.2

0.4

0.6

0.8

1.0

Time (s)

6.9 Fillet structure with closed end and simulation results of current waveform (Yoo et al., 2006).

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which may cause difficulty in implementing the algorithm of end point detection of the fillet joint for GMAW with rotating arc. To validate the simulation results, the current waveform of simulations was compared with that of experiments. The two results are in a fairly good agreement, although a very careful countermeasure would be essential for the application of end point detection algorithm for the fillet structure with a closed end in GMAW with rotating arc.

6.3.4 Arc sensor using electromagnetic arc oscillation A magnetic field externally applied to the welding arc deflects the arc by an electromagnetic force (Lorentz force) in the plane normal to the field lines. The welding arc can be deflected forward, backward, or sideways with respect to electrode and welding direction, depending on the direction of external magnetic field. If a unidirectional magnetic field is applied to an AC arc, or an alternating field is applied to a DC arc, then the arc is oscillated in the position normal to the welding direction. This has been used to improve the welding with both gas tungsten arc welding (GTAW) and GMAW processes (Hughes and Walduck, 1987). Oscillating the arc sideways with respect to welding direction could be used for strip cladding (Mallya and Srinivas, 1993) and welding a material that is sensitive to hot cracking (Tseng and Savage, 1971), because the arc oscillation results in a wide bead and uniform and shallow penetration. A mathematical model of magnetic arc deflection was developed for theoretical prediction of arc deflection and dynamic behavior of arc sensor, and confirmed by corresponding experiments (Kang and Na, 2002). The effect of magnetic field intensity, initial arc length, and arc current on the deflection of GTAW in an external magnetic field was investigated. As the magnetic flux density and arc length increase, the magnitude of arc deflection also increases, while it decreases with increasing arc current. This is probably because an increase in welding current and decrease in arc length make the arc stiffness increase. Although magnetic flux density and arc deflection were related linearly in simulation results, they showed a nonlinear relation in experimental ones. The linear relationship was probably attributable to the simulation that considered only the centerline deflection, while in actual welding the arc has a certain size of arc column. Magnetic arc oscillation changes the arc length, which periodically changes the welding voltage and current. An alternating parallel magnetic field causes the arc to oscillate in the position normal to the welding direction, which takes effect like a mechanical weaving. There is, however, a difference between magnetic arc oscillation and mechanical weaving on the flat plate. In mechanical weaving, the welding voltage and current are constant during bead-on-plate welding, while in magnetic arc oscillation they change

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periodically. The welding voltage was maximized both at the left and right end positions of arc oscillation, where the arc length was the largest, and minimized at its central position due to the smallest arc length. As the oscillation frequency increased, the magnetic flux density generated from the electromagnet was reduced due to the electromagnetic inductance. In the case of the electromagnet used in this work, the magnetic flux density decreased remarkably at the oscillation frequency higher than 15 Hz. Consequently the welding voltage variations were reduced considerably at the oscillation frequency over 15 Hz too. Magnetic arc oscillation changes the arc length, which periodically changes the welding voltage and current in V-groove GMAW. When the welding arc is positioned at the center of the groove, the current waveform becomes symmetric, while the current waveform changes to asymmetrical shape if the welding arc deflects from the groove center. The welding current signals were simulated and compared with experimental ones as shown in Fig. 6.10. By simulation and experimental results, the current waveform changed to a clear asymmetrical shape in the V groove welding with 2 mm offset distance of welding head, which can be effectively used for automatic seam tracking. Narrow groove welding is an important technique for increasing the productivity and reducing the distortion in the manufacture of thick-walled components. The most critical objective of narrow groove welding is to maintain the uniform and sufficient penetration at both groove faces. To improve the groove face fusion, the electrode may be oscillated by adopting a wire bending technique or alternatively a wire rotating method (Nomura et al., 1982). A welding system using electromagnetic arc oscillation was developed for effective side wall penetration in narrow groove welding (Fig. 6.11) and arc signal characteristics were investigated for automatic joint tracking (Kang and Na, 2003). The narrow groove GMA welding head is shown in Fig. 6.12. The electromagnet was built into the welding head of 6 mm thickness, where the surface of welding tip was coated with ceramic to insulate the body from the welding tip. The length and width of welding head were 160 and 60 mm respectively. A welding tip made of copper with high thermal conductivity was cooled by forced water flow to prevent its temperature from rising above its melting temperature. Shielding gas was supplied through the side holes, where a 10 mm root opening was investigated for square groove welds. The influence of welding parameters on weld bead formation was also investigated for oscillation frequency from 0 to 50 Hz and magnetic flux density from 0 to 75 gauss. It was revealed that the side penetration obtained with arc oscillation was deeper than that without oscillation, while the root penetration showed the opposite tendency. Increased magnetic flux density caused the oscillation width to increase, and consequently resulted in increased side penetration. If the magnetic flux

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Real-time weld process monitoring 310 Experiment Simulation

Welding current (A)

300

290

280

270

260 0.0

0.1

0.2

0.3

0.4

0.5

Welding time (s) (a) Exp.(raw data) Exp.(low-pass filtered data) Simulation Switching signal of magnetic fields

350

Welding current (A)

325

300

275

250

Simulation

225

200 0.0

0.2

0.4

0.6

0.8

1.0

Welding time (s) (b)

6.10 Welding current waveforms for V-groove welding with magnetic arc oscillation (Kang and Na, 2002): (a) welding with no offset; (b) welding with 2 mm offset.

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tion

irec

d ing

ld

We

Magnetic pole Welding torch Arc

Backing plate

6.11 Narrow groove welding using electromagnetic arc oscillation (Kang and Na, 2003).

Coolant outlet Coolant inlet

Yoke

Coil

Core

Pole

Contact tip

6.12 Photograph of narrow groove GMA welding head (Kang and Na, 2003).

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density increased over a critical value, undercut occurred at both groove faces. Electromagnetic analysis was performed for the design of electromagnets used in narrow groove GMAW, and also for investigating the characteristics of seam tracking arc sensors. Figure 6.13 shows the simulated and experimental current waveforms in narrow groove welding using electromagnetic arc oscillation. The waveforms of welding current were monitored under

350

Welding current (A)

Offset = 0 Experiment Simulation 300

250

200 0.0

0.2

0.4 0.6 Welding time (s)

0.8

1.0

(a) 350 Offset = 1 mm Experiment Simulation

Welding current (A)

325 300 275 250 225 200 0.0

0.2

0.4 0.6 Welding time (s)

0.8

1.0

(b)

6.13 Welding current waveforms in narrow groove GMA welding with magnetic arc oscillation with frequency of 5 Hz (Kang and Na, 2003): (a) welding with arc torch on center of narrow groove; (b) welding with arc torch 1 mm offset from center of narrow groove.

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the magnetic flux density of 50 gauss and arc oscillation of 5 Hz with a 10 mm root opening for square groove joint. Figure 6.13(a) shows a welding current waveform obtained for no deviation of welding head from the groove center. Figure 6.13(b) shows a waveform for the case of welding head offset to the right side by about 1 mm from the groove center. In the case of no deviation, two similar waveforms of welding current were obtained during one period of arc oscillation, where each current waveform was obtained during arc oscillation between a side wall and groove center. On the other hand, the current waveform had the same frequency as the arc oscillation in the case of 1 mm deviation, because the arc oscillation was likely to occur between two side walls. This periodic change of welding current can be used as the output signal for an arc sensor for automatic joint tracking in narrow groove GMAW with rotating arc.

6.3.5 Arc sensor using arc light intensity and welding resistance It was found that the arc light intensity showed opposite trends to the welding current and could be derived to be a function of welding current and arc length (Yoo et al., 1997a). The welding resistance is obtained by dividing the measured welding voltage by welding current. There are several reasons for employing the welding resistance for automatic seam tracking instead of the welding current. Firstly, the welding resistance provides the amplification effect with drooping characteristics of the GMAW power supply such that the welding current decreases and voltage increases with longer arc lengths. Therefore, the maximum-minimum ratio of welding resistance should be larger than that of welding current. Another reason is that it is easier to compare the welding resistance with arc light intensity because both signals have the same trend. Characteristics of the arc sensor using welding resistance and arc light intensity were investigated experimentally, because it was difficult to consider the effect of unstable arc and metal transfer with CO2 gas shielding through mathematical modeling (Yoo et al., 1997b). The welding current and voltage were measured through a current shunt with leads attached to the power source, while the arc light intensity was measured using an arc light sensor attached to the welding torch. Welding experiments were made on the V-groove using the solid steel electrode of 1.2 mm diameter. Argon and CO2 gas were used with a flow rate of 20 l/min and the travel speed was kept constant at 4 mm/s. The weaving frequency was selected to be 1 Hz for high-quality signals and the weave width was 10 mm, which provided an average weaving speed of 20 mm/s. When there is no deviation of the welding torch from the groove center, the measured welding current and resistance showed a phase shift of 180°,

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so that the peaks of welding resistance corresponded to the valleys of welding current. With argon gas shielding, the peaks and valleys of weaving signals were clearly observed because of the stable spray metal transfer mode at the welding current used. When CO2 gas shielding was applied, however, the signal quality deteriorated significantly, because the arc became unstable and the metal transfer occurred in globular mode. The welding resistance demonstrated almost the same characteristics of arc sensor as the welding current, the only difference being a phase shift of 180°. When argon gas shielding is used, the arc light intensity and welding resistance showed clear peaks and valleys without any phase shift, while signal qualities of both data were almost the same. Although the signal quality deteriorated in general with CO2 gas shielding, the peaks and valleys were clearly observed in arc light intensity, compared with the welding resistance signal. It was revealed that the signal quality of arc light intensity was much higher than that of welding resistance for CO2 gas shielding, and extensive signal processing was needed to find the weld seam position from the noisy signal. Clear weaving signals with comparable quality were detected for both the arc light intensity and welding resistance, even when the torch was shifted in the transverse direction from the groove center by 2 and 4 mm. Signal distortion depended on the amount of deviation such that the peaks shifted from the center with larger deviations than the valleys. When CO2 gas shielding was used, the signal quality deteriorated in general, while the arc light intensity provided higher-quality signals than the welding resistance. The effect of electrode shift was easily observed in the arc light intensity, while it was almost impossible to observe in the welding resistance. Signal differences of welding resistance and arc light intensity were calculated with respect to the amount of deviation. When the deviation was less than 2 mm, both signal differences changed almost linearly with respect to the amount of deviation. This linear relationship would be useful for developing the automatic seam tracking system, because the arc sensor was generally used to compensate for a small deviation. The results of automatic seam tracking experiments using these two signals showed that they could be successfully applied in actual welding, combined with the least squares and integration methods.

6.4

Electromagnetic monitoring of weld seams in gas metal arc welding

The electromagnetic sensor with detection coils fastened around a core is simple in construction and hence can be used for the detection of mechanical variables such as position or displacement. It is very difficult to define

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Steel plate Exciter

Left

Gap

Fore detector Right

6.14 Configuration of inductive sensor with three detectors (Bae and Park, 2006).

the relationship between the induced voltage and the workpiece condition, since it is affected by the electrical conductivity and magnetic permeability of the object and the geometrical configuration (Baratoletti et al., 1998). A dual-core sensor using two separated detectors was proposed to enhance the sensor sensitivity (Kim and Shin, 2003). Even an electromagnetic sensor consisting of one exciter and three separated detectors was developed to track the weld seam and to control the torch height at the same time for the workpiece with gap variation (Bae and Park, 2006). Figure 6.14 shows the configuration of the inductive sensor proposed in this study. Two detectors are located at the left and right side of weld seam, while the third one was located at the fore part of the sensor just over the weld seam. The left and right detectors were used to search the seam and the torch was placed at the groove center by eliminating the difference of their outputs. At the same time, the fore and right detectors were used to determine the torch height from the workpiece surface and also to determine the gap width. Figure 6.15 shows the relationship between the distance of sensor from gap center and the voltage difference between the right and left detectors. Within about ±5 mm distance from gap center, the relationship could be assumed to be linear for various heights of the sensor, which could be effectively used to decide the position of welding torch relative to the weld seam center. In order to obtain the relationship between the sensor output and the sensor-to-workpiece distance, the fore and right detector outputs were measured simultaneously for various sensor-to-workpiece distances and gap widths under the condition that the sensor was located just above the weld seam center. Both detector outputs increased distinctly for increasing sensor height and gap width, as would be expected. The fore detector

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Votlage difference (dV)

0.4

h=3

0.3 0.2

h=5 h=7

0.1 0.0 –0.1 –0.2 –0.3 –0.4 –0.5 –15

–10

–5

0

5

10

15

Distance from gap center d (mm)

6.15 Difference of two inductive detector outputs for various heights h = the sensor to workpiece distance in mm (Bae and Park, 2006).

showed a higher output than the right one, because the air gap below the fore detector reduced the effect of eddy current. The detector outputs could be represented by second order functions with the variables of sensor-toworkpiece distance and gap width. The automatic seam tracking system using the proposed inductive sensor could effectively follow the joint line, although it left an offset from the desired path which could possibly be removed through further investigation.

6.5

Laser visual monitoring of weld seams in gas metal arc welding

The active vision sensor based on optical triangulation is classified into two types according to the beam characteristics: structured beam or scanning beam (Lee and Na, 1993). Although the vision sensor with structured light is largely influenced by arc noise and the preprocessing time of images is long, it is widely used because it is relatively cheap and has a simple structure (Agapakis et al., 1986). In spite of the complex structure and high price, the application of the vision sensor with scanning beam increases steadily because the effect of arc noise is small and the preprocessing time of images is relatively short (Oomen and Verbeek, 1987). Since the 1980s, much research has been conducted to apply the vision sensor technology to welding automation. In most of the investigations, however, the sensing resolution and field of view were mainly considered in the design of vision sensors, while the sensor reliability was only rarely investigated. Recently

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the effect of arc light was investigated as to the geometrical configuration of welding arc and sensor, where various reflection properties of base metal surface were considered, to improve the reliability of vision sensors (Lee and Na, 1996). In this study, the reflectance model of welding arc was derived to estimate the arc noise for various configurations of sensor, base metal, and welding arc. Various experiments were carried out, first to determine the bidirectional reflectance distribution function (BRDF) parameters of the model, and second to verify the validity of the proposed model. Experimental results of the gray level of reflected arc agreed fairly well with the calculated ones, which were reversely proportional to the square of the distance between welding arc and reflecting area, where the arc light could be assumed as a point light source in the reflectance model for the distance between welding arc and base metal greater than about 45 mm. Experimental and simulated results showed that considerably high gray levels of reflected arc might be captured by the vision sensor, which then had a considerable influence on the performance of vision sensors.

6.5.1 Vision sensor for seam tracking of height-varying weldments Usually the fabrication of shipping containers and liquefied natural gas (LNG) tanks includes a welding process for corrugated sheet metals. Many types of corrugation are used in structure constructions according to their purposes, while the shape of corrugations can be represented as a composition of several parts such as line and curves. When the welding torch tip is exactly tracking the weld seam of corrugated sheets by keeping the torch angle perpendicular to the surface of sheet metal, the vision sensor should move together with the welding torch because it is mounted in front of the torch holder. In order to obtain the intersecting point between laser plane and base metal by simulations, only the shape definition of corrugated sheets is needed because the relative position of vision sensor can be obtained by using its mathematical model. An automatic welding system including a vision sensor was developed for the corrugated membranes of LNG tanks (Yu and Na, 1998). A vision sensor which could provide the information of 3D object geometry was investigated primarily, because it was difficult to apply the arc sensor to automatic welding of thin plates. However it was difficult to determine the proper configuration of sensory system including a charge-coupled device (CCD) camera and diode laser to specific weldments because of the interference and relative motion among vision sensor, welding torch and weldments during actual welding. The sensory system should be analyzed for various configurations of the sensor and weldments shapes by using a mathematical model of a CCD camera, laser plane, and workpiece with height

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Real-time weld process monitoring S4 R4 S5

S3

H

R3

S2 S1

S6

R2

S7

W

Wst (a) W1

W2

W3

S3 S4

S2 H

S5

S1 Wst (b)

6.16 Typical shapes of corrugation (Yu and Na, 1998): (a) LNG tank corrugation; (b) shipping container corrugation. (W = width, H = height, R = radius.)

variation to develop a vision sensor with high performance for corrugated membrane structure of LNG tanks. Figure 6.16 shows two typical corrugation shapes which are prevalent in the construction of LNG tanks and shipping containers. The shape of LNG tank corrugation consists of two linear parts and five circular arcs, and can be divided into seven segments from S1 to S7. Each segment of LNG tank corrugations could be expressed as the function of some parameters such as coordinates of the start point of line segment (or coordinates of the center point of circular arc segment), the length of line segment (or the radius of circular arc segment), and the angle of tangential line at the start and end point of segments. The shape of shipping container corrugations consists of five line parts which can be divided into five line segments, and consequently can be described more easily than the case of LNG tanks. The

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Main body

Torch Height slide Vision sensor U

q

Torch CCD camera Diode laser

Side slide

C LNG tank corrugation

Shipping container corrugation Rotating body

6.17 Schematic diagram of automatic seam tracking system with a vision sensor for corrugated membrane of LNG tanks (Yu and Na, 1998).

shape description of corrugated sheets was used to obtain the intersecting point between laser plane and base metal. Figure 6.17 shows the schematic diagram of automatic seam tracking system with a vision sensor developed in this investigation for the corrugated membrane of LNG tanks. The apparatus comprised a robot which had two rectilinear coordinates, a torch rotation mechanism, a vision sensor composed of a CCD camera, a diode laser of 690 nm wavelength and a narrow band pass filter, and a PC for processing the image data and driving the robot. The data deficiency is classified roughly into two types: the area of object surface which is not radiated by the laser beam and the area where the light beam reflected from the object surface is not observed by the camera. The second type of data deficiency is further divided into two various cases: the missing field of view (FOV) – the camera cannot catch the irradiated point because it is beyond the FOV, and the shadow effect – some part of the object occludes the light reflected from irradiated area to reach the camera. Figure 6.18 shows the simulated results for corrugated membrane of LNG tanks at the weld line, when the camera and laser diode of the vision sensor were located symmetrically about the vertical axis and have different separation angles. It was shown that the data deficiency occurred mainly at the corrugation corner and flat part of the corrugated sheet. The result also showed that the total separation angle between camera and laser beam of vision sensor

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Real-time weld process monitoring Sampled Missed Shadowed

(a) Sampled Missed Shadowed

(b) Sampled Missed Shadowed

(c)

6.18 Simulation results of measurement points (Yu and Na, 1998): (a) separation angle of 10°; (b) separation angle of 20°; (c) separation angle of 30°.

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largely affected its measuring efficiency for the corrugated sheets of shipping containers and LNG tanks.

6.5.2 Laser vision sensor using neural network for 3D weld seams For automatic welding of structures with 3D weld seam, a vision sensor was applied to acquire the image patterns at various sensor positions and orientations (Yoo and Na, 2003). A neural network model using two input parameters and two hidden layers was developed to extract the proper features from the image data. A conceptual mobile platform-manipulator welding system to which a laser vision sensor of scanning beam attached was designed for automatic welding of 3D seam in the hull block. To extract the proper feature points from the image data, it was necessary to identify the pattern categories. Figure 6.19 shows some typical image patterns at various sensor positions given from the geometric sensing model. The neural network model and the training model used in this study were programmed by C codes. At first, the sensed images of each image pattern were given by the sensing model, and some characteristic features, such as first derivatives, second derivatives, maximum or minimum value of them, sum of them, and so on, were extracted and tested. It was revealed that the sum of coordinates of each image pixel and the maximum difference of the coordinates of two P4

1

Pattern 2

P3

Pattern 1 Z

P2

Pattern 1-3

P5

Pattern 1-2

0

P1

0

Pattern 1-1 Pattern 3

X

Pattern 4

Y 1

0

6.19 Feature points and typical images of vision sensor for 3D weld seam in hull block (Yoo and Na, 2003).

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consecutive pixels are the most appropriate to differentiate the image patterns from each other. A neural network using the aforementioned parameters as inputs and two hidden layers that had 10 nodes each was developed to find the pattern type as output. A back-propagation algorithm was adopted to teach the neural network by using the geometrical patterns determined by simulations. Feature points of the classified image were extracted by the line segmentation method to find the weld seam or its end position for the automatic welding system.

6.5.3 Automatic seam tracking in pulsed laser edge welding Vision sensors adopted for automatic seam tracking in arc welding generally have an auxiliary light source and a preview distance, which is the distance between the welding and sensing position. The preview distance can generate the tracking errors, especially in micro-welding of small parts. A vision sensor was applied to find the weld seam by monitoring the weld pool in pulsed Nd :YAG laser welding of bellow edges (Lee and Na, 2002). The vision sensor observed the weld pool, and the weld seam was extracted from the image of the monitored weld pool. No auxiliary illumination source was used for monitoring the weld pool. It was found that the shape and brightness of the weld pool changed temporally even in one pulse duration. Experiments were carried out to find the appropriate moment of shutter triggering for relatively clear images. The welding system for automatic seam tracking in micro laser edge welding consisted of a three-axis stage, Nd :YAG laser focusing optics, CCD camera, narrow band pass filter, synchronization circuit, and PC with frame grabber and pulse generator, as shown in Fig. 6.20. The Nd :YAG laser beam was perpendicular to the edge joint, while the CCD camera was mounted with a separation angle from the laser beam. Pulsed laser seam welding consisted of multiple periodic pulses. The start of each pulse was detected in the synchronization circuit whenever the laser pulse is radiated. To get a relatively clear image, the shutter of the CCD camera was synchronized with the start of each pulse and opened at a specific time delay from the laser pulse start. The shutter speed of the CCD camera was selected to be 1/30 000 second due to the short pulse duration (1.5 ms) and the rapid temporal change of weld pool. Figure 6.21(a) shows the temporal development of weld pool, where the laser beam pulse was irradiated at a little distance from the previous bead center. In the early stage of the pulse, the front boundary of previous bead reflected the light from the current weld pool, and therefore the current weld pool and the reflected previous bead boundary were relatively bright. As time passed, the weld pool temporally increased, became bright and

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Laser beam

Pool image

Nd:YAG laser optics CCD camera

Nd:YAG laser

CCD camera control unit Laser pulse signal

Monitoring signal Shutter trigger pulse

PC Pulse generator

Synchronization circuit Sync. signal

Frame grabber Pool image output

6.20 Automatic seam tracking system in micro-laser edge welding of bellows (Lee and Na, 2002).

finally formed a weld pool. To show the temporal behavior of weld pool and find the moment suitable for a clear and meaningful image, the representative weld pool images were acquired at 0.2, 0.4, 0.6, 0.8, 1.0, and 1.4 ms from the pulse start, as shown in Fig. 6.21(b). The weld pool and reflected area were shown in the early stages (0.2 and 0.4 ms), while the weld pool increased and became bright in the later stages (after 0.6 ms). The solid– liquid interface boundary and the central region of weld pool were clearly distinguished in the images. The weld pool before 1.0 ms was not sufficiently formed, while the front boundary of the weld pool after 1.0 ms was blurred and sometimes indistinct. From these results, the time of image acquisition for automatic seam tracking was selected to be 1.0 ms from each laser pulse start. The principle of general vision sensors used with the structured light is based on optical triangulation as shown in Fig. 6.22(a). The plane of the laser diode is projected on to the workpiece, specifically at a little distance from the weld pool, and the pattern of illuminated line is observed by a CCD camera that is mounted with a specific separation angle from the laser diode. The height variation in the direction of Z-axis (ΔZ) of weld seam causes the laser stripe to move by the amount of ΔV in the direction of Vaxis in the image plane. As in the height variation, the weld seam can deviate in width direction during the welding process. In this case, the weld seam position in the laser stripe is shifted by the amount of ΔX, and consequently ΔU in the image plane. Figure 6.22(b) shows the working principle of the

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Rear boundary Welding direction

Previous weld bead Previous bead center Front boundary

0.2 0.2 mm

Weld seam Current weld pool

Current pool center Reflected area (a)

0.2 ms

0.4 ms

0.6 ms

0.8 ms

1.0 ms

1.4 ms

(b)

6.21 Weld pool images captured at various times after laser pulse start (Lee and Na, 2002): (a) temporal development of weld pool; (b) weld pool images at various times.

developed vision sensor. The CCD camera and Nd :YAG laser beam, instead of the laser diode used in conventional laser vision sensors, had a separation angle, and the weld pool image was captured in the CCD. Consequently the height variation of weld seam, ΔZ, gave rise to a movement of the weld pool image, ΔV, in the direction of V-axis of the image plane. The weld seam

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Side view

Nd:YAG laser beam CCD

CCD Laser diode ΔZ Height variation Laser diode plane

Preview distance Image

Nd:YAG laser beam Pr

V

ev

iew

Weld pool

dis

Weld seam

tan

Laser stripe

U

ce

ΔV by ΔZ : height variation

Z X

ΔU by ΔX : deviation in width direction

Y

(a)

Side view Nd:YAG laser beam CCD CCD ΔZ Nd:YAG laser beam

Height variation

Weld pool Image

Weld pool centerline (imaginary)

V

U

Weld pool Laser beam area

Z X

Y

Weld seam

(b)

ΔV by ΔZ : height variation ΔU by ΔX : deviation in width direction

6.22 Comparison of conventional and developed vision sensors (Lee and Na, 2002): (a) conventional vision sensors with CCD camera and structured light; (b) developed vision sensor with CCD camera only.

must be kept at the center of laser beam during the welding process. However, the deviation of weld seam from the laser beam center in the width direction, ΔX, caused the weld seam on the weld pool centerline to move the amount ΔU in the U-axis direction. As a result, the cross-section of the Nd :YAG laser beam along the Z-axis in the developed vision sensor

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could be regarded as the plane of laser diode in other vision sensors using the structured light. Consequently, the weld pool centerline, where the cross-section of laser beam intersects the weld pool, was acting as the laser stripe on the workpiece. This principle made it possible to find the 3D weld seam position on the weld pool centerline without any auxiliary light source and preview distance. Therefore, this type of vision sensor could extract the weld seam information directly from the weld pool image in pulsed laser welding of bellow edges, as from the laser diode stripe in conventional vision sensors.

6.6

References

agapakis j e, katz j m, koifman m, epstein g n, friedman j m, eyring d o and rutishauer h j (1986), ‘Joint tracking and adaptive robotic welding using vision sensing of weld joint geometry’, Weld J, 65 (11), 33–41. bae k y and park j h (2006), ‘A study on development of inductive sensor for automatic weld seam tracking’, J Mater Process Tech, 176, 111–116. baratoletti c, buonanni r, fantasia l g, frulla r, gaggioli w and sacerdoti g (1998), ‘The design of a proximity inductive sensor’, Meas Sci Technol, 9, 1180–1190. cook g e (1983), ‘Robotic arc welding: research in sensory feedback control’, IEEE Trans Ind Electronics, IE-30 (3), 252–268. dilthey u, oester m and gollinick j (1996), ‘Mechanized metal-gas-shielded arc welding with high-speed rotary wire electrode’, Schweissen und Schneiden, 48 (11), 866–875. hughes r v and walduck r p (1987), ‘Electromagnetic arc path control in robot plasma welding’, Robotic Welding, IFS Publication and Springer-Verlag, Bedford, UK, 243–263. kang y h and na s j (2002), ‘A study on the modelling of magnetic arc deflection and dynamic analysis of arc sensor’, Weld J, 81 (1), 8/s–13/s. kang y h and na s j (2003), ‘Characteristics of welding and arc signal in narrow groove gas metal arc welding using electromagnetic arc oscillation’, Weld J, 82 (5), 93/s–99/s. kim c h and na s j (2001a), ‘A study of an arc sensor model for gas metal arc welding with rotating arc, Part 1: dynamic simulation of wire melting’, Proc Instn Mech Engrs, 215-B, 1271–1279. kim c h and na s j (2001b), ‘A study of an arc sensor model for gas metal arc welding with rotating arc, Part 2: simulation of an arc sensor in mechanically rotating gas metal arc welding’, Proc Instn Mech Engrs, 215-B, 1281– 1288. kim j w and na s j (1991a), ‘A study on prediction of welding current in gas metal arc welding, Part 1: modelling of welding current in response to change of tipto-workpiece distance’, Proc Instn Mech Engrs, 205, 59–63. kim j w and na s j (1991b), ‘A study on prediction of welding current in gas metal arc welding, Part 2: experimental modelling of relationship between welding current and tip-to-workpiece distance and its application to weld seam tracking system’, Proc Instn Mech Engrs, 205, 64–69.

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kim j w and shin j h (2003), ‘A study on a dual-electromagnetic sensor system for weld seam tracking of I-butt joints’, Proc Instn Mech Engrs, 217-B, 1305– 1313. lee c w and na s j (1993), ‘Vision sensor for welding automation’, J Kor Weld Soc (in Korean), 11 (3), 10–21. lee c w and na s j (1996), ‘A study on the influence of reflected arc light on vision sensors for welding automation’, Weld J, 75 (12), 379/s–387/s. lee s k and na s j (2002), ‘A study on automatic seam tracking in pulsed laser edge welding by using a vision sensor without an auxiliary light source’, J Manuf Syst, 21 (4), 302–315. mallya u d and srinivas h s (1993), ‘Magnetic steering of arc and bead characteristics in submerged arc strip cladding’, Weld J, 72 (11), 517/s–522/s. nomura h (1994), Sensors and Control Systems in Arc Welding, London, Chapman & Hall. nomura h, sugitani y and kobayashi y (1982), Narrow Gap MIG Welding Process with High Speed Rotating Arc, IIW document, 12-527-82. oomen g l and verbeek w j p a (1987), ‘A real-time optical profile sensor for robotic arc welding’, Robotic Welding, IFS Ltd., Bedford, UK, 117–128. shi y h, kim j t and na s j (2005), ‘Signal patterns of high speed rotational arc sensor for gas metal arc welding’, Sensors for Industry Conference, Houston, USA. tseng c f and savage w f (1971), ‘The effect of arc oscillation’, Weld J, 50 (11), 777/s–586/s. yoo c d, yoo y s and sunwoo h k (1997a), ‘Investigation on arc light intensity in gas metal arc welding, Part 1: relationship between arc light intensity and arc length’, Proc Instn Mech Engrs, 211-B, 345–353. yoo c d, sunwoo h k and koh k i (1997b), ‘Investigation on arc light intensity in gas metal arc welding, Part 2: application to weld seam tracking’, Proc Instn Mech Engrs, 211-B, 355–363. yoo w s, shi y h and na s j (2006), ‘End point detection of fillet weld using mechanized rotating arc sensor in GMAW’, Weld J, 85 (8), 180/s–187/s. yoo w s and na s j (2003), ‘Determination of 3-D weld seams in ship block using a last vision sensor and a neural network’, J Manuf Syst, 22 (5), 340–347. yu j y and na s j (1998), ‘A study on vision sensors for seam tracking of heightvarying weldment, Part 2: applications’, Mechatronics, 8, 21–36.

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7 Weld profile monitoring L. WU and G.-J. ZHANG, Harbin Institute of Technology, China

A weld is the joint formed by welding, and is defined as the solidified part behind the weld pool. The weld profile is an important factor for weld quality.

7.1

Necessity for weld profile measurement

It is necessary to measure the weld profile for three reasons: to assess (1) the shaping parameters, (2) the weld penetration, and (3) the weld surface shaping defects.

7.1.1 Requirement of shaping parameters of weld profile In view of the importance of strength in a weld, to meet the mechanical properties of welded joints, such as tensile property, fatigue property, etc, weld profile dimensions should satisfy special requirements, which are also one of the points of inspection of weld quality. The welding criteria of different countries, such as the ISO welding standard system1, the European EN standard system2, the American AWS standard system3, and the Japanese JIS standard system4, have strict stipulations for weld shaping dimensions.

7.1.2 Weld penetration The penetration of the weld joint is the most important factor related to the strength and other performances of welded products, and it is vital to control the penetration strictly for some important welded products. The backside width of a weld is the most direct physical parameter reflecting weld penetration. However, in welding, owing to the limit of the environment, direct backside inspection is often not feasible, and topside shaping 164 WPNL2204

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(a)

(b)

165

(c)

7.1 Photomicrograph of weld bead at different penetrations.

parameter inspection is adopted to predict backside width. This method needs to select and detect appropriate on-line topside geometric feature parameters of the weld, and then set up the relation model between these topside geometric feature parameters and backside width5–9. Take the thin-plate gas metal arc weld (GMAW) as an example. Figure 7.1 shows the weld bead at different penetrations. Through analysis of the molten metal volume, the rules governing the weld bead shape in GMAW show that the topside weld bead section area can be proposed as the characterized parameter that can predict the backside weld bead shape. The topside weld bead section area can be expressed as topside weld width and average height of weld reinforcement. The welding current step experiment, welding speed step experiment, variational heat sink experiment, variational gap experiment, and variational misalignment suggest these characterized parameters are reliable10.

7.1.3 Inspection of weld surface shaping defects Weld surface shaping defects include crack, crater crack, cavity, undercut, hump, bad shaping, and misalignment, as given in the standard ISO6520-1. The geometric features of these defects are very obvious, and can be identified from weld bead appearance.

7.2

Outline of weld profile

According to the different structures of welded joints, fusion welded joints can be divided into five basic types: butt joint, T-joint (cruciform joint), lap joint, corner joint, and edge joint, of which butt joint and T-joint are the most common strength. The detailed description of the profile, geometric parameters, and shaping requirements of the two kinds of joints is as follows. The reference is the standard ISO5817-199211.

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7.2.1 Butt joint A butt joint is defined as a weld joint formed by welding two workpieces in the same plane together. According to differences in thickness of plates, groove type, and welding procedures, there are main categories of butt joints, as shown in Fig. 7.2. The requirements of weld profile dimensions of fusion butt joints are as follows: •

The surface of weld bead should be uniform, and there should be a smooth transition between weld beads, also between weld beads and base metal. • In an I-typed groove butt joint, as shown in Fig. 7.3(a), the width and reinforcement of weld should be in accordance with the regulations of Table 7.1.

(a) Edge-flange butt joint

(b) Single side V-shape

(c) I-shape

(d) Single V-shape

(e) Double V-shape

(f) Single U-shape

(g) Double U-shape

δ

δ

δ1

(h) Different thickness

7.2 Categories of butt joint.

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L

L

Weld profile monitoring B a b a

B b

a

b B1

B1 (a)

I-typed

(b)

h

p

h

h

h

a

167

Non I-typed

7.3 Geometric parameters of butt weld: (a) I-typed; (b) non-I-typed (a, width from toe to edge of groove; b, gap of groove; B, weld topside width; B1, weld backside width; h, height of weld reinforcement; p, size of obtuse side of groove; g, width of groove).

q

q

7.4 Side angle of butt weld.

Table 7.1 The size of butt weld profile Welding process

Type of joint

Weld width B Bmax

Bmin Submerged arc welding Manual arc welding and gas shielded arc welding

I-typed weld Non-I-typed weld I-typed weld Non-I-typed weld

•

b g b g

+ + + +

Weld reinforcement h

8 4 4 4

b g b g

+ + + +

28 14 8 8

0∼3 Flat position: 0 ∼ 3 Other: 0 ∼ 4

In a non-I-typed groove butt joint, as shown in Fig. 7.3(b), the width and reinforcement of weld should also be in accordance with the regulations of Table 7.1. • The side angle of weld θ should be more than 90°, as shown in Fig. 7.4.

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hmax

168

25 mm

7.5 Distance between highest and lowest points of weld surface.

(a) Single side V-typed

(b) Single side V-typed with obtuse edge

(c) Double side V-typed

(d) Double side V-typed with obtuse edge

(e) Single side J-typed with obtuse edge

(f) Double side J-typed

7.6 Example of T-joint and cruciform joint.

•

•

In terms of concavity–convexity of weld appearances, in a random given 25 mm range of weld seam, the reinforcement of weld seam hmax–hmin cannot be more than 2 mm, as shown in Fig. 7.5. In a random given 300 mm range of continuous weld seam, the linearity of edge of weld seam on the axial direction of weld seam should be less than 3 mm.

7.2.2 T-joint T-joints, including inclined T-joint, tri-joint, and cruciform joint, are defined as weld joints formed by welding the two or three vertical or angular workpieces together through a fillet weld. As is shown in Fig. 7.6, there are also a lot of categories. The geometrical parameters for the appearance of this kind of welding joint are described in Fig. 7.7. The requirement for shaping of T-joint is shown in Table 7.2.

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hf


h

2

d

h

1

h

h

0

e

7.7 The geometrical parameters of T-joint (h, thickness of weld; hf, size of toe weld; h0, effective thickness of weld at weld throat; h1, penetration; h2, degree of convexity; d, weld toe; e, weld root).

Table 7.2 Requirement for T-joint weld profile Range

h

2

d

0

e

h 1

h

Weld side angle θ

Allowable limit

0.9hf0 < hf ≤ hf0 + 2 hf ≥ 0.9hf0 h0 = 0.9 h00 h0 ≥ 0.9h00 (h0 ≥ 0.63hf0) (h0 = 0.63 hf0) h2 ≤ 2 mm

h

Weld toe height hf Weld throat Thickness h0 Degree of convexity h2

Parameter

hf

Item

hf – real weld toe height hf0 – designed weld toe height h0 – real weld throat thickness h00 – designed weld throat thickness h00 = 0.7 hf0 h2 – degree of convexity q

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7.3

Surface shaping defects

Weld appearance defects include surface crack, porosity, crater, slag inclusion, lack of fusion, undercut, overlap, misalignment, and incompletely filled groove the appearance characteristic, formation, and influential factors of every defect as follows as defined in the standard ISO6520-1: 1998. •

•

•

•

Surface crack. Under the influence of weld stress and other embrittling factors, in the local position of the weld the binding force among metal atoms is destroyed and new interfaces are generated; the aperture between interfaces is called weld crack. It has the characteristic of keenedged indentations and a large length to width ratio. As shown in Fig. 7.8, there are longitudinal cracks, transverse cracks, and radial cracks. No surface cracks are permitted in a high-quality weld. Crater crack. The hot crack in a crater is called a crater crack, and is shown in Fig. 7.9. Thus should be no crater cracks in important weld joints. Lack of fusion. The lack of fusion between weld metal and base metal or between layers of weld metal are classified as side wall lack of fusion, weld bead lack of fusion, and root lack of fusion, as shown in Fig. 7.10. Crater. As shown in Fig. 7.11, a crater is the concave area typically formed at the end of weld bead because of incorrect arc breaking or arc ending in the process of arc welding.

7.8 Surface cracks.

7.9 Crater crack.

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Lack of fusion

Side wall lack of fusion

Root lack of fusion

7.10 Lack of fusion.

Crater

h

h

7.11 Crater.

7.12 Undercut.

•

Undercut. A type of bad-formed weld defect, undercut, seen in Fig. 7.12, is the groove or concavity formed in the base metal along the weld toe because of incorrect parameter selection or operation. Undercut makes the area between weld bead and base metal rough, which causes stress focus in this area; therefore, the strength capacity of the weld joint is greatly decreased. In the standard, the size of undercut h should be less than 0.5 mm for important welds. • Over-height and over-convexity. Over-height means that the metal on butt weld is too high. In the same way, if the metal on fillet weld is too high, that is called over-convexity. Figure 7.13 shows examples. The higher the weld reinforcement, the more serious the stress concentration will be. In the standard, for important butt welds, the weld reinforcement h ≤ 1 + 0.1b, and the maximum value should be less than 5 mm; for important fillet welds, h ≤ 1 + 0.1b, the maximal value should be less than 3 mm. • Overlap. Overlap is the metal flow to unmelted base metal out of the weld in the welding process, as shown in Fig. 7.14.

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Real-time weld process monitoring Nominal weld

b h

b Real weld

h

7.13 Over-height and over-convexity.

Overlap

7.14 Overlap.

Pit Pit

7.15 Pit.

•

Pit. The low-lying part that is lower than the base metal surface formed on the weld surface or back of weld after welding is called a pit, as seen in Fig. 7.15. • Misalignment. As shown in Fig. 7.16, the misalignment is the surface offset of two welding workpieces that should be the same height after welding. According to the standard, in important butt welds the misalignment h ≤ 0.1t; the maximal misalignment should be less than 3 mm.

7.4

Automatic detection of weld profile

Weld profile detection methods are classified as geometrical size detection and shaping defect detection according to inspected objects, as on-line detection and off-line detection according to detection time, and as automatic detection and manual detection according to detection mode12–17.

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f

f

h


7.16 Misalignment.

Laser

Δd

1

Position sensor

θ 0

Δz

7.17 Principle of triangle measurement.

Here, two kinds of automatic method used for detecting weld appearance are introduced. One is the structured light detection method, which is relatively highly developed and more accurate18,19. The other method is the shape from shading, which is currently under research20,21.

7.4.1 Structured light detection The principle of structured light detection Structured light detection is an active visual method based on the principle of laser triangle measuring, which is shown in Fig. 7.17. The point light of a low-power laser is projected on the diffuse surface, and part of the diffusely reflected light passes a focus lens and produces an image on the surface of a light-sensitive sensor. When there is some displacement of the diffuse surface on the direction parallel to the laser light, on the imaging plane there will be a corresponding offset of the moving point. Thus, according to the offset of imaging point on the imaging plane, the position of the diffuse surface can be determined.

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Electronic imager

Laser diode α

Collecting lens

Joint

Laser stripe

Part A

Part B

7.18 Principle of structured light detecting weld workpiece surface shape.

It is easy to see that the height of surface point can be detected by the laser triangle measuring method. After a line structure light has been projected on the surface of the workpiece, the points with different heights have different offsets of image points on the imaging plane. Therefore, according to the stripe image on the imaging surface, the profile of the workpiece surface can be detected. The principle is shown in Fig. 7.18. The structured light detection method can accurately detect the geometrical shape of a weld cross-section, and is suitable for automatic weld tracking, parametric adaptive control of different weld joints and welding bead automatic planning of multi-layer welding. It can also be used to inspect weld appearance after welding. Figure 7.19 shows the detected structured light images of different welded joints. Mathematical model of structured light vision inspection For the structured light vision inspection, the known parameters are the coordinates of the points in the image plane, and it is necessary to calculate the coordinates of corresponding points in the workpiece reference frame. Thus, the mathematical model of the structured light vision inspection has been developed, as shown in Fig. 7.20. In Fig. 7.20, OwXwYwZw is the world reference frame (workpiece reference frame), plane OiXiYi is the image plane of CCD camera, axis Xi is

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↓

↓

(a) I typed butt joint with no gap

(b) I typed butt joint with no gap

(c) Lap joint

↓

↓

(d) V groove butt joint

(e) T-joint

↓

↓

(f) Good shaped weld

(g) T-typed weld

7.19 The structured light images of different welded joints.

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↓

↓

(h) Over-height

(i) Pit

↓

↓

(j) Burn-through

(k) Crater

(l) Undercut

(m) Misalignment

7.19 Continued

parallel to the row of the image, axis Yi is parallel to the column of the image, and point Oi is the intersection of optical axis and image plane. OcXcYcZc is the camera reference frame, the origin Oc is the optical center of camera lens, the axis Xc, Yc is separately parallel to the Xi, Yi with the some direction, and Zc is collinear with the optical axis and points outside. The lens of the camera approximately satisfies the pinhole projection model, corresponding to a thin lens in physics, which can be denoted by geometry perspective projection. A point (xc,yc,zc) in the camera reference frame

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Oc

Yc f

Oi

Yi q

Xi

P Zw

A

Yw

Ow

Xc

Zc (Zi)

B

Xw

7.20 Model of structured light vision inspection.

becomes an image point (xi,yi) in the image plane by perspective projection. The transformation relation is denoted as: xc =

zc xi zy ; yc = c i f f

[7.1]

in which f is the distance from the lens center to image plane, i.e. effective focus. The coordinate transformation from the real image plane to the image in computer (frame in memory) is: xi =

( u − cx )

Sx

; yi =

( v − cy )

[7.2]

Sy

in which (u, v) is the coordinate of the pixel in frame memory; (cx, cy) is the coordinate of the image center in frame memory; and (Sx, Sy) is the number of pixels in unit distance in the row and column direction in frame memory. The projector projects the light pattern onto the workpiece with a certain angle θ. The equation of the plane P of the structured light in camera coordinate system is: aXc + bYc + Zc = c

[7.3]

If we put the formula (7.1) into the above equation (7.3), we can get the perspective transformation between Cartesian coordinates (xc,yc,zc) in the camera reference frame of a point in the structured light stripe AB and its coordinate (xi, yi) in the image plane coordinate system, which is shown in formula (7.4):

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Real-time weld process monitoring cxi axi + byi + f cyi yc = axi + byi + f cf zc = axi + byi + f xc =

⎫ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪⎭

[7.4]

If the homogeneous coordinate of the point on the light stripe in world frame is (xw, yw, zw, 1)τ, and the corresponding homogeneous coordinate in camera frame is (xc, yc, zc, 1)τ, the transformation from world coordinate to the camera coordinate can be denoted as: ⎡ xc ⎤ ⎡ xw ⎤ ⎢ yc ⎥ ⎡ R T ⎤ ⎢ yw ⎥ ⎢ zc ⎥ = ⎢⎣ 0 1 ⎥⎦ ⎢ zw ⎥ ⎢1⎥ ⎢1⎥ ⎣ ⎦ ⎣ ⎦

[7.5]

in which R is the 3 × 3 rotation matrix and T is the 3 × 1 translation vector. Through camera calibration, the parameters R, T, (a, b, c) for light plane equation and effective focus f are known. So, from the known coordinate of the point in image coordinate system, the corresponding coordinate in camera coordinate can be obtained by formula (7.4) and, further, the corresponding coordinate in the world coordinate system can be obtained by inverse transformation of formula (7.5).

Example of application of the structured light detection The structured light method can precisely detect the weld profile on-line or off-line, and obtain the geometric dimension of the weld appearance, such as weld width and reinforcement. These data can be applied to real-time control of weld shaping quality, or to detection of flaw of weld surface, such as cavity, undercut, or defective formation. Now, we introduce an application example of the structured light used to real-time detect and control the solidified weld behind pool in gas tungsten arc welding (GTAW)22. According to Yu et al.22, the cross-section of a fully penetrated joint in GTAW welding without filler metal can be described by Fig. 7.21. In general, the sag depression depth H, the sag width b, and the backside bead width bb are used to describe the cross-section geometrically. It was observed that, when H and b were utilized to describe bb, no adequate relationship could be found. Thus a new geometric parameter, called the average sag depression depth, is defined by h = S/b where S is the sag depression area. It was found that bb can be determined with sufficient accuracy by h. There is a good relationship between the average sag depression h and the backside bead width bb, as shown in formula (7.6):

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179

h = s/b H

S bb

7.21 Full penetration weld parameters22 (s, cross-sectional area of sag depression; b, sag width; h, average sag depression defined as the ratio h = s/b).

h0 , b0 (set-points) Adaptive controller

Image processing

Welder

Manipulator

h, b (feedback)

Laser Current

Optical fiber

Frame grabber

Cylindrical lens

Motion and arc length

Filter

X Image Monitor Y

Camera

Torch Travel speed

Weld pool

Laser stripe

7.22 The structured light experiment system22.

bb = 85.81 + 4.94h

[7.6]

The sag geometry was measured by a structured light vision system, shown in Fig. 7.22. A laser stripe was projected onto the sag behind the pool rear, and a camera was used to sample the stripe images. The sensor, which consists of the laser, lens, and camera, is attached to and moves with the torch. The laser stripe was selected to be 20 mm behind the electrode. The sag depression area S and the sag width b could be known on-line through processing the sampled stripe images, then the average sag depression depth h = S/b was known. So, the backside bead width bb was calculated by

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formula (7.6), and inputted to the adaptive controller. The controller gave the orders to direct the act of the manipulator and adjust the size of welding current. Thus, the backside bead width was kept stable.

7.4.2 Detection of weld profile based on shape from shading Visual image sensing is the most obvious method for detection of object surface, but usually it can get only the plane 2D information of weld profile. With the development of the computer visual technique, object surface height recovery algorithms gradually mature, such as the method of double eyes visual, image sequences, and single view image with constraints. These methods lay the foundation for calculating weld surface height. Shape from shading is one kind of 3D computer visual algorithm. Based on the basic principles of object imaging, according to the relationship between normal direction of the object surface and the gray scale of the image, we can get object surface normal direction from the value of the gray scale of the image, and then we will get an object’s 3D information. The surface height recovery algorithm from the single view image has been used in remote sensing image. In recent years, height recovery from shading has been used in detection of weld profile23.

Basic principle and iterative algorithm23 The key to establishing the shape from shading lies in finding the relationship between the normal direction of the object surface and the gray scale of the image, which is found by deriving the reflectance map equation. The problem lies in solving this reflectance map equation. According to optical imaging, the object image depends on several factors: the light source, the surface profile, the surface reflectance characteristics, and the imaging direction, etc. The geometrical relationships involved in object imaging are shown in Fig. 7.23. We define the world coordinate system o-xyz originating at the object’s center, and also we define the image coordinate system O-XY. The normal direction of point P(x, y, z) on object surface is n. The lighting direction of light source is s. The incident angle is i. The reflection angle is e, the observing direction of camera is v. Let us introduce the grads (gradients) space concept: if the function of object surface height is z = f(x, y), supposing object surface is smooth, namely the first-order partial derivatives of surface height to x and y exist, then the direction n of surface normal can be expressed as the grads space vector (p, q, l) and it can also be figured by (p, q) in the grads space, where

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Camera z

v

e

181

n g i

y

s

P

x

0

7.23 Geometric imaging of light reflectance23.

p=

∂f ( x, y ) ∂f ( x, y ) ,q= ∂x ∂y

[7.7]

The following are assumed: • The camera gets the image from a source at infinity, namely the distance between camera and object is far greater than the biggest size of the object. • The light source travel from an infinite place and the light intensity is uniform. • The light reflection characteristic of the object surface is uniform and it is an entirely diffuse surface. Then the ideal reflectance map equation, namely the gray scale of the image I(x, y) can be expressed as the function of object surface grads (p, q), as shown in formula (7.8): I ( x, y ) = R ( p, q ) =

ρ ( 1 + pps + qqs ) 1/2

( 1 + p2 + q2 ) ( 1 + ps2 + qs2 )

1/2

[7.8]

where (ps,qs) is the lighting direction of the light source. From the ideal reflectance map equation, if we can obtain the height or grads (p, q) of the object surface, we can calculate the gray value I(x,y) of an object image. The basic idea of shape from shading is the inverse resolving process of the reflectance map equation, namely getting the object surface grads (p, q) from the image gray I(x,y). Because two variable values have been derived from only one variable, the characteristic of this equation comes to be morbid (the conditions are insufficient to solve it). In terms of resolving the morbid, or ill, characteristic of the reflectance map equation, we introduce constraint conditions as follows. (I(x,y) is abbreviated to I and R(p,q) to R in the next section). Because of noise

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existing in the real image, we introduce a light intensity error to the constraint equation: E1 = ∫∫ ( I − R ) dxdy 2

[7.9]

A further constraint is placed on the surface direction and we introduce a surface smoothness error item to the equation: E 2 = ∫∫ ( px2 + py2 + qx2 + qy2 ) dxdy

[7.10]

The discrete surface direction (zx, zy) of the discrete image is different from the continuous surface direction (p,q), so we introduce the grads consistency error: E 3 = ∫∫ [( zx − p ) + ( zy − q ) ]dxdy 2

2

[7.11]

By mixing the surface height and gray scale information, we join the three constraint conditions together and get the whole error expression equation: E = E1 + λE 2 + μE 3 = ∫∫ {( I − R ) + λ ( px2 + py2 + qx2 + qy2 ) + μ [( zx − p ) + ( zy − q ) 2

2

2

]}dxdy [7.12]

where λ and μ are the weighted factor of surface smoothness error and grads consistency error respectively. For an ideal smooth surface, the whole error is zero, but for a real surface, every error item has a certain impact. Ordering the entire error E and grads consistency error E3 to be the smallest one, we can get: λΔp = −(I − R)Rp − μ(zx − p) λΔq = −(I − R)Rq − μ(zy − q) Δz = px + qy

[7.13]

Introducing the discrete Laplacian, {Δf }kl ≈

k ( f kl − f kl ) ε2

[7.14]

where f¯ expresses the means of surface direction f and ε expresses the distance between pixels. Using formula (7.14) to deploy formula (7.13), we can get the discrete calculation formula of grads and height: ( kλ′ + μ ) pkl = ( kλ′ pkl + μzx ) + ( I − R ) Rp ( kλ′ + μ ) qkl = ( kλ′qkl + μzy ) + ( I − R ) Rq

[7.15]

k k z = 2 zkl − ( px + qy ) 2 kl ε ε

when λ′ = λ/ε2, from the above formula, we get the final iterative formula. The entire error includes the synthetic effect of three error items. By changing the value of the error item-weighted factor, we can get another

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0.4 0.3 0.2

0.0 –0.1

z, mm

0.1

–0.2 –0.3 –0.4

24

16

8

8

0

–8 y, p ixel –16 –24

–32 –32

16

24

0 –8 el –16 pix –24 x,

7.24 Iterative calculation result3.

algorithm based on height from gray. Because the grads consistency error was introduced, we can get the result of surface height while calculating the surface direction. By adjusting the value of surface smoothness weight factor, the calculation result is the same as the real value.

Application example23 In order to get surface height from a weld image, we simplify the image light source. At the same time, we take the weld by stationary welding as the research object. Position the source lighting from the upward direction, with incidence obliquity and bevel of (45°, 135°), to obtain the image of the crater lit by the parallel light source. Suppose the object surface has an even surface reflectance rate. The profile of the crater consists of a series of different height circles and it is near to center-symmetric. According to general knowledge of the welding process, we confirm prior conditions as follows: the height of boundary point is zero; the surface grads of boundary point is zero; image size is 64 × 64; the corresponding real object plane size is 4.0 × 4.0 mm2. By adopting the above algorithm about surface grads and height to be calculated from image gray, the surface smoothness error item-weighted factor λ decreases with the iterative criteria error μe3. We get the surface figure curve of Fig. 7.24 by iterating 7040 times. The top and bottom of the

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curved surface are 0.27 and −0.25 mm respectively. The values of the iterative calculation are 0.30 and −0.30 mm respectively. The entire weld point surface profile can be shown by iterative calculation. The surface of the calculated profile is smoother than the real surface. The biggest error is the surface error nearing to the direction of light source. It is caused by the strong non-linearity of the reflectance image. Also, because of the factors of incompleteness – Lambert surface and the limit of light source distance and so on – the accuracy of surface recovery is affected to a greater or lesser degree.

7.5

References

1 ISO 6520-1: 1998, Welding and allied processes – Classification of geometric imperfections in metallic materials. Part 1: Fusion welding, IDT. 2 EN 729-4-1994: Quality requirements for welding – Fusion welding of metallic materials. Part 4: Elementary quality requirements. 3 AWS WI-2000 Welding Inspection Handbook, third edition. 4 JIS Z 3001:1999:Welding terms. 5 wu cs, gao jq, hu jk. Real-time sensing and monitoring in robotic gas metal arc welding. Measurement Science & Technology 18 (1): 303–310 2007. 6 lu w, zhang ym. Robust sensing and control of the weld pool surface. Measurement Science & Technology 17 (9): 2437–2446 2006. 7 tsai ch, hou kh, chuang ht. Fuzzy control of pulsed GTA welds by using realtime root bead image feedback. Journal of Materials Processing Technology 176 (1–3): 158–167 2006. 8 murugan n, gunaraj v. Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes. Journal of Materials Processing Technology 168 (3): 478–487 2005. 9 mi b, ume c. Real-time weld penetration depth monitoring with laser ultrasonic sensing system. Journal of Manufacturing Science and Engineering Transactions of the Asme 128 (1): 280–286 2006. 10 yan zh, zhang gj, gao hm, wu l. Determining the penetration from topside weld bead and weld pool geometry in P-GMAW. Science and Technology of Welding and Joining 10 (6): 744–749 2005. 11 Standard ISO5817–1992: Quality and grading of arc welding weld defects in steel structure. 12 venkatraman b, raj b, menaka m. Online infrared detection of inclusions and lack of penetration during welding. Materials Evaluation 63 (9): 933–937 2005. 13 mirapeix j, cobo a, jauregui c. Fast algorithm for spectral processing with application to on-line welding quality assurance. Measurement Science & Technology 17 (10): 2623–2629 2006. 14 reichert c. Pre- and postweld inspection using laser vision. SPIE 3396: 244–254 1998. 15 barborak d, reichert c. ‘Post-weld inspection using laser vision’, The International Conference on Advances in Welding Technology, 1997, Columbus, Ohio.

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16 da silva rr, siqueira mhs, de souza mpv. Estimated accuracy of classification of defects detected in welded joints by radiographic tests. NDT & E International 38 (5): 335–343 2005. 17 anouncia sm, saravanan r. Non-destructive testing using radiographic images – a survey. Insight 48 (10): 592 2006. 18 lee h, sung k, park h. Measurement of weld bead defect for shock absorber using laser vision sensor. Advances in Nondestructive Evaluation, PT 1–3 Key Engineering Materials 270–273: 2332–2337, Part 1–3 2004. 19 boillot jp, noruk j. The benefits of laser vision in robotic arc welding – Laser vision weld inspection can help reduce destructive testing costs. Welding Journal 81 (8): 32–34 2002. 20 li lp, lin t, chen sb. Light intensity analysis of a passive visual sensing system in GTAW. International Journal of Advanced Manufacturing Technology 27 (1–2): 106–111 2005. 21 li lp, chen sb, lin t. The modeling of welding pool surface reflectance of aluminum alloy pulse GTAW. Materials Science and Engineering A – Structural Materials Properties Microstructure and Processing 394 (1–2): 320–326 2005. 22 zhang ym, kovacevic r, li l. Adaptive control of full penetration gas tungsten arc welding. IEEE Transactions on Control Systems Technology 4 (4): 394–403 1996. 23 zhao d. Intelligent Control for the Shape of the Weld Pool in Pulsed GTAW with Filler Metal Based on 3-Dimension Visual Sensing [D]. Harbin Institute of Technology, Harbin, China, 2000.

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8 Weld penetration monitoring W. LU, Cummins Inc., USA and Y.-M. ZHANG, University of Kentucky, USA

8.1

Introduction

An important parameter in welding is weld penetration. By definition, weld penetration is the distance that the fusion line extends below the surface of the material being welded. There are two typical modes of weld penetration: partial penetration, in which the fusion depth is less than the thickness of the weld metal; and full penetration, in which the metal fusion reaches the bottom surface of the workpiece. Figure 8.1 shows the profiles of partial penetration and full penetration. If partial penetration is allowed so that the pool of molten metal (the liquid weld pool or simply the weld pool) is supported by solid metal, welds can be made less precisely but more productively, using processes such as gas metal arc welding (GMAW), with predetermined welding parameters. However, critical metal joining applications, such as gas tungsten arc welding (GTAW), typically require that the weld pool reach the bottom side of the work piece to produce full penetration; and the support from solid metal to the weld pool is thus not present. As a result, the liquid metal may drop from the weld pool especially when the backside width is large. Hence, to meet this requirement, the backside width of the weld pool must be measured, controlled, and maintained at a desired level or at least above a minimal value. However, a direct measurement of the backside width typically needs a backside sensor which is impossible or at least not convenient to install on the welding torch. To be convenient, the sensor should be ‘frontside’ so that it can be attached to and carried by the welding torch. In addition, the sensor should be durable in a harsh welding environment and its installation should have little or no effect on the torch’s accessibility. Preferably, the sensor should be application independent or nearly application independent so that the resultant control system can be used in different applications without extensive application specific studies. The major difficulty in obtaining a suitable penetration sensor arises from the invisibility of the backside of the weld pool from the front (torch) side. 186 WPNL2204

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Liquid weld pool

Solid metal

(a) Partial penetration Liquid weld pool

Weld pool surface

Solid metal

Wb (b) Full penetration

8.1 Weld pool and penetration.

The majority of the research in penetration sensing has been focused on finding a way to estimate the backside width of the weld pool using signals that can be measured from the frontside. Existing methods include pool oscillation, which uses the pool oscillation frequency to detect the penetration mode (partial or full penetration)1–3, infrared sensor, which measures front surface’s temperature distribution to estimate the weld pool depth4–7, ultrasonic sensor, which detects the weld pool depth based on received ultrasonic signals8–11, weld pool shape, which utilizes the frontside’s weld pool shape to estimate the backside width of the weld pool12, sag depression which estimates the backside width of the weld pool based on the depression of the weld pool13,14, and intrinsic plasma charge, which extracts weld penetration information from the weld arc15–19. This chapter introduces various methods of monitoring the weld penetration and backside weld width. Also introduced are methods used to control the welding application such that optimal weldment is obtained.

8.2

Weld pool oscillation sensor

The weld pool is composed of liquid metal. Thus, oscillations of the liquid metal in the weld pool occur when external excitations exist. These oscillations can be rather persistent and can be frozen during solidification, resulting in ripples in the solidified weld bead. The oscillation can be triggered

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Plotter

Waveform recorder

Oscilloscope

RC filter

Power source

Welding monitor

8.2 Schematic of experimental setup of observing weld pool oscillation.

by a variety of approaches, such as mechanical vibrations, impact of droplets entering the weld pool, gas bubbling and sudden changes in arc current. The oscillations in the weld pool can provide information about the size of the pool and this information can be used for in-process control of weld pool geometry. Xiao and Ouden2,3 investigated the GTA weld pool oscillations during partial and full penetrations for both stationary and travel arc. At zero travel speed, partial penetration pools were produced in 4 mm and 1 mm thick Fe360 and AISI 304 plates. The size of the weld pool was varied by varying the arc time at constant arc current and by varying the current level. Figure 8.2 shows the schematic of experimental setup. The RC filter was used in order to remove the high-frequency noise of the power source. By applying short-circuiting current pulses of approximately rectangular shape, the weld pool was excited and brought into oscillation at its natural frequency. The amplitude and frequency of the weld pool oscillation were measured directly after the current pulse by monitoring the arc voltage variation. To this end, a waveform recorder was used. The geometry of the weld pool was obtained from the cross-section of the spot weld after solidification of the weld pool. The experimental results showed that, for partial penetration, the voltage variation is more or less sinusoidal in shape with an amplitude of about 100 mV. As for the frequency, it was found to decrease with increasing weld pool diameter, as shown in Fig. 8.3(a), in which the oscillation frequency of

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200

150

300

f (Hz)

f (Hz)

400

200

100

50

100 0 3 (a)

189

4

5 6 D (mm)

7

Partial penetration

0 1

8

2

3

(b)

4

5 6 7 8 D (mm) Full penetration

9 10

8.3 The oscillation frequency vs. pool diameter.

(a) Partial penetration

(b) Full penetration

8.4 Weld pool oscillation.

a partial penetration weld pool in steel AISI 304 is plotted as a function of weld pool diameter. In the case of full penetration, two oscillation modes were observed, which could be best dealt with in terms of Db, the bottom diameter of the penetrated pool. As shown in Fig. 8.3(b), the oscillation frequency of a full penetration weld pool in steel AISI 304 is plotted as a function of Db. At small values of Db, the weld pool oscillates in the partial penetration mode (A ∼100 mV, f ∼150). At a certain value of Db, a transition occurs to a different oscillation mode with much larger amplitude (∼250 mV) and much lower frequency (∼30 Hz). The transition of the two modes occurs rather abruptly at Db ∼ 4.9 mm. This phenomenon can be explained as follows. For the partial penetration pool, the current pulse gives rise to a standing surface wave as shown in Fig. 8.4(a). The natural frequency f0 of this wave is given by the diameter of the weld pool D as: 12

⎛ γ⎞ f0 = 5.84 ⎜ ⎟ D−3 2 ⎝ ρ1 ⎠

[8.1]

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(a) Mode 1

(b) Mode 2

8.5 Oscillation modes of the traveling weld pool.

where γ is the surface tension of the liquid metal and ρ1 the density of the liquid metal. By contrast, the full penetration weld pool behaves as a stretched membrane as shown in Fig. 8.4(b). Its natural frequency can be expressed as: 12

⎛ γ ⎞ −1 f0 = 1.08 ⎜ Deq ⎝ hρ s ⎟⎠

[8.2]

where h is the plate thickness, ρs is the density of solid plate material, and D2eq = (D2t + D2b)/2 is the diameter of the equivalent cylinder whose volume is equal to the volume of the paraboloidal weld pool determined by the top diameter Dt and bottom diameter Db of the weld pool. The transition from the partial penetration mode to the full penetration model in that specific study3 occurred when the bottom diameter of the weld pool was half the plate thickness. Although the transition point may be different for other applications, the abrupt change in oscillation behavior always exists. The pool oscillation in the traveled arc was further explored. The weld pool was excited in the same way as the stationary arc. It was found that the oscillation of the partial penetrated weld pool is dominated by one of two different oscillation modes: Mode 1 and Mode 2 as shown in Fig. 8.5, dependent upon different weld conditions, whereas the oscillation of the fully penetrated weld pool was characterized by a third oscillation mode: Mode 3, as shown in Fig. 8.4(b). In the partial penetration case, Mode 1 has the oscillation frequency of /2 f = 0.07D−3 1

[8.3]

whereas Mode 2’s frequency is /2 f = 0.04D−3 1

[8.4]

where D1 is the diameter of the circle having a surface area equal to the surface area of the weld pool. When the excitation factor, the duration of the pressure pulse, is short, the depression of the weld pool surface will be small and the liquid metal in the weld pool will be pushed predominantly in the radial direction as shown in Fig. 8.5(a), which results in oscillation Mode 1. When the pressure pulse duration is increased, the weld pool

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surface below the arc is pushed down over a larger area, and a considerable amount of liquid metal is driven from the front of the area of the weld pool; the weld pool surface takes the form of Fig. 8.5(b). A critical factor tpc can thus be determined to characterize the oscillation mode. When full penetration occurs, the oscillation differs significantly from the partial penetration. It is in a mode (Mode 3) whose frequency is determined by f = 0.2D−1 2

[8.5]

where D2 is the diameter of the equivalent cylinder, the volume of which equals the volume of the fully penetrated weld pool. Thus, the transition from partial penetration to full penetration gives rise to an abrupt change in oscillation behavior, similar to that shown in Fig. 8.3(b). The weld pool oscillations in both the stationary arc and travel arc show that an abrupt change of pool oscillation frequency indicates the transition of penetration mode. Thus, it is expected to be useful for weld pool penetration control.

8.3

Infrared sensor

Arc welding is essentially a thermal process. Thus, infrared (IR) sensing seems a natural choice for weld process monitoring4–7. IR sensing is a noncontact measurement of the emissions of the IR portion of the electromagnetic spectrum. The temperature at a particular point on the welded metal reaches a maximum value and cools with time as the heat source moves. When constant heat input is supplied, the isotherm lines are formed parallel to the welding bead and are a function of the distance from the welding torch. In the case of moving heat sources, a quasi-stationary thermal state is established. Once steady state is achieved, the isotherms remain unchanged and move with the heat source. These isotherms are affected by changes in the base metal properties, plate thickness, joint fit-up, etc. Using IR cameras, we can depict the isothermal lines and the temperature distributions during welding. This has shown that the weld pool depth affects the surface temperature distribution of the base metal. Thus, we can monitor the weld pool depth by capturing the isothermal lines. IR sensing techniques include IR area scan, IR line scan, and point sensing techniques. An IR area scan provides a two-dimensional image of the IR emission during welding. Thus it is useful to depict an overall flow of heat transfer. Line scan and point sensing techniques, on the other hand, provide fast information owing to less computation, which makes them attractive for real-time processing. IR sensing techniques are normally integrated with mathematical modeling of the heat transfer. It can be assumed that, if the thermal gradient

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Real-time weld process monitoring Isothermal contour map

Scan line

Line scan

Temperature (volts)

200 160 120 80 40 0 0

50

100

150

200

250

Distance (pixels)

8.6 Data display methods for infrared sensor.

around the weld is constant, then the weld pool penetration is constant. Numerical modeling of the welding process is integrated with sensor development in the course of this effort in order to estimate the net heat exchange between a weldment surface and the infrared detector. The weld penetration was then numerically or practically estimated. The best way to explain this technique is by using examples. In one study4, GTAW was performed and the IR radiation that characterizes the thermal distribution of the plates being welded was detected by an IR camera. The IR camera determined the temperature distribution by sampling a portion of the emitted energy within a certain wavelength band and the temperature distribution of the plates being welded was represented as a line-scan (thermal profile across any line on the plate) and isotherms (regions of equivalent temperature), as shown in Fig. 8.6. One of the techniques to identify the solid–liquid metal interface is the inflections technique, which uses the change in slope of the thermal profile across the weld pool. Under steady-state welding conditions, a frame of data of the weld pool is acquired and the acquired data are analyzed using linescan and isotherms. The line-scan scheme is used to locate the solid–liquid

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Temperature Temperature gradient

180

3

Temperature (volts)

160 2

140 120

1

100 0

80 60

–1

40

Temperature gradient (volt/pixel)

200

193

–2

20 0 0

50

100 150 Distance (pixels)

200

–3 250

8.7 Temperature profile and first derivative for stationary arc.

metal interface in the temperature distribution of the plate being welded. The interface appears as a change in slope, inflection points, of the thermal profile across the weld pool, which is caused by the difference in emissivity between the solid and liquid metal. Figure 8.7 shows a temperature profile and its first derivative for a stationary arc. Analysis showed that the inflection points can be detected in the region five to ten lines ahead and behind the calibrated torch position. The predicted weld bead width is the distance between the points of inflection in the line-scan and this distance, obtained in pixels, can be converted to length using a calibrated magnification factor.

8.4

Ultrasonic sensor

Ultrasonic sensing is a non-destructive testing (NDT) technique8–11. It usually uses a laser array source to generate ultrasound on one side of the weld sample, and a receiver on the other side receives the diffracted laser arrays. When used for weld penetration sensing, this technique relies on diffracted signals at the bottom tip of the weld pool. A typical setup of an ultrasonic sensing system is shown in Fig. 8.811. When the laser array source generates a shear wave, the first received ultrasonic signal corresponds to the diffracted wave from the weld root because a shear wave does not propagate through liquid. The propagation path of laser array can be drawn as in Fig. 8.9, where the ultrasound is

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Laser source

Welding torch Receiver

Welding power supply

Focusing object

PC

Filter / amplifier

8.8 A typical setup of ultrasonic weld penetration sensing system.

x1

–x1

S2

S1

0

B D

A

x2

–x2

8.9 Propagation path of laser array in ultrasonic weld sensing system.

generated at point A and goes through path S1, the weld root and path S2 and is finally received at point B. By Fermat’s principle, a ray path between any two points is a path of minimum propagation time. During real-time welding, changes in material properties due to a temperature gradient cause the ultrasonic speed to vary. The propagation path of ultrasound will be curved, as shown in Fig. 8.10. By means of finite element analysis (FEA), ray equation initial values, and boundary conditions, the ray path can be mathematically calculated and specified by the source x1 and destination x2. In this way, the time-of-flight (TOF), which denotes the time that the ultrasound travels from point A to point B, is found by integrating the variable of time along the ray path. Assisted by geometric analysis, an analytical relationship between TOF and penetration depth D can be established by repeating the ray tracing algorithm for different penetration depths.

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Straight line Ray traced Weldment

8.10 Curved ultrasonic ray.

8.5

Vision-based sensor

It can be argued that the most direct way to monitor the weld penetration might be the image viewing sensors owing to the close relationship between the weld pool surface shape and weld penetration20. The geometry of the weld pool surface, i.e. the size and shape of the weld pool surface, may contain sufficient information of the weld penetration. The primary difficulty of obtaining a clear image from the weld pool is due to the presence of the strong arc light. Coaxial viewing was first proposed by Richardson et al.21 to solve this problem by blocking the arc light with the electrode when observing the weld pool. Another method is to suppress the arc light using a more intense illumination laser within a small band width of the laser. This technology has been implemented and commercialized by Control Vision, Inc. to develop a camera whose high-speed shutter is synchronized with a pulsed laser. Using this camera, very clear weld pool images have been acquired22. In this technology, the specular nature of the weld pool surface was taken advantage of. That is, the reflection of the illumination laser from the specular surface of the weld pool is not collected by the camera and the weld pool thus appears as dark in the image. However, when the 3D surface of the weld pool needs to be monitored, the specular surface would affect the laser stripes projected on the weld pool surface to be imaged. To image the 3D surface of the weld pool, a special technique has been invented to successfully obtain a 3D weld pool surface image as shown in Fig. 3.16(c) in Chapter 323,24. Thus, the weld pool surface image can be available to depict the weld pool geometry and weld penetration.

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Real-time weld process monitoring Narrow band filter Neutral density filters (–56.6, 0, 56.6) O1

(90, 125.3, 35.3) B

80

M1

170

38.2 A (63.3, 5.1, 131.4)

Workpiece

M2

Camera lens M4

45 (–56.6, 160.3, 0) O2

O 45 80

X

(90, 144.2, 54.2) D 35.3

120

Z

CCD camera

Y C (63.1, 52.6, 49.2)

180

54.2

O4 (–56.6, 170.9, 0)

38.6

M3

Neutral density filters O3 (–56.6, 0, –56.6)

8.11 Schematic of the light pass of the double-side visual sensing system.

Figure 8.11 shows a visual double-sided sensing system capable of imaging the weld pool topside and backside simultaneously in a frame to determine the weld pool geometry parameters12. The light path consists of topside and backside paths. The light from the weld pool reaches the reflector O1 at a 45° angle with the X-axis and is reflected to pass the composite filter, then reflected by O2 and finally focused on the target of the CCD camera. The backside light has a similar path. Both the topside and backside images concentrate on the same target of the CCD camera through the double-sided visual sensing light path system. The CCD imaging system, composed of a CCD camera, optical lens, and frame grabber, takes both the topside and backside images simultaneously and an image processing algorithm is implemented to extract the weld boundary, as well as the characteristic parameters, such as backside maximum length, width, topside length, width, etc. As both topside and bottomside images of the weld pool are captured, this double-sided image capturing system can be used to simultaneously control the topside and backside weld bead width.

8.6

Weld pool sag depression

When a fully penetrated weld pool is well established, the weld pool depression will be significantly increased owing to the free surface on the backside. The size of the bottom surface of the pool plays a fundamental role in balancing the forces of the plasma impact and surface tension, which is directly

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Backside bead width (mm)

9 8 7 6 5 4 3 2 1 0

0

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Top surface depression (mm)

8.12 Penetration state versus pool depression.

Set-point h0, b0 Adaptive controller Welder

Manipulator

Torch

Laser source

Feedback h, b Image processing Frame grabber Camera with filter

Metal

8.13 Experimental system diagram for weld sag depression monitoring.

related to the surface curvature. Thus, the surface depression and bottom size of the weld pool are closely correlated, as shown in Fig. 8.1225. It is possible to use the weld pool depression to estimate the backside width of the weld pool13,14. The experimental setup for this weld sag depression monitoring is shown in Fig. 8.1313. The sensor, which consists of the laser, lens, and camera, is

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Real-time weld process monitoring h = S/b

b

H S bb

8.14 Fusion parameters of a cross-viewed fully penetrated weld pool.

X

(a , y1)

(b , y2) l2 (x)

l1 (x) c2 (x, x2) Y

8.15 Geometric illustration for sag parameter.

attached to and moves with the torch. A laser stripe is projected on the sag behind the weld pool in order to increase the contrast of the weld sag over the bright welding arc. The sag is captured by a fast response camera. The measurement accuracy of the sag geometry is determined by the image signal-to-noise ratio, camera resolution, and processing algorithm. Typically, the sag geometry is characterized by the parameters shown in Fig. 8.14, where H is the sag depression depth, S the cross-section area of the sag depression, b the sag width, h the average sag depression, which is defined as the ratio h = S/b, and bb the backside bead width. It was observed that when H and b are utilized to describe bb, no adequate relationship can be found. On the other hand, the average sag depression depth, defined by h = S/b, has a good linear correlation with the backside bead width. It is possible to determine the backside bead width bb with sufficient accuracy. In order to calculate the sag geometric parameters, the left, middle, and right portion of the sag are fitted to a linear, a quadratic, and a linear model, respectively. Suppose the models are l1(x) (x ≤ a*), c2(x, x2) (a* < x < b*), and l2(x) (x ≥ a*), respectively, as shown in Fig. 8.15. The points of interception of the models are (a*, y1), and (b*, y2). Denote the straight line that connects the two interceptions as l. Then we can employ the following equation to calculate the sag geometric parameters of interest:

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199 [8.6]

b*

Cross-section area of sag depression S = ∫ (c2 − l *)dx

[8.7]

Average depression area h = S/b

[8.8]

a*

Thus, the sag depression method for sensing weld penetration can be summarized as three steps14: (1) recognition of sag border, (2) sag geometric parameters computation, and (3) weld pool penetration calculation based on sag geometry.

8.7

Plasma charge-based sensors

During plasma arc welding (PAW), when full penetration is established, plasma jet efflux from the backside of the metal piece occurs. Based on the backside efflux plasma charge in PAW, one can detect the weld penetration, resulting in a sensor referred to as efflux plasma charge sensor (EPCS)15. As can be seen in Fig. 8.16, a backing chamber is formed between the workpiece and the detection metal plate, which are electrically isolated. Once the workpiece has been penetrated, the plasma jet exits from the backside and establishes an electrical potential between the workpiece and the detection plate. Otherwise the voltage Ve across the sensing network R and C should be small or zero. Because the amount of efflux plasma is closely related to the penetration, the value of the voltage was regarded as being linearly dependent on the degree of penetration, which

Electrode – Torch

Power supply Plasma jet

+

Backing bar Re

C

Work Efflux plasma Backing gas Insulator Detection plate

8.16 Illustration of efflux plasma charge sensor (EPCS).

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200

Real-time weld process monitoring Tungsten electrode Standoff distance

Weld pool surface Liquid weld pool

Extension distance

Solid metal

Deformation depth

(a) Influence of weld pool deformation on arc behavior Weld pool surface

Liquid weld pool

Solid metal

(b) Ideal behavior

8.17 Arc behavior and weld pool surface.

leads to a continuous version of the EPCS sensing system. This sensor, on the other hand, can be used discretely as well. The property of the voltage Ve showed that the workpiece was assured to be fully penetrated if Ve is greater than a predetermined threshold voltage. Thus, EPCS can give a ‘Yes’ or ‘No’ answer for full weld pool penetration. It is well accepted in the welding community that the arc voltage is proportional to the arc length. Arc voltage can thus be measured to determine the arc length for arc length control, seam tracking, etc. However, this measurement is not suitable for weld pool surface depth measurement where high precision is critical. This is because the trajectories of the electrons in the arc gap are curving and are subject to the minimum voltage principle and the effect of the geometrical shape of the electrodes (thus the shape of the weld pool surface). Figure 8.17(a) shows such a curved electrical arc. The measurement of the depth of the weld pool surface requires measurement of straight distance with an ideal arc behavior shown in Fig. 8.17(b), i.e. the arc behavior is not subject to the effect of the weld pool surface shape. The simplicity and durability of the arc voltage sensor inspired a similar sensor, which behaves ideally like that in Fig. 8.17(b) to measure the weld pool surface in PAW applications16. As stated above, the trajectories of the

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Electrode

– +

Torch nozzle Orifice

Non-transferred plasma arc

–

Pilot arc power supply R

Va

+

C

Main arc power supply

From PC IGBT module

Workpiece

8.18 Non-transferred plasma charge sensor principle.

electrons in welding arcs, i.e. transferred arcs established between the two electrodes, are subject to the minimum voltage principle and the effect of the geometrical shape of the weld pool surface. However, a non-transferred plasma arc can behave ideally like that in Fig. 8.17(b) and not be subject to the effect of the geometry of the weld pool surface (since the weld pool is no longer one of the two arc terminals) and can maintain a straight trajectory. Hence, the system shown in Fig. 8.18 is proposed to integrate the sensing/welding system in PAW. In this system, the main welding current (MWC) is temporarily cut off by the IGBT (isolated gate bipolar transistor) module and a relatively weak pilot arc is established between the tungsten electrode and the orifice during this period. The pilot arc’s plasma is blown out of the orifice by the orifice gas to form a plasma jet connecting the orifice to the weld pool surface. This plasma jet, as a non-transferred arc in relation to the gap between the orifice and the weld pool surface, is not subject to the effect of the geometry of the weld pool surface and can be used to measure the straight distance l from the orifice to the weld pool surface as: l = A/Va − B

[8.9]

where A and B are two constants, Va is the non-transferred arc voltage drop on the measurement capacitor C. This nice relationship is a result of a considerably greater flux of electrons than that of positive ions in the non-transferred arc due to the much higher thermal velocity of electrons. For the typical plasma electron temperature of 5300–7000 K, the mean electron velocity is in the range of 4.5 × 105 to 5.2 × 105 m/s. In the case where the temperatures of electrons and positive ions are both equal to the plasma temperature (i.e. Te = Ti = T) and where the lightest species of positive ion has a mass of approximately 2000 times that of electrons, the typical ion velocity is in the range of 9.0 × 103 to 10.0 × 103 m/s. It is obvious that the flux of electrons greatly exceeds that of

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positive ions. Thus, more electrons approach the bottom side of the pilot arc, i.e. the workpiece, while more positive ions accumulate at the top, i.e. the nozzle, resulting in an electrical field being established at the two ends of the plasma jet until it retards the approaching charged particles, causing a charge balance, which results in a constant voltage source on the measurement RC circuit. Because the non-transferred arc can be regarded as a resistor whose resistance is proportional to the length l of the arc, the voltage Va on the capacitor C is reversely proportional to l. Thus, given that the torch standoff distance L remains constant, the weld pool depth d is solely depicted by the measured arc voltage Va as: d=l−L=

A − B−L Va

[8.10]

However, d would not be accurately measured if the torch standoff L (the distance from the torch to the workpiece) varies. Since L does change both during welding and from application to application, a signal that can reflect the on-line variation of L and is relatively stable is demanded as a reference so that the original arc voltage Va can be compared with it. The resultant relative signal will be less sensitive to the variations in manufacturing conditions which cause L to vary and more accurate as a measurement of the weld pool surface18,19. Arc welding applications can be divided into two modes: keyhole and melt-in welding. In the melt-in mode, where no keyhole is established, the weld pool surface is not supposed to be severely deformed. Because the weld pool surface is not deep, a flat weld pool surface would be an ideal reference to measure the depth of the weld pool surface. For a butt joint, the top surface of the workpiece can be used as the reference surface to measure the reference distance l 0 (Fig. 8.19a). The depth of the weld pool surface d can be obtained via l − l 0:

Torch

Torch Non-transferred plasma Weld pool

Non-transferred plasma

l0

Weld pool

l

d Workpiece

Workpiece (a) Reference signal

(b) Distance to the reference

8.19 Monitoring of weld pool surface using top surface as reference.

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A A V0 −V ΔV − 0 = A a 0a A 0 a Va Va VaVa Va Va

203

[8.11]

where V 0a is the sensor signal measured from the reference surface (Fig. 8.19a) associated with l 0 and Va is the sensor signal measured from a deformed weld pool surface (Fig. 8.19b) associated with l. As V 0aVa only varies in a small range in comparison with ΔVa, the weld pool depth d is considered to be proportional to ΔVa. It can be seen that the key is to establish the reference surface and obtain V 0a. To use the proposed sensor, the main welding current (MWC) must be periodically switched between on and off because the measurement can be accurately made only when the MWC is off. When the MWC is off, the welding arc pressure becomes zero. The weld pool is still subject to the non-transferred plasma jet’s pressure but this pressure is insignificant. As a result, the weld pool surface will oscillate but the oscillation will decay so that the weld pool surface reaches a relatively steady state if the COFFP (current off period) is long enough. Because of the presence of the non-transferred plasma arc, this weld pool surface is not completely flat. However, it is only very slightly deformed and can be used as the reference surface shown in Fig. 8.19(a). To obtain the reference surface and V 0a, the sensor signal is monitored after the MWC is switched off. Theoretically, after the sensor signal becomes constant, the constant sensor signal can be measured as V 0a. Practically, the signal that does not change beyond a threshold can be used as reference V 0a. When the whole thickness of the workpiece is melted, the weld pool can be pierced by the plasma jet to form a small cavity, referred to as the keyhole, through the whole thickness of the workpiece and the plasma jet may reach the bottom of the workpiece. Using the bottom surface of the workpiece as a reference, the distance from the bottom surface of the weld pool can be measured to determine if the keyhole has been established through the workpiece. The robustness of the nontransferred plasma sensor in keyhole welding application can thus be improved by using a stable keyhole as reference, as shown in Fig. 8.20(a), so that the non-transferred plasma charge voltage is determined by l 0 which is now the distance from the orifice to the bottom surface of the workpiece. This voltage is used as the reference V 0a. In Fig. 8.20(b), the non-transferred plasma charge voltage Va is determined by l, the distance from the orifice to the weld pool surface. The distance from the bottom of the weld pool surface to the bottom surface of the workpiece is thus Δl = l 0 − l =

A A Va − Va0 ΔV − = A =A 0a 0 0 Va Va Va Va Va Va

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Torch

Torch

Non-transferred plasma Weld pool

Non-transferred plasma l0

Weld pool l

Δl Workpiece

Workpiece

(b) Distance to the reference

(a) Reference

8.20 Monitoring of weld pool using bottom surface as reference.

where ΔVa = Va − V 0a is referred to as the relative arc voltage. Similarly, since V 0aVa varies in only a small range in comparison with ΔVa, Δl is considered proportional to ΔVa for simplicity. Because the reference is taken on-line and it reflects the in-process variation of the variables that affect sensor accuracy, ΔVa is more accurate and more tolerant of the process turbulences than using Va directly. To establish a stable keyhole, sufficient energy must be supplied to the workpiece, either by increasing the MWC or by reducing the travel speed. Because the surface profile is strongly affected by the plasma arc pressure, which is proportional to the square of the current, it would be simpler to maintain the pressure or the current constant. Hence, it is preferable to reduce the travel speed of the torch to establish the stable reference keyhole whenever it is needed. Further, because the MWC will be maintained constant, its level must be appropriately selected so that the desired weld pool surface can be obtained. The characteristic of keyhole formation can be used to judge if a stable reference keyhole has been established. That is, once the sensor signal Va stops decreasing or starts to decrease slowly (for example at a speed 0.05 V/s or lower), it can be concluded that a stable reference keyhole has been established. There are a variety of other successful weld penetration sensors that can effectively estimate the weld penetration. But the majority of the sensing techniques can be categorized into the aforementioned approaches. Because of its importance and complexity, weld penetration monitoring is still a very active topic in the welding community and attracts a great amount of attention.

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205

Weld penetration control using sensor feedback

It is apparent that manufacturing processes need robust and reliable controls for low cost and high productivity. When the uncertainties and variations in welding conditions are substantial, welding parameters must be adjusted on-line to assure the production of quality welds. Because sufficient penetration is a minimal requirement for the production of quality welds, joint penetration control with sensor feedback has found variety of applications. The following section is devoted to providing an example of control of weld penetration based on the feedback penetration signal. Owing to the highly concentrated arc, PAW can easily melt the metal and form a deep weld pool, sometimes even a keyhole, a small cavity in the molten metal that moves along with the welding torch. It is this keyhole that significantly increases the heat transfer efficiency and hence penetration capability. In order to maintain a proper level of welding current and, thus, an appropriate state of the keyhole, it is critical to monitor the weld penetration status. In this example, a novel PAW process, referred to as a quasi-PAW (Q-PAW) process, is monitored using the integrated welding and efflux plasma charge sensor (EPCS), as demonstrated in Fig. 8.1617. Figure 8.21 shows the corresponding dynamic changes of the weld pool and the keyhole in Q-PAW process. At instant t1, the current is switched from base to peak current (Fig. 8.21a). The depths of the weld pool and the partial keyhole then increase under the peak current (Fig. 8.21b). At t2, the weld pool becomes fully penetrated and the complete keyhole is established through the thickness of the workpiece (Fig. 8.21b). This instant (t2) can be detected using the efflux signal as the instant when the efflux signal exceeds the pre-set threshold. In Fig. 8.21(a), the current is switched from the peak current to the base current right after the establishment of the keyhole is confirmed. In general, the peak current is switched to the base current d seconds (d ≥ 0) after the establishment of the keyhole is confirmed. Denote this instant as t3. Then the peak current duration Tp = t3 − t1. In the case shown in Fig. 8.21(a), the delay d = 0 and t3 = t2. Hence, in Fig. 8.21(a), Tp = t2 − t1. In the proposed quasi-keyhole process, the current is switched back to the peak current Tb seconds after t3, where the base current duration Tb is a pre-programmed fixed parameter. Denote this instant as t4. Assume that the keyhole is confirmed again at t5 and the current is switched to the base current at t6 = t5 + d. It is evident that t4 is the t1, t5 is the t2, and t6 is the t3 for the succeeding new pulse cycle. If t1, t2, and t3 are denoted as t1(k), t2(k), and t3(k), t3 − t1 as Tp(k), t4 − t3 as Tb(k), the peak current between t1 and t3 as Ip(k − 1) (determined in the last cycle before t1), and the base current between t3

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Real-time weld process monitoring Current Keyhole signal

Current

Period k+1

Period k

Period k–1 Ip(k) Ip(k–1)

Tb(k)

Tp(k)

Tp(k+1)

Tb(k+1)

Ib d

d t1

t4

t2 t3

t1(k) t2(k) t3(k)

t5 t6

t1(k+1) t2(k+1) t3(k+1)

(a) Time sequence Plasma torch

Plasma torch

Plasma jet

Workpiece

t = t1

Plasma jet Partial keyhole Weld pool

Weld pool

Workpiece

t1 < t < t2

Plasma torch

Plasma jet Keyhole

Workpiece

t = t2

(b) Keyhole and weld pool development

8.21 Signal waveform and dynamic development in quasi-keyhole process.

and t4 as Ib(k), then t4 can be denoted as t1(k + 1), t5 as t2(k + 1), and t6 as t3(k + 1). Further, Tp(k + 1), Tb(k + 1), Ip(k) and Ip(k + 1) can be defined accordingly. In this way, the process to be controlled is defined as a system whose control variable is uk = Ip(k) and whose output is yk = Tp(k). (For the convenience of further derivation of control algorithm, u(k) and y(k) are denoted as uk and yk.) The objective is to adjust the control variable and achieve the desired output despite the fluctuations/variations in

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Ip

Tp

207

Quasi-keyhole process

Controller _

8.22 Schematic of non-linear interval controlled quasi-keyhole PAW.

manufacturing conditions. The proposed feedback control system diagram is shown in Fig. 8.22. Because the welding process is generally a non-linear system with uncertainties/variations/fluctuations, non-linear control can outperform its linear competitor in certain applications. This example uses a non-linear interval model control algorithm as the controller to control Q-PAW. For the non-linear interval model controlled Q-PAW process, the system can be modeled as:

{

}

yk = a0 + a1uk − 1 + a2 uk − 2 yk − 1 + a3uk − 3 yk − 2 1373.5 < a0 < 2506.6, − 2.8 < a1 < −1, − 0.7 < a2 < −0.2, − 0.3 < a3 < 0

[8.13]

The units of y and u, millisecond (ms) and ampere (A), define the units of aj(j = 0, 1, . . . 3) in the model; for example, ms is the unit for a0, ms/A for a1, and 1/A for a2 and a3. It can be seen that, except for a0, all other parameters in the model are negative. This is in accordance with the fact that a large peak current uk−1 and high initial temperature, at which uk−1 starts to apply, reduce the time yk necessary to establish the keyhole. Further, a0 must be positive so that the peak current duration is not negative. Hence, the resultant model is in accordance with the physics behind the process to be controlled. For the convenience of the control algorithm calculation, it is preferred that a positive change of control action results in a positive steady-state change in the output. Hence, u˜ = −u and ãj = −aj (j = 1, 2, 3) are used as the new control variable and new parameters, and the model becomes yk = a0 + ( −a1 )( −uk −1 ) + ( −a2 )( −uk −2 ) yk −1 + ( −a3 )( −uk −3 ) yk −2 = a0 + a1uk −1 + a2uk −2 yk −1 + a3uk −3 yk −2

[8.14]

As a result, Δyk+1 = ã1Δu˜k + ã2 [u˜k−1yk − u˜k−2yk−1] + ã3 [u˜k−2yk−1 − u˜k−3 yk−2] = ã1Δu˜k + ã2 [ykΔu˜k−1 + u˜k−2Δyk] + ã3 [yk−1Δu˜k−2 + u˜k−3 Δyk−1]

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208


where Δyk+1 = yk+1 − yk and Δu˜k = u˜k − u˜k−1. Hence, yk+1 = yk + ã1Δu˜k + ã2[ykΔu˜k−1 + u˜k−2Δyk] +ã3[yk−1Δu˜k−2 + u˜k−3Δyk−1]

[8.16]

The bounds of yk+1 can thus be calculated: max yk+1 |Δu˜k = yk + maxã1Δu˜k + maxã2[ykΔu˜k−1 + u˜k−2Δyk] + maxã3[yk−1Δu˜k−2 + u˜k−3Δyk−1]

[8.17a]

min yk+1 |Δu˜k = yk + minã1Δu˜k + minã2[ykΔu˜k−1 + u˜k−2Δyk] + minã3[yk−1Δu˜k−2 + u˜k−3Δyk−1]

[8.17b]

Using the resultant max yk+1 |Δu˜k and min yk+1 |Δu˜k, the bounds of yk+2 under the assumption of u˜k+1 = u˜k can then be calculated: max yk+2 |Δu˜k,Δu˜k+1=0 = maxyk+1 + maxã1Δu˜k+1 + maxã2[yk+1Δu˜k + u˜k−1Δyk+1] + maxã3[ykΔu˜k−1 + u˜k−2Δyk]

[8.18a]

min yk+2 |Δu˜k,Δu˜k+1=0 = minyk+1 + minã1Δu˜k+1 + minã2[yk+1Δu˜k + u˜k−1Δyk+1] + minã3[ykΔu˜k−1 + u˜k−2Δyk]

[8.18b]

Similarly, the bounds of future outputs under u˜k+j = u˜k (∀j > 0)can be recursively calculated up to instant k + m until the output settles at its steadystate value: max yk+m |Δu˜k,Δu˜k+j(∀j > 0) = maxyk+m−1 + maxã1Δu˜k+m−1 + maxã2[yk+m−1Δu˜k+m−2 + u˜k+m−3Δyk+m−1] + maxã3[yk+m−2Δu˜k+m−3 + u˜k+m−4Δyk+m−2] [8.19a] min yk+m |Δu˜k,Δu˜k+j(∀j > 0) = minyk+m−1 + minã1Δu˜k+m−1 + minã2[yk+m−1Δu˜k+m−2 + u˜k+m−3Δyk+m−1] + minã3[yk+m−2Δu˜k+m−3 + u˜k+m−4Δyk+m−2] [8.19b] It is found that m = 3 is sufficient for the quasi-keyhole process addressed in this study in that the prediction would not change after three steps ahead provided the assumption u˜k+j = u˜k (∀j > 0). For the non-linear control, the control criterion is max yk+m |Δu˜k,Δu˜k+j = 0(∀j > 0) = y0

[8.20]

where y0 is the desired output set-point. However, the solution for Δu˜k is not analytical. Since max yk+m |Δu˜k,Δu˜k+j = 0(∀j > 0) only varies with Δu˜k for the given previous set of system output and input (yk, yk−1, yk−2, . . . ; u˜k−1, u˜k−2, . . .), Δu˜k can be effectively determined without extensive computation. In this study, max yk+m |Δu˜k=0,Δu˜k+j=0(∀j > 0) is calculated first. If max yk+m |Δu˜k=0,Δu˜k+j = 0(∀j > 0) is smaller (larger) than y0, Δu˜k will be positive (negative). Then the magnitude of Δu˜k can be increased (decreased) gradually so that

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(8.15) is satisfied in a given accuracy. After Δu˜k is determined, uk is calculated as follows: uk = −u˜k = −(u˜k−1 + Δu˜k)

[8.21]

The following is the procedure for the computation of the non-linear interval control algorithm and the control process. •

• • • • • • • • • • • • •

Step 1: Let k = 0 and u˜i = u˜initial(i = 0, . . . , I − 1) where u˜initial is the initial input for the initial open-loop period (cycle 1 through cycle I which is set to be 3 in this study) Step 2: k = k + 1 Step 3: Output uk−1 = −u˜k−1 Step 4: Measure yk Step 5: k < I − 1? If yes, go to Step 2 Step 6: Δu˜k = 0 Step 7: Calculate max yk+m |Δu˜k,Δu˜k+j=0(∀j > 0) and e0 = y0 − max yk+m |Δu˜k,Δu˜k+j = 0 (∀j > 0) using Equations (8.18a), (8.18b), (8.19a) (8.19b), and (8.20) Step 8: If |e0| < ε (ε > 0 is the maximally permitted output error), go to Step 12. Otherwise, δ = 1 A if e0 > 0 or δ = −1 A if e0 < 0 Step 9: Δu˜k = Δu˜k + δ Step 10: Calculate e1 = y0 − max yk+m |Δu˜k,Δu˜k+j=0(∀j > 0) Step 11: If e0e1 > 0, go to Step 9 Step 12: u˜k = u˜k−1 + Δu˜k Step 13: Stop welding? If no, go to Step 2 Step 14: Stop welding

It can be seen that the amplitude of change for Δu˜k is 1 A, which is considered a sufficient resolution for the peak current. Figure 8.23 shows an experimental result of using the non-linear interval model control algorithm to weld a 6.7 mm thick stainless steel (type 304) with a single pass.

8.9

Summary

This chapter introduces methods for the monitoring of weld penetration. Weld penetration is a critical factor in welding applications to determine weld quality and strength. It denotes the extended depth of the weld fusion line below the weld surface. Owing to the difficulty of monitoring the bottom surface of the weld pool, a number of sensing techniques utilize the top side of the weld pool to estimate the weld penetration. This chapter provides the majority of these approaches. It also introduces an example that uses the sensor signal to feedback control weld penetration.

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Peak current (A)

7000 6000 5000 4000 3000 2000 1000 0

0

20

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60 Weld cycle

80

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120

0

20

40

60 Weld cycle

80

100

120

(a) 215 210 Peak current duration (A)

210

205 200 195 190 185 180 175

(b)

(c)

8.23 Non-linear interval model control experiment on 6.7 mm thick plate: (a) output; (b) input; (c) backside.

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References

1 renwick r.j. and richardson r.w. (1983). Experimental investigation of GTA weld pool oscillations. Welding Journal, 62(2): 29s–35s. 2 xiao y.h. and den ouden g. (1990). A study of GTA weld pool oscillation. Welding Journal, 69(8): 293s–298s. 3 xiao y.h. and den ouden g. (1993). Weld pool oscillation during GTA welding of mild steel. Welding Journal, 72(8): 428s–434s. 4 nagarajan s., banerjee p., chen w.h. and chin b.a. (1992). Control of the welding process using infrared sensors. IEEE Transactions on Robotics and Automation, 8(1): 86–93. 5 banerjee p., et al. (1995). Infrared sensing for on-line weld shape monitoring and control. ASME Journal of Engineering for Industry, 117: 323–330. 6 wikle h.c. iii, zee r.h. and chin b.a. (1999). A sensing system for weld process control. Journal of Materials Processing Technology, 89–90: 254– 259. 7 wikle h.c., kottilingam s., zee r.h. and chin b.a. (2001). Infrared sensing techniques for penetration depth control of the submerged arc welding process. Journal of Materials Processing Technology, 113(1–3): 228– 233. 8 carlson n.m., et al. (1992). Ultrasonic NDT methods for weld sensing. Material Evaluation, 50(11): 1338–1343. 9 graham g.m. and ume i.c. (1997). Automated system for laser ultrasonic sensing of weld penetration. Mechatronics, 7(8): 711–721. 10 hopko s.n. and ume i.c. (1999). Laser generated ultrasound by material ablation using fiber optic delivery. Ultrasonics, 37(1): 1–7. 11 mi b. and ume i.c. (2004). Three-dimensional ray tracing of laser ultrasound for weld penetration sensing. Journal of the Acoustical Society of America, 115(4): 1565–1571. 12 chen s.b., lou y.j., wu l. and zhao d.b. (2000). Intelligent methodology for sensing, modeling and control of pulsed GTAW: Part I – Bead-on-plate welding. Welding Journal, 79(6): 151S–163S. 13 zhang y.m., et al. (1993). Determining joint penetration using weld-face geometry, Welding Journal, 72: 463s–469s. 14 zhang y.m. and kovacevic r. (1997). Real-time sensing of sag geometry during GTA welding. ASME Journal of Manufacturing Science and Engineering, 119(2): 151–160. 15 zhang y.m. and liu y.c. (2003). Modeling and control of quasi-keyhole arc welding process. Control Engineering Practice, 11(12): 1401–1411. 16 lu w., zhang y.m. and emmerson j.e. (2004). Sensing of weld pool surface using non-transferred plasma charge sensor. Measurement Science and Technology, 15(5): 991–999. 17 lu w., zhang y.m. and lin w-y. (2004). Nonlinear interval model control of quasikeyhole arc welding process. Automatica, 40: 805–813. 18 lu w., zhang y.m. and emmerson j. (2007) Adaptive nontransferred plasma charge sensor and its applications, Journal of Manufacturing Science and Engineering – Transactions of the ASME, 129: 180–189. 19 lu w. and zhang y.m. (2006). Robust sensing and control of the weld pool surface. Measurement Science & Technology, 17: 2437–2446.

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20 rokhlin s.i. and guu a.c. (1993). A study of arc force, pool depression, and weld penetration during gas tungsten arc-welding. Welding Journal, 72(8): S381–S390. 21 richardson r.w., gutow d.a., anderson r.a. and farson d.f. (1984). Coaxial arc weld pool viewing for process monitoring and control. Welding Journal, 63(3): 43–50. 22 kovacevic r., zhang y.m. and li l. (1996). Monitoring of weld penetration based on weld pool geometrical appearance. Welding Journal, 75(10): 317s–329s. 23 kovacevic r., and zhang y.m. (1997). Real-time image processing for monitoring of free weld pool surface. Journal of Manufacturing Science and Engineering – Transactions of the ASME, 119(2): 161–169. 24 kovacevic r. and zhang y.m. (1996). Apparatus and method for measuring 3D weld pool shape. U.S. Patent No. 5,481,085. 25 ma y. (2001). High speed image based stochastic analysis of dynamic plasma reflection behaviour. Thesis 2001 (M.S.E.E.), University of Kentucky.

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9 Weld pool surface monitoring C. S. WU, Institute for Materials Joining, Shandong University, China

To ensure high quality and service reliability of welded structures, the implementation of real-time control of arc welding processes is of great practical significance. To this end, the sensing of the weld pool is fundamental to automated welding because the weld pool encodes significant information about the welding process. Vision sensors attached to and moving with the welding torch to conduct frontside sensing of the weld pool surface have been developed and used in a number of applications (Richardson, 1984; Zhang et al., 1993). During the arc welding processes, the weld pool surface is not flat but is deformed (Rokhlin and Guu, 1993; Lin and Eagar, 1983). Thus, the weld pool surface has a 3D profile, which consists of a 2D pool surface boundary and 3D pool surface deformation. The deformation of the weld pool surface is an inherent characteristic of arc welding processes. The observation and study of the weld pool surface can provide information related to the welding process. Furthermore, the pool surface deformation is an important phenomenon in the arc welding process because of its influence on the arc energy distribution, and its correlation with possible weld defects and with the weld penetration (Lin and Eagar, 1983, 1985; Rokhlin and Guu, 1993). Through sensing, the weld pool surface geometry including the boundary and deformation, monitoring and control of weld quality, specifically of weld penetration, can be realized.

9.1

Vision-based sensing 2D surface geometry of weld pool in gas tungsten arc welding

A low-cost vision sensor such as a CCD (charge-coupled device) camera can be used to observe the 2D surface geometry of weld pool from the frontside. The measurement results of 2D weld pool geometry in real time is of significance not only for weld penetration control, but also for 213 WPNL2204

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verification of simulation results of fluid flow and heat transfer in weld pools. The main difficulty encountered in vision-based sensing of weld pool geometry is the strong interference from the arc light. To measure the geometry of a weld pool surface, the influence of the arc light must be overcome first. For pulsed-current gas tungsten arc welding (GTAW), a special sampling sequence makes it possible to measure 2D weld pool geometry at the instant when the welding current is just changed from its peak value to the base level, and the arc light interference is relatively low (Wu and Liu, 1996; Wu et al., 1999). However, traditional non-pulsed, constant-current GTAW is used more widely in manufacturing. To this end, a common commercial CCD camera was combined with a composite light filter to form a vision sensing system of 2D weld pool surface geometry (Gao and Wu, 2001; Wu et al., 2003). This section introduces the modified system and the measuring results.

9.1.1 Experimental system Figure 9.1 shows the block diagram of the experimental system. The major functional elements of the experimental system are an industrial computer for process control, a welding power supply, a welding table, a control unit, a CCD camera with a special filter, an image grabber and a monitor. The main feature of the experimental system is that the common commercial CCD camera is combined with a special narrow-band filter with optimized optical parameters so that the images of the weld pool are captured clearly

CCD camera with filter TIG torch

Power supply

Control unit

Workpiece

Work-table

Monitor

Image grabber

Industrial computer

9.1 Diagram of the system (TIG-tungsten insert gas).

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and the aim of low-cost sensing is achieved. The control unit was developed to control the welding process parameters. Its main functions include receiving the data of welding process parameters from the computer, and controlling welding current and welding speed. The CCD camera has a resolution of 600 TV lines and eight levels of shutter-speed (from 1/60 s to 1/1000 s). A specially designed and manufactured narrow-band filter (central wavelength 610 nm, half band width 10 nm, and transparency 27%) is assembled on the CCD camera to provide a clear image of the weld pool. The image frame grabber has an image capturing and transforming speed of 30 frames per second and a grayness resolution of 1/256. For this system, the CCD camera observes the weld pool at a certain angle (30°) behind the torch. Once the image of the weld pool captured by the vision sensor has been digitized through the frame grabber, it is stored in the computer as a matrix in which one element (pixel) represents a dot of image. A series of image processing procedures are carried out, such as eliminating noise, enhancing contrast, and extracting the edges of weld pool (i.e. pool boundary). The image processing algorithm includes two steps: (1) the methods of noise elimination and image enhancement are combined with each other to sharpen the points between the trough and peak in gray level and (2) a heuristic edge tracking method is used to search for edges. Then the weld pool geometry can be obtained by determining the coordinates of the left edge and right edge points. The detailed algorithm and formulae are provided in Gao and Wu (2003). Through setting up the model for image capturing of vision sensor, the real size corresponding to a pixel in the image of the weld pool is determined. After the vision system has been calibrated, the transforming coefficients are obtained as follows: 0.043 mm/pixel in the direction perpendicular to the welding direction and 0.0752 mm/pixel along the welding direction.

9.1.2 Results GTAW experiments were performed under different welding conditions. For each condition, bead-on-plate welding was made with a constant current from the start to the end of the weld. The workpiece material was mild steel (Q235) with dimensions of 200 mm × 50 mm and thickness 2 mm. Argon was used as shielding gas with flow rate of 10 l/min. The diameter of the tungsten electrode was 3.2 mm with angle 90o. The arc length was held at 6 mm. Figures 9.2–9.4 show the measured weld pool surface geometry under the conditions of different levels of welding current and travel speed. In these figures, (a) is the raw image of weld pool, captured directly by the CCD camera, (b) is the weld pool image after processing by the algorithm, and

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(a)

(b)

18 16 14 12 10 8 6 4 2 0

0

2

4 6 8 10 x (mm)

(c)

y (mm)

9.2 The weld pool geometry at welding current 100 A and travel speed 180 mm/min: (a) raw image, (b) processed image, (c) weld pool boundary obtained.

(a)

(b)

18 16 14 12 10 8 6 4 2 0

0

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(c)

y (mm)


(a)

(b)

18 16 14 12 10 8 6 4 2

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(c)


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(c) is the weld pool boundary after transformation from image to the real dimensions based on transforming coefficients of 0.043 and 0.075 mm/pixel. It can be seen that the system accurately measures the whole weld pool geometry.

9.2

Sensing 2D weld pool boundary in gas metal arc welding

During gas metal arc welding (GMAW) process operation, there are very strong interferences from arc light, metal transfer, spatter, and other sources. Thus, it is very difficult to measure or observe weld pool geometry in GMAW. Until now little work has been done in this field compared with GTAW. A high-shutter-speed camera assisted by a pulsed laser is used to capture the weld pool image in GMAW, and the pool boundary is extracted using the developed image processing algorithm (Wu et al., 2004a). The measured data of weld pool geometry could lay a foundation for correlating GMAW weld pool geometric parameters to the weld penetration in order to control penetration in GMAW.

9.2.1 Experimental procedure The experimental system consists of an inverter power supply for GMAW, a 3D welding worktable for achieving the required travel speed and trajectory, a laser with optical fiber, camera, monitor, frame grabber, and control computer (Wu et al., 2004a). The camera is positioned directly behind the weld pool at an angle of 30°. The laser is projected on the weld pool through the optical fiber. The frame grabber digitizes the video signal at a speed of 30 frames per second and a grayness resolution of 1/256. To overcome the strong interference from the arc light in GMAW, a high shutter-speed camera assisted with a pulsed laser is used (Hoffman, 1991). The pulse of the laser lasts only 5 ns. The shutter of the camera is synchronized with the laser pulse. Although the average power of the laser is only 7 mW, its peak power during the pulse period reaches 70 kW. During the pulse duration, the intensity of the laser illumination is much stronger than that of the GMAW arc and hot metal. Thus, the arc light interference can be efficiently minimized when the weld pool image forms in the camera. Besides, there is a narrow-band filter compatible with the laser wavelength in the optics of the camera so that the ratio of signal to noise of the image is further improved. Consequently, clear images of the weld pool can be acquired during the GMAW process. Bead-on-plate welding experiments were conducted on mild steel (Q235) of dimensions 200 mm × 50 mm × 6 mm. The welds were made using direct

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Real-time weld process monitoring y

x

9.5 The captured image of GMAW weld pool.

current electrode positive (DCEP), the electrode diameter was 1.2 mm, the wire extension was 20 mm, and the shielding gas was 80% Ar + 20% CO2 with a flow rate of 12 l/min.

9.2.2 Results Figure 9.5 shows the image captured by the system. In the captured images, the pool surface exhibits mirror-like reflection where the solid surface of the workpiece diffusely reflects the projected laser, so the weld pool will be dark. Owing to the interaction of brightness of adjacent domains, the weld pool edges locate among points with big variation of gray levels. Because of the inherent characteristics of the GMAW process, there are disturbing noises in the captured image. This makes it difficult to extract weld pool edges. A special image processing software has been developed to solve this problem, which includes the following steps (Wu et al., 2004a): 1

2

3

4

Pre-processing. The algorithms of noise elimination and image enhancement are combined with each other to sharpen the points with big variation of gray levels. Searching for the center point. Since the weld pool has a maximum width and length, the center point of the weld pool is determined first. Determining the domain points. Four domain points (left, right, front, and rear) are determined at points with maximum width and length of weld pool. Searching for edges. The left and right domain points are taken as the starting point. Within 30 pixels around the starting point, the left and right edges of weld pool are determined according to the characteristic of sudden change of gray levels. Then the center point is taken as the starting point, and searching is conducted along both sides in the

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(b)

9.6 The GMAW weld pool image: (a) the raw image; (b) the processed image.

welding direction. The front and rear edges of weld pool are determined within the front and rear domain points. Figure 9.6 shows the original and processed images of the weld pool in the GMAW. After extracting the edge points of the weld pool images, the coordinates of these edge points are obtained and stored in an array. Because the actual size of the weld pool is different from the size of the digital image, calibration must be carried out. The real coordinates depend on the focal length of the camera lens, the angle of view and the distance from the camera and the weld pool. Because the distance from the camera to the weld pool is 300 mm, the view of angle is kept at 30° and the real dimension of the weld pool is comparatively small, so the coefficients transforming the pixel in the image into the real size are approximated to constants. During the calibration, a picture with known dimensions was put at the place where a weld pool forms during welding process. Then the camera captured the image, and the computer determined the dimension represented by a pixel. In the system, the camera is treated as a pinhole model, thus along the x direction (perpendicular to the weld centerline), a pixel represents 0.0521 mm and along the y direction (parallel to weld centerline), a pixel represents 0.1316 mm. Figure 9.7 shows the measurements of the GMAW weld pool image. The coordinates of the weld pool edges have been transformed into real dimensions so the geometric parameters of the weld pool can be easily obtained. Table 9.1 lists the measured width and length data of the weld pools for a few welding conditions. Based on these results, it is evident that welding process parameters have a significant influence on weld pool geometry in the GMAW. If the roles of welding process parameters in determining the weld pool geometry are known quantitatively, the correlation between the weld penetration and the weld pool geometric parameters in GMAW will be understood.

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(a)

(b) 10 x (mm)

5 0 –5 –10 –20 –15 –10 –5

0

5

10

y (mm) (c)

9.7 The measured GMAW weld pool. Welding current 120 A, arc voltage 20 V, and travel speed 135 mm/min: (a) raw image; (b) processed image; (c) the weld pool edge.

Table 9.1 The measured width and length of GMAW weld pool Welding current (A)

Arc voltage (V)

Travel speed (mm/min)

Pool width (mm)

Pool length (mm)

100 110 110 100

20 20 20 22

120 120 140 120

9.17 10.15 9.64 10.46

22.14 24.86 23.0 21.01

9.3

Modeling and simulation of weld pool surface

During arc welding processes, the weld pool surface is not flat but is deformed (Lin and Eagar, 1983; Rokhlin and Guu, 1993). Therefore, the pool surface is three-dimensional. The surface of a full-penetration weld pool that is facing the welding torch is referred to as the front free surface, whereas the surface on the opposite side is called the back free surface. When the workpiece is fully penetrated, both the front and back free

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surfaces of the weld pool become curved as a result of different forces. The deformation of each free weld pool surface is defined as the deviation from its flat position. The front free surface profile has an effect on the arc heat flux and pressure distribution, which directly influence the fluid flow and heat transfer inside the weld pool (Weiss et al., 1996). On the other hand, the profile of the free surface deformation gives information about the extent of penetration depth, and some weld defects such as undercut. The surface deformation magnitude helps to determine whether the weld pool has broken or burn-through has occurred. Thus, modeling and simulation of the surface deformation of the weld pool are highly significant to monitoring of the welding process. Although there have been significant advances in the numerical simulation of the GTAW process (Kou and Wang, 1986; Zacharia et al., 1989; Choo et al., 1992; Wu and Yan, 2004; Wu et al., 2004b), little attention has been paid to simultaneously calculating both front and back free surface deformation in a full-penetration weld pool. In this section, the consistency and coupling deformation equations are employed to determine both front and back free surface profiles of a full-penetrated weld pool. During the transient development of the weld pool, its volume varies with time until the quasi-steady state has been achieved, and the weld pool evolves from partial penetration to full penetration.

9.3.1 Surface deformation of a partially penetrated weld pool For a partially penetrated weld pool in GTAW, there is only the front free surface. A static balance of forces on the front free surface is considered, and the motion inside the weld pool is ignored. As shown in Fig. 9.8, ϕ(x, y) is the configuration function of front free surface of weld pool, while h(x, y) is the configuration function of the melt–solid interface. The profile of the front free surface of the weld pool will form a shape which minimizes the total energy inside the weld pool subject to the constraint on mass conservation. The free surface shape ϕ(x, y) can be obtained from the following equation (Wu et al., 2007): pa − ρgϕ − λ = − γ

(1 + ϕ 2x )ϕ yy − 2ϕ x ϕ y ϕ xy + (1 + ϕ 2y )ϕ xx 32 (1 + ϕ 2x + ϕ 2y )

[9.1]

where Pa is the arc pressure, ρ is the density of liquid metal, g is the acceleration of gravity, λ is Lagrange multiplier,γ is the surface tension, and ϕxx = ∂2ϕ/∂ x2, ϕyy = ∂2ϕ/∂ y2, ϕxy = ∂2ϕ/∂ x∂ y. The Lagrange multiplier λ is determined by the constraint

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Real-time weld process monitoring y x j (x,y)

H

h (x,y) Δ

z

9.8 Schematic sketch of the cross-section of non-penetrated weld pool.

∫∫ϕdx dy = 0

[9.2]

Ω

where Ω denotes the 2D domain for the front free surfaces of the weld pool. Equation (9.2) means the volume of a partially penetrated weld pool is constant if no filler metal is added.

9.3.2 Front and back surface deformation of a fully penetrated weld pool For a fully penetrated weld pool, there are two free surfaces, i.e. the front and back free surfaces. As shown in Fig. 9.9, ϕ(x, y) is the configuration function of the front free surface of the weld pool, Ψ(x, y) is the configuration function of the back free surface of the weld pool, h(x, y) is the configuration function of the melt–solid interface, and L is the workpiece thickness. To minimize the total energy inside the fully penetrated weld pool, two non-linear partial differential equations are used (Wu et al., 2007). For the front free surface: pa − ρgϕ − λ = − γ

(1 + ϕ 2x )ϕ yy − 2ϕ xϕ yϕ xy + (1 + ϕ 2y )ϕ xx 32 (1 + ϕ 2x + ϕ 2y )

[9.3]

and for the back free surface: ρg ( ψ + L) + λ = − γ

(1 + ψ 2x )ψ yy − 2ψ x ψ y ψ xy + (1 + ψ 2y )ψ xx 32 (1 + ψ 2x + ψ 2y )

[9.4]

where ψxx = ∂2ψ/∂ x2, ψyy = ∂2ψ/∂ y2, ψxy = ∂2ψ/∂ x∂ y. The Lagrange multiplier λ is determined by the mass conservation constraint:

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x

z

h (x,y)

L

y y (x,y)

223

x

z

9.9 Schematic sketch of the cross-section of a full-penetrated weld pool.

∫ ∫ ϕ d xd y − ∫ ∫ ψd xd y = 0

Ω1

[9.5]

Ω2

where Ω1 and Ω2 denote the 2D domains for the front and back free surfaces of weld pool, respectively. Equation (9.5) means the volume of the fully penetrated weld pool is constant if no filler metal is added. The coupling between ϕ(x, y) and ψ(x, y) is through λ and the restraint condition Eq. (9.5). The arc pressure pa in Eqs. (9.1) and (9.3) is written as (Lin and Eagar, 1986; Yamamoto et al., 2003): pa(r ) = C j

μ0 I 2 ⎛ r2 ⎞ exp ⎜⎝ − 2 ⎟⎠ 8 π 2 σ 2j 2σ j

[9.6]

where μ0 is the magnetic permeability in free space, Cj is the adjusting parameter, I is the welding current, r is the distance from the arc centerline, and σj is the current distribution parameter.

9.3.3 Numerical method for determining the Lagrange multiplier Equations (9.3) and (9.4) are transformed into finite difference equations. In fact, the free surface deformation is coupled with heat and fluid flow fields in GTAW. A special algorithm is employed to solve the surface deformation, fluid flow, and heat transfer in GTAW (Zhao et al., 2005). This section focuses on the calculation of free surface deformation, and the location and extent of the weld pool are known from the preceding temperature calculations.

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For a given λ, the approximations of ϕ(x, y) and ψ(x, y) can be found by solving Eqs. (9.3) and (9.4). But for an arbitrary choice of λ, the restraint condition Eq. (9.5) may not be fulfilled. An evaluation function is defined as ΔV (λ ) =

∫ ∫ ϕ( x, y; λ )dxdy − ∫ ∫ ψ ( x, y; λ )dxdy

Ω1

Ω2

[9.7]

which measures the deviation from the objective of mass conservation. If an appropriate value of λ* is found, then ΔV(λ*) = 0

[9.8]

The secant method (Cai et al., 2005) is used to determine the value of Lagrange multiplier λ. To clarify the calculation procedure, the iterative scheme is briefly stated as follows: 1 Make an initial guess λ1, solve Eqs. (9.3) and (9.4), obtain ϕ and ψ, and compute ΔV(λ1). 2 Make a second guess λ2, solve Eqs. (9.3) and(9.4), obtain ϕ and ψ, and compute ΔV(λ2). 3 For n = 3, 4, 5, . . . , repeat the following steps until |ΔV(λ)| is sufficiently small: (a) Calculate a new value λn by: λ n = λ n −1 − (b)

ΔV (λ n−1 ) ( λ n − 2 − λ n −1 ) ΔV (λ n−2 ) − ΔV (λ n−1 )

[9.9]

Solve Eqs. (9.3) and (9.4) using λn, and compute ΔV(λn).

9.3.4 The overall solution technique A mathematical model has been developed to predict the fluid flow and heat transfer in the weld pool of the GTAW (Zhao et al., 2005). A separated algorithm is employed to solve the surface deformation, fluid flow, and heat transfer in quasi-steady state conditions, i.e. three problems are calculated separately and improved by turns. In this way the strong coupling problems among the surface deformation, fluid flow, and heat transfer are solved successfully. The whole calculation procedure consists of the following main steps: 1 2 3 4 5

conduct the domain discretization and grid system formation; calculate the temperature distribution based on the initial conditions; determine the 3D weld pool geometry based on the temperature profiles; calculate the surface deformation of the weld pool; conduct the coordinate system transformation;

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6 calculate the fluid velocity field inside the weld pool under the bodyfitted coordinate system, and get the convergent results; 7 calculate the temperature field over the whole domain, and get the convergent results; 8 repeat steps (3)–(7), and improve the calculation accuracy until the convergent criterion for the weld pool surface deformation, fluid flow field, and temperature distribution are all met.

9.3.5 Results Numerical simulations are performed for bead-on-plate GTAW on a stainless steel workpiece with dimensions of 200 × 100 × 3 mm3. In this study case, the welding current may be altered, while the arc voltage is 12 V and welding speed is 125 mm/min. Because of the symmetry with respect to the welding direction (x-axis), only half the workpiece is taken as the calculation domain. The thermal physical property parameters of stainless steel (SS304) and other parameters used in the calculation are described in Zhao et al., (2005). Figure 9.10 shows the longitudinal and cross-sections of a fully penetrated weld pool. It can be seen that both the front and back surfaces of the weld pool are deformed under the action of arc pressure, gravity, and surface tension. Since the arc is traveling along the welding direction (x-axis), the maximum depression for both front and back surfaces is behind the arc centerline (x = y = 0). The front surface of the weld pool reaches its maximum at a point (x = −0.25 mm, y = 0), whereas the back surface gets depressed mostly at a point (x = −3.15 mm, y = 0). Figure 9.11 shows a 3D drawing of the front and back surface deformation of a fully penetrated weld pool when the welding current is 115 A. Figure 9.12 demonstrates the influence of the welding current on the surface deformation of a weld pool. In this case the welding current varies from 90 to 120 A with a step increment of 5 A. The temperature calculation results illustrate that under a welding current of 105 A the workpiece is not fully penetrated. From 110 A, it is a completely penetrated weld pool. For non-penetration cases (less than and equal to 105 A), the pool surface under the arc is depressed while that at the rear is humped so that the mass restraint condition is fulfilled. With increasing welding current, the full penetration emerges, the back surface of the weld pool starts to deform, and the hump at the frontside decreases. When the extent of penetration expands at a higher level of welding current, both front and back surface depression get bigger, and the hump at the rear part of front surface disappears. The effect of the welding current on the surface deformation of weld pool is significant. The reasons are as follows: • The arc pressure is directly proportional to the square of the welding current (Lin and Eagar, 1986). As the welding current increases, the arc

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1.0 0.5 0.0 z (mm)

–0.5 –1.0 –1.5 –2.0 –2.5 –3.0 –3.5 –4.0 –8

–7

–6

–5

–4

–3

(a)

–2 –1 x (mm)

0

1

2

3

4

1.0 0.5 0.0

z (mm)

–0.5 –1.0 –1.5 –2.0 –2.5 –3.0 –3.5 –4.0 –4 (b)

–3

–2

–1

0 y (mm)

1

2

3

4

9.10 The calculated weld pool geometry (welding current 115 A, voltage 12 V, welding speed 125 mm/min): (a) longitudinal section of weld pool (y = 0); (b) cross-section of weld pool (x = −1.6 mm).

pressure pa increases much faster, and the higher magnitude of arc pressure results in marked surface depression of the weld pool. • The volume of the weld pool expands with an increase of welding current because a higher welding current is related to greater heat input. Both domains Ω1 and Ω2 become larger, and a larger weld pool is deformed more easily. • A higher welding current results in a larger volume of the weld pool so its weight also increases. All these factors are important, but the arc pressure has the greatest effect on the surface depression.

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0 –1 –2 –3 –16–14 –12 –10

–8

–6 –4 x (m –2 m)

2

–2 4

z (mm)

y

–4

6

m

(m

–6

–6

–4 x (m –2 m)

4

2 0

0 2

(b)

–2 4

–4

6 8

6

)

0

0

8

–8

4

2

(a)

–3 –4 –5 –6 –16–14 –12 –10

227

–6

y

6

)

m

(m

9.11 Three-dimensional surface deformation of weld pool (welding current 115 A, voltage 12 V, welding speed 125 mm/min): (a) front free surface; (b) back free surface.

Figure 9.13 shows the depression volume of the front pool surface under different welding currents. When the workpiece is not penetrated below 105 A, the deformed volume VDF of the front pool surface is zero because the depressed part equals the humped one. The penetration occurs between 105 and 110 A in the study case, so the value of VDF rises slowly with the welding current. After the weld pool has been fully penetrated, the rising rate of VDF suddenly increases, i.e. the slope of the curve VDF vs. welding current increases in a marked way. Because the depression volume of the weld pool at the frontside has the characteristics mentioned above, it can be employed as an indicator of weld penetration extent. In practice, the frontside sensor can be developed to measure the weld pool surface depression for weld penetration control.

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Real-time weld process monitoring 0.2 0.0 –0.2 90 A 95 A 100 A 105 A 110 A 115 A 120 A

–0.4 –0.6 –0.8 –8

–7

–6

–5

–4

–3

Surface deformation (x = 0) (mm)

(a)

–2 x (mm)

–1

0

1

2

3

0.0 –0.2 90 A 95 A 100 A 105 A 110 A 115 A 120 A

–0.4 –0.6 –0.8

–4

–3

–2

–1

0 y (mm)

1

2

3

4

–2

–1

0 y (mm)

1

2

3

4

Surface deformation (x = –1) (mm)

(b) 0.0 –0.2

90 A 95 A 100 A 105 A 110 A 115 A 120 A

–0.4 –0.6

–4

–3

(c) Surface deformation (x = –2) (mm)

0.2 0.0 90 A 95 A 100 A 105 A 110 A 115 A 120 A

–0.2 –0.4 –0.6 –5

(d)

–4

–3

–2

–1

0 y (mm)

1

2

3

4

5

9.12 The surface deformation under different levels of welding current (magnified for deformation magnitude): (a) front surface deformation at longitudinal section (y = 0); (b) front surface deformation at cross-section (x = 0); (c) front surface deformation at cross-section (x = −1 mm); (d) front surface deformation at cross-section (x = −2 mm). WPNL2204


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Depression volume (mm3)

5 4 3 2 1 0 90

95

100

105 I (A)

110

115

120

9.13 The front surface depression volume vs. welding current.

9.4

Sensing 3D weld pool surface using specular reflection

As mentioned above, pool surface deformation can always be expected in arc welding. Measurement of the deformed weld pool surface plays an important role in understanding the physical processes occurring in the weld pool. A possible application of 3D pool surface shape sensing is the monitoring and control of weld quality, specifically of weld penetration. Kovacevic and Zhang (1996, 1997) proposed a novel mechanism for observing the pool surface shape. The sensing system is shown in Fig. 9.14. A LaserStrobe vision system as introduced in Section 9.2 is used to eliminate the arc radiation. A high-speed shutter camera equipped with a pulse laser to view the weld pool through the bright light is employed and a frosted glass-based grid is used to project a laser stripe in order to image the mirrorlike liquid pool surface. To obtain information about the 3D weld pool surface, a special technique must be implemented. The common practice for determining the 3D shape of a surface is to project a structured light on the surface and sense the diffuse reflection of the structured light. The shape information can then be extracted from the deformation of the structured light. However, the weld pool surface is smooth and no substantial amount of structured light can be reflected diffusely. It appears that only specular reflection can be utilized. In the case of specular reflection, the observed scene is the virtual image of the original object (Pedrotti and Pedcotti, 1993). The relationship

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Real-time weld process monitoring Image Laser strobe TIG welding controller computer capturing card supply

Speed controller Laser

Computer

Torrch

Fiber optical cable

Frosted glass and grid

P60 camera

Travel direction

9.14 The schematic diagram of sensing system for 3D weld pool surface shape.

P60 camera

S

Incident rays Grid Reflected rays Weld pool surface

S′ (Image of S) (a)

(b)

9.15 Specular reflection of the weld pool surface with conventional sensor: (a) optical principle and (b) image. The virtual image is a spot in this case. The sensed specular reflection is a bright spot as shown in (b) and contains no information on the weld pool surface.

between the original and its virtual image is determined by the shape of the mirror surface. Usually, structured light is formed by projecting a laser through a grid. The sole function of the grid is to form the structured light stripe. As the light passes through the grid, its direction of travel is unchanged (Fig. 9.15a). This causes the virtual image of a laser point source

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S3

231

S2 P60 camera S1

Incident rays Reflected rays

Frosted glass and grid

Weld pool surface

S3′ (Image of S3)

S1′ (Image of S1) S2′ (Image of S2) (a)

(b)

9.16 The sensing principle and the acquired images: (a) the sensing principle for specular reflection and (b) the sensed laser strips.

to be a single spot despite the shape of the mirror-like surface. Thus, a bright spot as shown in Fig. 9.15(b) is sensed by the camera, and no shape information of the reflection surface is contained in the image. To acquire the shape information, a novel incident mechanism of structured light (as shown in Fig. 9.16) is proposed (Kovacevic and Zhang, 1996). The proposed incident mechanism of structured light is realized through a specialized grid. This grid consists of a common grid and frosted glass. When the laser is projected on the frosted glass, the laser travel direction will be changed (see Fig. 9.16). From the viewpoint of light travel, any point on the frosted glass can be considered as a new point light source which disperses light with a certain diffuse angle (see Fig. 9.16). The camera views the slits (grid openings) through their reflection from the weld pool surface. Their virtual image consists of bright stripes deformed by the weld pool surface deformation (Fig. 9.16) and is sensed by the camera. The surface shape of the weld pool is contained in the acquired images. The resultant images are shown in Fig. 9.17. It has been shown by the experiments that the reflected stripes are straight when a flat mirror is substituted for the weld pool. When a convex (concave) mirror is employed, the reflected stripes are convex (concave) as well. The pattern of the reflected stripes describes the reflection surface. Thus, the geometry of the reflected stripes contains the weld pool surface information. After a series of image processing, the strip edges and pool boundary can be obtained, as shown in Fig. 9.17. Using these data, the free weld pool surface can be reconstructed based on the reflection law (Kovacevic and Zhang, 1996).

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(a)

(b)

9.17 An example of sensing of 3D weld pool surface: (a) acquired original image (b) processed image.

9.5

Improvement on observation of dynamic 3D weld pool surface

The aforementioned method is capable of obtaining specular reflection from the weld pool surface under the presence of the bright arc. However, the synchronization of the laser and high-speed shutter requires specific, high-cost, and sophisticated equipment (the LaserStrobe system) and is not suitable for use in production. More critically, to suppress the arc, the power of the laser during the pulse must be very high (70 kW for images shown in Fig. 9.17) and the frame rate can thus not be very high (30 frames per second for images in Fig. 9.17) because a high frame rate would increase the average power of the illumination laser. Such a low frame rate cannot sense the dynamic variation of 3D weld pool surface in real time. Recently, the Welding Research Laboratory at the University of Kentucky has proposed a novel approach to measure the specular weld surface using a low-power illumination laser at a high frame rate (Zhang et al., 2006). It is introduced as follows.

9.5.1 Proposed observation system The proposed sensing system is shown in Fig. 9.18. To observe the 3D shape of a weld pool surface in GTAW, a 20 mW illumination laser at a wavelength of 680 nm with variable focus is used to generate multiple lines. The project fan angle is 5° and the interline angle is 0.23°. The laser lines are projected on the weld pool under the torch electrode. An imaging plane is fixed coaxially to the laser at a known distance from the electrode. To minimize the

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Z Image GTAW Torch

Laser diode

Reflected laser lines High-speed camera O Imaging plane

Weld pool Structured laser lines

Y

Workpiece

X

9.18 Schematic of the measuring system.

9.19 Example of reflected laser lines on imaging plane.

influence of the arc, the camera observes the imaging plan with a band pass filter of 10 nm band width centered at wavelength of 680 nm. Although the illumination laser is continuous and very low power (20 mW) in comparison with that of the arc, clear images as shown in Fig. 9.19 are obtained under the presence of bright arcs. This is because the system shown in Fig. 9.18 takes advantage of the difference between propagation in illumination laser and arc plasma. In fact, due to the coherent and unidirectional property of the laser light waves, the laser travel direction remains unchanged and its intensity or power loss over the distance traveled by the laser light is insignificant. In addition, the specular surface of the weld pool reflects nearly all the intensity of the projected illumination laser lines. On

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the other hand, the arc light intensity decreases exponentially with distance. Hence, if the imaging plane is placed reasonably far from the torch, the intensity of the laser light falling on it will be much stronger than that of the arc light. As can be seen from Fig. 9.19, the reflected light falling on the image plane is distorted and shaped by the specular reflection surface of the weld pool. The reflection law determines distortion/shaping, and can be used to calculate the weld pool surface. The image plane can be as simple as a sheet of paper attached to a transparent glass as long as the laser light falling on the image plane is also visible to the camera from the opposite side. In the following experiments, an Olympus i-Speed high-speed camera capable of taking up to 33 000 frames per second is used to observe the image plane with the band pass filter centered at the wavelength of the laser, which blocks most of the arc light that falls on the imaging plane and allows viewing of light only in the wavelength range of the laser light.

9.5.2 Experimental results In the experiment, 2 mm thick mild steel sheet was used as the workpiece. The welding current was kept at 65 A and the welding speed changed from 2 to 3 mm/s at point C in Fig. 9.20 which shows the welded workpiece. In the initial segment of the experiment from point A to point B, the weld pool grows to a state of stable full penetration. While the image at point A is a blank plane without any lines, the image acquired at point B is shown as the first image in Fig. 9.21. In the segment B to C in Fig. 9.20, the welding process is at a steady state of full penetration under a welding speed 2 mm/s. The patterns of the reflected lines vary only slightly (due to the oscillation of the weld pool surface) as can be seen in Fig. 9.21, where the first, second, and third images are acquired at point B, the middle point between points B and C, and point C, respectively.

Topside view

A

B

C

D

E Backside view

9.20 Weld sample.

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9.21 Images of reflected laser stripes in segment BC.

9.22 Images of reflected laser stripes in segment CD.

After the welding speed is increased to 3 mm/s at point C, the area of the weld pool on the topside (the side aimed at by the torch) of the workpiece decreases. The surface changes its shape and becomes shallower, or changes from being concave toward being convex. The depth of the weld pool also reduces because of the reduced heat input so that the backside width of the weld pool reduces in the segment CD and eventually becomes zero at point D (i.e. the penetration state changes from full to partial or the depth of the weld pool reduces to less than the thickness of the workpiece). Hence, segment CD is a transition period. Figure 9.22 shows two images acquired during this transition period. As can be seen, the images during this period become smaller due to the reduced area of the specular (reflection) weld pool surface (see the topside of the welded workpiece in Fig. 9.20). At point D through the end of the weld, the joint penetration is partial and the weld pool surface on the topside becomes significantly convex. The first image shown in Fig. 9.23 was acquired at point D and other two images were acquired during segment DE. As can be seen, although the area of the specular (reflection) surface becomes smaller, the images are larger than those acquired during the full penetration periods in Fig. 9.21. The change of the weld pool surface from being concave or insignificantly convex to being significantly convex is responsible for this phenomenon because the weld pool surface must be convex when the penetration is

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9.23 Images of reflected laser stripes in segment DE.

partial but concave if a significant full penetration is established; the convex degree is insignificant if the full penetration is not significant. In addition, Fig. 9.23 shows that the number of the laser lines is reduced from five to four. This indicates that the length of the weld pool has been reduced. With these measured deformed laser lines, the weld pool surface deformation can be determined so that a 3D weld pool surface profile may be obtained after image processing, modeling, and identifying.

9.6

References

cai x, jeberg p v and langtangen h p (2005), ‘A numerical method for computing the profile of weld pool surface’, Int. J. Comput. Methods Eng. Sci. Mech., 6, 1–12. choo r t c, szelely j and david s a (1992), ‘On the calculation of the free surface temperature of gas-tungsten-arc weld pools from first principles: part II modeling the weld pool and comparison with experiments’, Metall. Trans. B, 23(6), 371–384. gao j q and wu c s (2001), ‘Experimental determination of weld pool geometry in gas tungsten arc welding’, Sci. Technol. Welding Joining, 6(5), 288–292. gao j q and wu c s (2003), ‘Neurofuzzy control of weld penetration in gas tungsten arc welding’, Sci. Technol. Welding Joining, 8(2), 143–148. hoffman t (1991), ‘Real-time imaging for process control’, Adv. Mater. Proc., 140(3), 37–43. kou s and wang y h (1986), ‘Computer simulation of convection in moving arc weld pools’, Metall. Trans. A, 17(12), 2271–2277. kovacevic r and zhang y m (1996), ‘Sensing free surface of arc weld pool using specular reflection: principle and analysis’, Proc Instn Mech Engrs, Part B: J. Eng. Manufacture, 210, 553–564. kovacevic r and zhang y m (1997), ‘Real-time image processing for monitoring of free weld pool surface’, ASME J. Manufacturing Sci. Eng., 119, 161–169. lin m l and eagar t w (1983), ‘Influence of surface depression and convection on arc weld pool geometry’, in Chen M M, Mazumder J and Tucker III C L, Transport Phenomena in Material Processing, American Society of Mechanical Engineering, New York, 63–69.

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lin m l and eagar t w (1985), ‘Influence of arc pressure on weld pool geometry’, Weld. J., 64(6), 163s–169s. lin m l and eagar t w (1986), ‘Pressure produced by gas tungsten arcs’, Metall. Trans. B, 17, 601–607. pedrotti f l and pedrotti l s (1993), Introduction to Optics, 2nd edn, Englewood Cliffs, NJ: Prentice-Hall. richardson r w (1984), ‘Coaxial arc weld pool viewing for process monitoring and control’, Weld. J., 63(3): 43–50. rokhlin s i and guu a c (1993), ‘A study of arc force, pool depression, and weld penetration during gas tungsten arc welding’, Weld. J., 72(8), 381s–390s. weiss d, schmidt j and franz u (1996), ‘A model of temperature distribution and weld pool deformation during arc welding’, in Cerjak H (ed.), Mathematical Modelling of Weld Phenomena 2, IOM Communications Ltd, London, 22–39. wu c s and liu y c (1996). ‘Rule-based control of weld bead width in pulsed gas tungsten arc welding’, Proc. Instn Mech. Engrs, Part B: J. Eng. Manufacture, 210(B1), 93–98. wu c s and yan f (2004), ‘Numerical simulation of transient development and diminution of weld pool in gas tungsten arc welding’, Modeling Simul. Mater. Sci. Eng., 12, 13–20. wu c s, gao j q and li k h (1999), ‘Vision-based sensing of weld pool geometry in pulsed TIG welding’, Int. J. Joining Mater., 11, 18–22. wu c s, gao j q, liu x f and zhao y h (2003), ‘Vision-based measurement of weld pool geometry in constant-current gas tungsten arc welding’, Proc. Instn Mech. Engrs, Part B: J. Eng. Manufacture, 217, 879–882. wu c s, gao j q and zhang m (2004a), ‘Sensing weld pool geometrical appearance in gas metal arc welding’, Proc. Instn Mech. Engrs, Part B: J. Eng. Manufacture, 218, 813–818. wu c s, zhao p c and zhang y m (2004b), ‘Numerical simulation of transient 3-D surface deformation of full-penetrated GTA weld pool’, Weld. J., 83(12), 330s-335s. wu c s, chen j and zhang y m (2007), ‘Numerical analysis of both front- and backside deformation of fully penetrated GTAW weld pool’, Computational Mater. Sci., 39, 635–642. yamamoto t, ohji t, miyasaka f and tsuji y (2003), ‘Simulation model for MAG arc welding as an engineering tool’, Mater. Sci. Forum, 426–432, 4057–4062. zacharia t, david s a, vitek j m and debroy t (1989), ‘Weld pool development during GTA and laser beam welding of type 304 stainless steel, part I – theoretical analysis’, Weld. J., 68, 499s–509s. zhang y m, wu l, walcott l and chen d h (1993), ‘Determining joint penetration in GTAW with vision sensing of weld face geometry’, Weld. J., 72(10), 463s– 469s. zhang y m, song h s and saeed g (2006), ‘Observation of a dynamic specular weld pool surface’, Measurement Sci. Technol., 17, L9–L12. zhao p c, wu c s and zhang y m (2005), ‘Modelling the transient behaviors of a fully-penetrated gas-tungsten arc weld pool with surface deformation’, Proc. Instn Mech. Engrs, Part B: J. Eng. Manufacture, 219, 99–110.

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10 Monitoring resistance welding D. F. FARSON, The Ohio State University, USA

10.1

Introduction

Resistance spot welding (RSW) is used in a wide range of industries, notably for joining automotive body sheet metal and for welding of packaging and interconnections during electronic device assembly. In a typical RSW application, two thicknesses of steel sheet to be fusion welded are clamped between copper electrodes and electrical current flowing from a power supply produces localized melting at their interface. When the current is removed, the melted material (the weld nugget) solidifies and joins the sheets. In terms of number of welds made, the RSW process is likely one of the most highly utilized of the macroscale welding processes. In the automotive industry alone, there are many tens of billions of resistance spot welds made every year. Because of its popularity, there has been much effort devoted to development of systems for quality monitoring and control. In recent years, there has been a distinct trend to integrate process monitoring into welding process controllers. Stand-alone process monitoring systems are still commonly available and used but process controllers provided with spot welding systems also commonly offer more process monitoring functions and increasingly use sensor data to control the spot weld process variables or weld time. Integration of process monitoring and control functions provides increased benefits since prevention or immediate correction of defect conditions offers more value than detection alone. The capabilities of monitoring and control systems are also enhanced and continued development of such systems is justified by increased computational power and decreased cost of computer hardware and software. In this chapter the RSW process is overviewed from a sensing and control point of view, starting with a summary of the relevant physical and mechanical phenomena and measurable signals generated as a weld is formed. Sensors for measuring signals and non-destructive inspection techniques useful for monitoring weld nugget development are discussed. Currently available monitoring and control systems are summarized and trends and 238 WPNL2204

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possible future developments in monitoring and control technology are noted. Citations to the research and engineering literature and some references that provide more in-depth information regarding process monitoring and control are listed at the end of the chapter.

10.2

Resistance spot welding systems, weld formation and signals

In this section, basics of RSW systems are discussed from a process monitoring and control point of view. Next, the physics of spot weld formation, the generation of signals that are measurable in real time (i.e. during weld formation) and their relationship to weld characteristics are summarized.

10.2.1 Systems and dynamic characteristics The most basic components of an RSW system are a frame to hold electrodes and to react to the forces generated by electrode motion actuator(s), a power supply, and a controller. Because high currents are required (typically in the range from 1–100 kA), a transformer is an essential component of any RSW power supply. From a process control standpoint, the speed of adjustment of the power supply output is determined by the electronic device(s), that rectify and regulate the transformer output. RSW equipment has much in common with that used for resistance seam welding and there are also many similarities to the projection welding systems. Thus, although this chapter concentrates on RSW, many of the monitoring and control systems described are also applicable to seam welding and projection welding. It is also noted that there are many different configurations even for the RSW process. Two directly opposed welding electrodes are assumed in discussions below, but parallel electrode configurations are also common. From a process monitoring and control viewpoint, important characteristics of the spot welding system components are their transient response speed and sensitivity. In this regard, there are major differences between the various power supplies and electrode actuation systems in use today. Having low system cost and a large base of existing systems, the most common power supplies for millimeter-scale sheet metal thicknesses are probably silicon controlled rectifier (SCR) single phase alternating current (AC) voltage supplies. Three-phase rectified SCR power supplies have traditionally been used for higher current applications such as welding of aluminum sheets of millimeter-thicknesses. Availability of reliable, economical high-current transistors has stimulated development of inverter-type power supplies – medium frequency direct current (MFDC) and high-frequency

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(HFDC) supplies. These are distinguished by their inverter switching frequency and output current capacities. Frequencies are typically in the range from 400 to 2000 Hz for MFDC supplies and 20 000 to 30 000 Hz for HFDC supplies. Typical output currents range from 500 to 100 000 A for MFDC supplies and from 100 to 10 000 A for HFDC supplies. MFDC supply currents are representative of those required for welding millimeter-scale (e.g. automobile body) sheet metal while HFDC currents are typical of those needed for electronics packaging and interconnection welding. Linear power supplies also use transistors to control the output of a rectified AC voltage source but they operate as non-switched linear series regulators. Linear supplies dissipate more power in the transistors than switching supplies and current outputs are consequently limited to about 1000 A and less. However, the response speed of linear supplies has no fundamental switching frequency limitation and so can be the fastest of the supply technologies mentioned. In their simplest implementations, the SCR supplies applied in resistance welding are adjustable voltage sources, with the SCR firing angle being the main control adjustment. The physically important variable of welding current is determined by the interaction of the power supply voltage and the variable resistance of the process as a weld is formed. With increased computing speed and decreased cost of digital controllers, SCR supplies are now sometimes operated as current-controlled or powercontrolled supplies. For operation in either of these modes, a current sensor must be incorporated into the primary or the secondary output circuit. A feedback control then adjusts the SCR firing control to achieve a desired current or power output level. Inverter and linear transistor supplies are usually designed to include current sensing and voltagecontrolled, current-controlled, and power-controlled modes are all commonly available. From a control viewpoint, one large difference among the power supplies mentioned above is the speed with which the output can be adjusted to provide weld current pulses and to compensate for process perturbations. The latter is often referred to as the small signal response speed of the power supply and is shorter than the minimum weld pulse time for the supply. The AC line frequency and switching frequency (or the time periods calculated as the inverse of these frequencies) set fundamental upper limits on the response speeds of SCR and inverter power supplies. Thus, a typical small signal response time of an SCR supply is in the order of 10−1 seconds. The small signal response time of inverter supplies is fundamentally limited by the inverter switching frequency and is typically in the order of 10−3 seconds for MFDC supplies and 10−4 seconds for HFDC supplies. The fastest small signal response times of 10−5 seconds and less are provided by linear transistor power supplies.

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There has also been development in the actuators used to provide electrode motion and clamping force during the welding process. Air cylinders, sometimes coupled with mechanical springs for fast response to maintain acceptable pressure during process fluctuations, have traditionally been the ‘mainstay’ actuators for resistance spot welding systems. However, servomotors are increasingly used, particularly for precision lowcurrent applications such as electronics packaging welding. Just as inverter power supplies have increased the speed at which welding electrical variables can be controlled, electric servomotor/linear slide mechanisms have increased both the response speed and the precision of electrode actuation. Thus regulation of clamping force to a constant magnitude or control to a prescribed waveform during a weld is possible with servomotor weld heads.

10.2.2 Weld formation and signals Many of the signals used for real-time RSW process monitoring and control arise from interaction of process variables (e.g. voltage and force) with physical mechanisms (e.g. material heating and melting) that play a role in the formation of the weld nugget. To discuss the various process monitoring and control systems, an overview of the phenomena occurring during weld formation and their role in generating process signals is presented. The discussion focuses on the formation of a molten weld nugget between two sheets of a resistive material (such as steel) while using electrodes of a highly conductive material (such as copper). With this combination of materials, the interface between the electrodes and sheets remains relatively cool and macroscale melting does not occur during process operation. Thus, most of the interesting phenomena related to weld formation and process signal generation occur within and between the sheets. The same basic mechanisms of heat generation and weld formation apply to other material combinations (such as conductive copper sheets and resistive molybdenum electrodes) but the different material properties cause the significant differences in heat distribution, weld formation and generation of monitoring and control signals. To start with, an illustration of a typical temperature distribution in the sheet material and electrodes occurring at the point in time during a spot weld is shown in Fig. 10.1. The increase in temperature within the sheets is due to joule heating caused by the flow of welding current through the resistive metal and surface contacts. The relative magnitude of electrical resistance as a function of distance through the weldment is also illustrated in Fig. 10.1. The generation of joule heat per unit volume P at any time and point r within the interior of the workpiece or electrodes is determined by the current density j and resistivity ρe at that location r by the relation:

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Water flow

Tm

Resistance

Temperature

10.1 Resistance spot welding formation and temperature and electrical resistance variations along a line through the middle of the weld.

P(r,t) = j2(r,t)ρe(r,t)

[10.1]

Heat generation per unit area Pc at any time and points on interface surfaces (metal–metal or electrode–metal) is due to flow of current through the contact resistance ρc and is given by the relation: Pc(r,t) = j2(r,t)ρc(r,t)

[10.2]

The resistance of the electrical circuit through the weldment changes dramatically with location, being highest at contact surfaces and lower in the bulk of the sheets. Both bulk and contact resistances change dramatically with temperature (hence, also time) and the latter also varies significantly with pressure. In the simplest analysis, the bulk resistivity ρe of a metal increases linearly with temperature according to the relationship: ρe (T) = ρe0[1 + α(T − T0)]

[10.3]

where α (Ω/ Ω/°C) is the resistance temperature coefficient and ρe0 is the resistivity at a reference temperature T0. For other metals and for wider temperature ranges, a power law relationship better describes the variation of resistivity with temperature. In the classical theory of electrical contacts, it is assumed that current passes through microscale and nanoscale asperities and the restricted area for current flow causes increased resistance at the contacting surface. The

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contact resistance and joule heating may be calculated from analysis of the flow of current through the asperities.1,2 Since thermal conductivity decreases with temperature, heat that is generated in current-carrying asperities tends to remain there, increasing resistance heating even more. Thus, at welding current densities, an unstable thermal runaway condition leads to rapid melting of current-carrying asperities.3 As asperities melt, the gap between the sheets becomes smaller and the remaining unmelted asperities come into contact and carry a share of the current. The end result of this process is complete melting of the metal contact area, which is the beginning of the formation of a weld nugget. It is also noted that oxides or contaminants introduce a film contact resistance on metal surfaces. The time variation of this film resistance with heat and pressure is more complex and varies with the type of film, but the general effect of both heat and pressure is also to decrease the contact resistance. The dynamic (time-varying) resistance between the welding electrodes Ree(t) is calculated as the ratio of the voltage measured between the electrodes Ve(t) and the weld current waveform I(t): V (t ) Ree(t ) = ee [10.4] I (t ) If the voltage difference between the top and bottom sheets Vss(t) is monitored during a weld, the corresponding dynamic resistance Rss(t) is calculated from Eq. (10.4) by replacing the electrode voltage with the sheet–sheet voltage: V (t ) Rss (t ) = ss [10.5] I (t ) It is noted that research has shown that the dynamic resistance of electrode–sheet contacts may smaller and less variable than the sheet–sheet resistance for uncoated materials, so Ree and Rss time waveforms have the same shape, but somewhat different magnitudes. For process control and monitor algorithms that rely on relative changes in dynamic resistance over the course of a weld, it may be possible to use the two interchangeably. However, the electrode–sheet contact resistance for coated materials may be relatively large and vary dramatically during a weld so the time variations of sheet–sheet and electrode–electrode resistances are different and the two are not interchangeable for process monitoring and control purposes. A sketch of the typical variation of sheet–sheet resistance during a weld is shown in Fig. 10.2. The dynamic resistance increases rapidly to a maximum at point A as current begins to flow and as the sheet–sheet interface is heated. Resistance then rapidly decreases to a maximum at point B as thin surface films dissipate and asperities melt. As a molten nugget is formed, the larger resistivity of molten metal causes a second resistance increase to a maximum at point C. However, decreased electrode separation due to

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A

C

RF B RC RB

Time

10.2 Sheet-to-sheet dynamic resistance during RSW of a resistive material showing the relative magnitudes and variation of the film, contact and bulk resistances, RF, RC, and RB, respectively (adapted from W Tan, Y Zhou, H W Kerr and S Lawson, A study of dynamic resistance during small-scale resistance spot welding of thin Ni sheets J. Phys. D: Appl. Phys. 37 (2004) 1998–2008).

plastic deformation (indentation) of the materials and continued decrease of resistance of the sheet–sheet contact area outside of the weld nugget lead to a gradual decrease in sheet–sheet resistance during the remainder of the weld time. Materials in the weld nugget and surrounding area also expand as they are heated and change phase. This effect can be measured by electrode displacement during nugget formation and cooling and also is related to the variation of electrode force during the welding cycle. The coefficient β of change in volume ΔV of an initial volume of material V0 due to a change in temperature ΔT is given by: ΔV = βΔT [10.6] V0 It is also noted that the coefficient of thermal expansion for a liquid material is usually significantly larger than that of its solid phase and there is also a noticeable volumetric expansion associated with melting (a latent volume of melting) that is analogous to the latent enthalpy of melting. If the weld current is excessive or applied for too long a time, or if the electrode force is too low, expulsion of the molten metal may occur. Ultimately, expulsion results when the unmelted interface surrounding the weld nugget is too narrow or has insufficient sheet–sheet pressure to contain the high pressure produced by expansion of the weld nugget material. If expulsion occurs, the electrode separation and the dynamic resistance drop abruptly as the electrode spacing decreases, as shown in Fig. 10.2 shortly after point C.

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The total amount of electrical energy E expended in the formation of a single resistance spot weld nugget can be approximated as the integral of the electrical power dissipated during the welding time Tw by the welding current flowing through the dynamic resistance Rss(t) of the welding circuit in the vicinity of the location where the weld nugget is formed: E=∫

Tw

0

I 2 (t )Rss (t )dt

[10.7]

A rough estimate of the volume Vn of molten metal in the weld nugget can be obtained by using this energy and an assumption that the nugget contains molten material with the minimum possible enthalpy Em gives an expression of the form: Vn = E / Em

[10.8]

In Eq. (10.8), Em is calculated as the sum of enthalpy rise when the weld material is heated from room temperature T0 to solidus temperature Ts and the additional enthalpy of fusion ΔHf corresponding to the temperature increase from solidus to liquidus temperature: Em ≈ ρcp (Ts − T0) + ΔHf

[10.9]

where cp is the average heat capacity at constant pressure of the weld material. The volume calculation in Eq. (10.8) neglects the fact that some electrical energy generated in the weld volume is lost by conduction to cooler surroundings. The enthalpy of the molten weld nugget material may also be larger than the minimum required for melting if the average nugget temperature is larger than the liquidus temperature. Both of these factors would make the actual nugget volume smaller than that estimated by Eq. (10.8).

10.3

Real-time process monitoring and control

The real-time monitoring and control systems of primary interest in this chapter are those based on sensor measurements of process variables, physical variables or weld characteristics measured as a weld is being made. Of the process variables already mentioned, those that are generated by the equipment and available for real-time measurement include welding power supply output (current, voltage, or power depending on supply operating mode) and electrode actuation force. Physical variables generated during weld formation include dynamic resistance, electrode displacement, temperature, acoustic emissions, and thermal radiation. In this section, sensors used to measure these various quantities and monitors and controls based on them are discussed.

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The ultimate objective of real-time monitoring is verification that the process is operating consistently and is generating welds that are the same as when it was originally configured. As discussed above, the physical phenomena occurring during weld formation may be monitored via measurements of electrical and other signals intrinsic to the process. Process control can be viewed as a natural extension of process monitoring since it uses the real-time measurements to adjust to process inputs to ensure consistent weld quality.

10.3.1 Objectives and approach of process monitoring and control Mechanical strength is generally the most important characteristic of a weld so monitoring and controlling it in real time would be ideal, if such a thing were possible. Unfortunately, this is not the case. Most designs that use spot welds rely on multiple welds to join the separate components and joint strength is related to the strength, number, and distribution of individual welds in a complicated way. Analytical calculations or finite element analyses are used to calculate the required number, distribution, and size of spot welds for a desired joint strength. Thus, quantifying weld joint strength using real-time measurements is not practical. Generally, the best compromise that is possible with real-time measurements is measurement or verification of the size of individual weld nuggets. Ultrasonic sensing, described in more detail below, offers some capability for real-time nugget size measurement. Other in-process measurements already mentioned, such as welding current or voltage, dynamic resistance, weld energy, or electrode deflection are only indirectly related to weld nugget size. If their time-varying measurements are close to values known to correspond to welds of adequate size, then there is some implied confidence in the size of the monitored weld. This is the foundation on which real-time weld monitoring and control systems described below are ultimately based. The indirect relationship between signals used as the basis for real-time monitoring and control systems and the ultimate characteristic of interest, weld strength, introduces the possibility of inaccuracy in the function of systems. Type I (a faulty weld exists but is judged to be good) and Type II (a good weld exists but is judged to be faulty) errors can both result because of this indirect relationship. It is also true that larger variations in weld quality are more likely to be accurately distinguished in real-time signals than small variations. Although the correlation between real-time measurements and weld quality varies from application to application, some degree of inaccuracy should generally be expected with real-time monitoring and control systems.

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10.3.2 Relationship of statistical process control to real-time monitoring and control Statistical process control (SPC) is a well-developed technique that applies statistical analysis to measurements of a process output to detect ‘out-ofcontrol’ values that indicate a non-random change in the process. An example relevant to spot welding might be the periodic welding and peel testing of a set of weld coupons to allow measurement of the weld nugget diameter. In this example, the average (or mean) of the nugget diameters from this sample set could be denoted x¯ and the range of the sample set diameters as R. Comparison of these sample set mean and range values to historical mean and range values obtained from analysis of a large number ¯ , allows anomalous sample sets to be of welds, typically denoted x¯ and R flagged. Statistical analyses of the same historical data used to calculate ¯ are used to set upper and lower control limits that define ranges of x¯ and R ¯ that would be expected for the ‘usual’ random variation values of x¯ and R inherent in that process. A value that falls outside the control limits would signify non-random variation, indicating that a deterministic change must have occurred in some aspect of the process. All quantities involved in the welding process can then be inspected and the cause of the anomalous measurements corrected. There are many texts that discuss the SPC method in more detail. The SPC methodology can also be applied to real-time measurements to detect non-random (also called out-of-control) variations in the RSW process. For example, the total energy expended during each spot weld may be calculated as shown in Eq. (10.7) This energy contains information about the weld nugget formation since it depends on the dynamic resistance waveform measured over the duration of the weld. SPC may be applied to sample sets of energy values periodically collected during production. Calculation of the average and range of sample sets and comparison with historical values allow non-random variations to be detected and their cause to then be identified and corrected. It is axiomatic that non-random variation of process measurements, whether real time or otherwise, is related to some systematic variation of process inputs. These are traditionally classified into one of four categories: machine variables and characteristics, human factors, material characteristics, or environmental conditions. Machine variables for the RSW process include electrode geometry, welding current, and clamping force. Examples of machine characteristics that are not easily varied or controlled in real time include electrode tip geometry or electrode actuation mechanism backlash. Important material characteristics including sheet metal composition, thicknesses, surface coatings and conditions, and fit-up can all have

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major effects. Operator and environmental factors usually play smaller roles in spot weld process quality. In the application of SPC or real-time process monitoring and control to spot welding, an important issue is identification and elimination of the causes of process variation. Analysis of the variation of process monitoring signals over time can be helpful for identifying when a deterministic change in the process has occurred and what action is needed to address it. For example, a typical cause of spot weld variation might be a change in surface condition of the welded sheets due to a fluctuation in coating thickness or composition. Such a change in material properties would cause a change in the dynamic resistance measured during the spot welding process and could be detected by process monitoring algorithms that compare measured dynamic resistance to expected values. Real-time process controllers based on process measurements are an alternative approach for minimizing the effects of variations in process inputs. In the ideal case where the controller functions perfectly, process variations of some level can be tolerated since they are compensated for by adjustments generated by the controller. Whatever the approach taken to minimize their effects, it is helpful to be aware of commonly occurring causes of spot weld variation. It should be borne in mind that it may be more economical, convenient, or reliable to simply modify the RSW process so it is less affected by some of these common causes of variations. For example, it is common practice to make pre-programmed increases in power supply output (e.g. welding voltage) based on number of welds completed since a prior electrode replacement or re-machining operation. When properly done, this ‘current stepping’ produces a consistent weld nugget size in spite of electrode tip geometry changes. Another example of a process modification would be incorporation of pre-pulses into the spot welding current waveform. Low-current pre-pulses can condition the sheet surfaces to normalize contact resistance prior to application of the main welding current pulse. Such a modification can make the process robust to small variations in metal surface condition and could eliminate the need for more expensive steps such as manual inspection of incoming material. Electrode ‘wear’ is probably the most widely recognized source of weld nugget variation and is also the one whose effects must be compensated for by welding controllers. Electrode wear is a combination of flattening and erosion of the welding electrode tips that occurs due to application of current and force during repeated welding cycles. Whether deformation or erosion is the dominate mechanism depends to some extent on the alloy being welded.4 The net effect of both these tip degradation mechanisms is a generally increased contact area. This leads to decreased current density, decreased clamping pressure, and ultimately to

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undersized weld nuggets and/or expulsion. Most RSW controllers include provisions for ‘current stepping’, which are programmed increases in welding power supply output based on the number of welds since the last tip replacement or reconditioning. Mechanical tip dressing systems that recondition welding electrodes by re-machining them back to nominal shape after a programmable number of welds are also often incorporated into automated welding stations. It is notable that electrode force stepping is rarely used, which may be a reflection of the fact that electrode force settings that are acceptable over the range of tip conditions can be found. Variation of metal surface condition is also a significant cause of variation in RSW. When welding coated materials, this may occur as a result of coating thickness or composition variation. The large decrease in contact resistance that occurs during initial current flow times can be used for pre-conditioning of sheet and electrode interfaces. In systems that provide such capability, a series of short, low-magnitude current pulses can be imposed prior to the main welding current to eliminate the initial contact resistance variation. This step, although it does consume some additional weld cycle time, also decreases weld variation due to variability of metal surface conditions. Poor fit-up of parts can be a source of significant spot weld variation. In the common case of sheet metal welding, sheets may be bent so as to cause a gap to remain after clamping or, if less severe, may cause decreased interface pressure. Variations in part size and/or shape can result in spot welds being too close to edges. In both cases, the likely result is expulsion of the molten metal from the weld volume with subsequent porosity or void formation and decreased weld strength.

10.3.3 Sensors and interfacing The spot welding process and the manufacturing environment place some special requirements on the choice and integration of sensors to measure process and physical variables,5,6 although the sensor technologies are for the most part the same as those used for commercially available sensors for the various quantities.7 Precision DC welding currents are commonly measured with series shunts, small calibrated resistances which produce a millivolt-level signal that is proportional to current. AC welding currents are commonly measured with a toroidal Rogowski coil placed around a secondary conductor. Hall effect current sensors are also used. Electrode force sensors are commonly piezoelectric or strain gauge load cells. Linear variable displacement transducers (LVDT) and optical encoders (digital) have been used to measure electrode displacement with respect to the welded material or another fixed datum.

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Real-time weld process monitoring V

I

10.3 The output V of a Rogowski coil is directly related to the time derivative of the currentI flowing in a conductor.

Because of their popularity, some more details of Rogowski coils8 are summarized here. These devices are helical coil sensors uniformly wound on a relatively long, non-magnetic, circular or rectangular flexible strip. A time-varying magnetic field induces a proportional voltage in seriesconnected coils. When the Rogowski coil is used for current measurements, the coil wraps around a current-conducting wire as shown in Fig. 10.3. Rogowski coils can be used to measure transient currents with pulse times down to several nanoseconds, have excellent linearity, no danger of opening the second winding of the current transformer and low construction costs. Although Rogowski coils are simple to construct, uniformity of winding is necessary to ensure that the output voltage is independent of the position of the conductor relative to the coil. Also, homogeneity of flux in the turns is optimized by winding on a thin strip with a small cross-sectional area. Finally, the return loop should be positioned so that both terminals are at the same end of the coil. Details concerning construction and usage of Rogowski coils for RSW current measurement have been summarized.9 In typical RSW applications, the welding electrodes provide a unique opportunity to couple transducers to the weld sheets. Ultrasonic transducers can be used to send or receive acoustic energy, enabling the use of acoustic emission (passive receiving of acoustic waves emitted by the weld) and ultrasonic inspection (active interrogation of the weld volume by transmitted and/or reflected ultrasonic energy). Transducers must be mounted in both electrodes for transmission measurements, but a single transducer may be used to send and receive energy for reflection measurements. Practical limitations provided by the typical manufacturing environment place special constraints on the design of sensors used with the RSW

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process. Dirt, electrical, and acoustic noise, collisions with parts during loading and unloading, motion system programming, and high cycle fatigue incurred over the course of many welds are all factors that require monitoring systems to be rugged and unobtrusive on the welding process. For example, sensitive optics attached to the welding electrodes may be damaged by collisions with parts and fouled from the dust and dirt typically found in the manufacturing environment. The additional, variable interface introduced by the use of replaceable electrode caps is a source of inaccuracy in ultrasonic and acoustic emission-based sensing systems. The usual grounding, shielding, and isolation practices used to interface sensors to industrial controls are needed for RSW sensing. The magnetic fields generated by high-resistance welding currents are particularly problematic, although shielded/twisted pair or coaxial interface wiring is usually sufficient to overcome them. The operation mode of the power supply control determines the electrical variables that can be usefully monitored for in-process assessment of weld nugget development. If current is controlled, weld voltage and/or power may be monitored, while if voltage is controlled, current and/or power may be monitored. If power is controlled, either current or voltage may be monitored. In addition, dynamic resistance, described and discussed in more detail below, may also be monitored with any of the control modes. A similar discussion of controlled vs. monitored variables, given above in relation to welding current and voltage, is applicable to weld heads that include both force and displacement measurement. In particular, if force is controlled, then displacement may be monitored as a process variable. If displacement is controlled, for example by a hydraulic device that prevents backward motion of the electrodes during welding, then the force measurement is directly related to weld nugget expansion. For air cylinder weld heads where both force and displacement vary during the weld, there is some process information contained in both signals. Implementation of a feedback force control function requires that the weld head incorporates a force sensor in series with the electrode actuation. Encoders designed into the weld mechanism have been used to monitor electrode actuator position.

10.3.4 Real-time monitoring and control algorithms The process signal characteristics discussed above are unique for different materials, thicknesses, numbers of layers, and different coatings. Thus, as mentioned earlier, process monitoring and control algorithms are based on comparisons of measured signals with those from prior experiments for the same welding application. In the case of electrical signals, dynamic

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resistance and/or weld energy can be acquired and evaluated during welding. In principle, a feedback control can be implemented on power supply output (e.g. the current output of a current-controlled supply) to cause either dynamic resistance or energy measurements to track a desired waveform. In the case of real-time weld energy, a common alternative real-time control strategy is simply to regulate weld time so as to ensure a desired value of weld energy. The electrode force signal is commonly used for triggering the initiation of welding current flow. Electrode force can fine-tune the ‘squeeze time’ portion of the weld cycle normally incorporated into the welding sequence by not initiating welding current until there is adequate force. This ensures good electrical contact and containment of the molten weld nugget material during welding. Electrode deflection is closely related to the expansion, melting, and expulsion of material during the RSW process and monitors and controls based on this measurement, both with and without information from electrical signals have been demonstrated. One controller uses number of realtime input variables including electrode force, electrode–electrode voltage Vee and secondary circuit current, and electrode displacement and temperature as weld response variables. A simplified analytical real-time model uses the measured inputs to the predict response variables, and weld quality assessments are made based on a comparison of the predicted and measured responses.10 The passive monitoring of RSW nugget formation and growth by acoustic emissions has been proposed11 but real-time monitoring and control of RSW quality based on active ultrasonic measurements are more widely studied technologies (e.g. Bilge et al.12) and are available as options on some commercial RSW controllers. The through-transmission and reflection signals are recorded, stored, and evaluated. Both the strength of the transmitted and reflected signals as well as the times for transmission and reflection have been proposed as being correlated to weld nugget diameter and thickness. In particular, shear waves are not transmitted through liquid, so the transmitted shear wave energy signal is correlated to weld nugget size and can be used for monitoring or control.13 Alternatively, the time-of-flight of ultrasonic waves reflected from the liquid/solid nugget boundary can be used to monitor weld nugget growth and terminate weld current when a target size has been achieved.14 A simple approach is to compare measured signals to those from welds of known quality and to base ‘good’ or ‘bad’ quality decisions on the comparison. A control approach based on the same measured signal varies the current to control the rate of nugget growth so the transmitted shear wave energy signal matches a desired waveform from prior data.10

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10.3.5 Resistance spot welding signals and monitoring example Specific examples of signals measured from a spot welding process and their relationships to spot weld size15 and weld expulsion16 are summarized as examples of spot welding processes and real-time monitoring. Electrical voltage and current and electrode force and displacement were monitored during a precision spot welding process. The process used a HFDC power supply operated in constant current mode at a welding current of 1.25 kA and pneumatic electrode actuation. RWMA class 2 electrodes with a diameter of 2.38 mm were used to weld 304SS sheets with thickness of 0.254 mm. A subminiature strain gauge load cell was placed beneath the lower electrode holder to measure the force and force change during welding. The load cell had a maximum non-linearity of ±0.25% of full scale and a digitized resolution of 0.015 N. The displacement of the upper electrode holder relative to the fixed lower electrode holder was measured using a fiber optic position sensor. A high-speed video imaging system with frame rate of 4500 per second and vertical resolution of 1.75 μm/pixel was also employed to directly observe the displacement of the welding electrodes during welding. The process monitoring signals collected during a weld are shown in Fig. 10.4. The weld schedule used a series of preconditioning current pulses, visible in the voltage traces at around 1 ms. When the welding current was switched on, it rose to the welding level. The welding voltage had an initial spike of almost 2 V, then decreased and was relatively constant at a value of approximately 1.5 V throughout the remainder of the 6 ms weld time. The upper electrode displacement as measured by the fiber optic sensor increased continuously while the current remained on and then decreased when the current was terminated. It was determined that the oscillation in the displacement signal was caused by elasticity in the polymer sensor mount. The force signal was corrupted by a bias error that was present whenever the welding current was flowing. This bias was subtracted from the signal and the corrected signal plotted in Fig. 10.4(b) and in subsequent figures is referred to as ‘force change’ to denote the correction. The magnitude of the force change signal varied roughly as the second derivative of the placement signal. The high-resolution video electrode displacement signal revealed that the electrode separation steadily increased for the duration of the welding current pulse. When the current was shut off, the electrode separation decreased rapidly for about 2 ms and then more slowly throughout the remaining 10 ms of the recorded waveform. The correspondence of the decrease in separation during this ‘high-speed’ portion of the signal to weld nugget dimensions was analyzed further.

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10.4 Time-varying signals for a measured for a normal weld: (a) current, voltage; (b) electrode displacement (fiber optic), force change; (c) electrode displacement (video).

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It was noted earlier that the coefficient of thermal expansion of liquid metal is greater than that of solid material. Thus, it was proposed that the rapid initial decrease in electrode separation immediately after the current termination was due to cooling and solidification of the weld nugget. The magnitude of the electrode displacement during this initial, steeply sloped portion of the electrode separation curve was measured and compared with the size of weld nuggets generated at varying currents and weld times. The resulting electrode displacement was found to be highly correlated (Fig. 10.5a, R2 coefficient was 0.98) with post-weld measurements of weld nugget thicknesses. The same displacement parameter was also found to be correlated with weld nugget diameter, but with a somewhat lower correlation coefficient (Fig. 10.5b, R2 = 0.91). The maximum electrode displacement was also found to correlate with nugget thickness for welds made at varying currents and weld times although its correlation coefficient (Fig. 10.5c, R2 = 0.93) was lower than that measured for the displacement during the initial high-speed stage of cooling. There is interest in detecting the expulsion event during spot weld formation. Expulsion of a relatively large amount of molten metal is relatively easily observed in real-time voltage, force, and displacement signals. As is shown by comparison of Fig. 10.6(a) and (b) with Fig. 10.4, the expulsion event causes a brief change of slope or (for larger amount of expelled material) a decrease in magnitude of the electrode separation. For a large expulsion, a brief (less than 1 ms duration) increase in voltage is observed at the instant of expulsion. A smaller, shorter voltage spike is observed for the smaller expulsion, although comparison with the voltage trace in Fig. 10.4 reveals that similar voltage spikes may also be observed during weld made without expulsion. There is also a momentary disruption in the force signal during expulsion, although this signal is somewhat more complicated than the displacement signal. Owing to the pneumatic electrode actuation and elastic deflection of the spot welder mechanism and displacement measurement sensor mount, the electrode displacement and electrode force signals displayed damped oscillations. These oscillations were excited by the abrupt expulsion event and were much more evident in the welds where expulsion occurred.

10.4

Future trends

Refinements in resistance weld monitoring and control that are currently being introduced into practice are based on phenomena reported in research literature and concepts previously described in patent filings some years ago. Rather than being based on new process understanding or concepts, much of the current progress is based on more affordable, capable, electrical and mechanical components that allow faster and more

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10.5 Relationship of electrode displacement to weld nugget size: (a) displacement in first cooling stage to nugget thickness; (b) displacement in first stage of cooling to nugget diameter; (c) total electrode displacement to nugget thickness.

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10.6 Sensor signals for current, voltage, electrode displacement and force change for a weld with: (a,b) large expulsion; (c,d) small expulsion.

precise regulation of electrode force and motion and welding power supply output. This increased process controllability, combined with the increased computational power of controllers, has led to more sophisticated sensor signal processing and integration of real-time monitoring and control functions in commercially available resistance welding controllers. Spot weld control systems with more integrated process sensing capability (exemplified by ultrasonic process sensing currently available commercially) will continue to lead to more reliable process monitoring and control capabilities. On a longer timescale, it can be anticipated that continued advances in computational hardware and software may lead to real-time implementation of numerical simulations of the spot welding process. Extensive effort has been devoted to developing accurate process simulations and there is currently some capability to realistically simulate the process. However, it has been noted that incomplete representation and strong coupling between electrical, thermal, metallurgical, and mechanical effects have prevented completely realistic simulations.17 However, simpler models,18 perhaps incorporating artificial intelligence techniques, may be sufficiently accurate

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and executable in unison with the resistance welding process, enabling more sophisticated, generally applicable, and reliable sensor-based process controls.

10.5


Resistance Welding Manual, revised 4th edn. The Resistance Welding Manufacturers’ Association (RWMA) AWS, Miami, FL. Welding Handbook, 8th edn. The American Welding Society, Miami, FL. Statistical Process Control, John R. English and Terry R. Collins, Wiley, New York, 2001.

10.6

Acknowledgment

The review of this chapter by G. J. Daumeyer III is gratefully acknowledged.

10.7

References

1 holm r (1976), Electric Contacts, Theory and Applications. Berlin, Germany: Springer-Verlag, 1976. 2 timsit rs (1999), ‘Electrical contact resistance: Properties of stationary interfaces’, IEEE Trans Compon. Packaging Tech, 22 (1), 85–98. 3 timsit rs (1991), ‘The melting voltage in electrical contacts’, IEEE Trans Compon. Hybrids Manufacturing Tech, 14 (2), 285–292. 4 spinella dj, brockenbrough jr, fridy jm (2005), ‘Trends in aluminum resistance spot welding for the auto industry’, Welding J, 84(1), 34–41. 5 chien cs, kannatey-asibu e (2002), ‘Investigation of monitoring systems for resistance spot welding’, Welding J, 8 (9), 195–199-s. 6 heckendorn lc (1995), ‘Resistance welding sensor’, US Patent 5504299. 7 fraden j (1996) Handbook of Modern Sensors: Physics, design and applications, 2nd edn, Springer-Verlag, New York. 8 tumanski s (2007), ‘Induction coil sensors – a review’, Meas Sci Tech 18 (3), R31–R46. 9 destefan de (1996) ‘Calibration and testing facility for resistance welding current monitors’, IEEE Trans Instrum Meas 45 (2), 453–456. 10 nied ha, godwin sj, cohen rk, klint rv, wang h-p (1984) ‘Resistance spot welder process monitor’, US Patent 4596917. 11 notvest kr (1974) ‘Acoustic emission spot welding controller’, US Patent 3824377. 12 bilge u, scarpelli af, schwartz re, ross jj (1987), ‘Ultrasonic method and apparatus for spot weld control’, US Patent 4711984. 13 rooks b (2004), ‘Continuing new welding developments at Weldex show’, Indust Robot – Int J, 31(2), 139–144. 14 nied ha (1984), ‘Acoustic wave spot welder adaptive control’, US Patent 4449029.

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15 farson df, chen jz, ely k, frech t (2004), ‘Monitoring resistance spot nugget size by electrode displacement’, J Manuf Sci Eng Trans ASME, 126 (2), 391–394. 16 farson df, chen jz, ely k, frech t (2003), ‘Monitoring of expulsion in small scale resistance spot welding’, Sci Tech Welding Joining, 8(6), 431–436. 17 williams nt, parker jd (2004), ‘Review of resistance spot welding of steel sheets Part 1 Modelling and control of weld nugget formation’, Int Mater Rev, 49 (2), 45–75, 18 chen jz, farson df (2006), ‘Analytical modeling of heat conduction for small scale resistance spot welding process’, J Mater Process Technol, 178 (1–3): 251–258.

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11 Monitoring laser welding A. ANCONA and T. SIBILLANO, CNR-INFM LIT3, Italy

11.1

Introduction

Laser welding is a highly automated process that is being used more and more in the automotive industry. The advantages of laser welding include high speed, deep penetration, high aspect ratio of the joints, and low thermal distortion. To further improve the efficiency of the laser welding systems, quality assurance measures are increasingly required directly into the production processes. Traditional off-line inspection of welds is expensive, reduces productivity, and requires dedicated test equipment and people. Therefore, the development of an automated on-line monitoring sensor for laser welding defect detection, and closed loop control systems, has been an open field of research in the last years. Several solutions have been proposed. These systems rely on the analysis of different signals emitted during the laser welding process (Fig. 11.1). After a brief introduction on the basics of the physical interactions involved during a laser welding process, in this chapter an overview will be given of the recently developed monitoring systems. The mechanisms underlying the generation of the acquired signals and their potential to detect the different weld defects will be discussed. Finally, the currently available commercial systems and the perspectives of real-time monitoring of laser welding processes will be examined.

11.2

Principles of laser welding

The interaction of the focused laser beam with the material involves rapid melting and, above a certain intensity threshold, ablation and vaporization. Unlike conventional welding processes where the energy is deposited on the surface of the workpiece and is brought into the interior by conduction and convection, in the ‘deep penetration laser beam welding’ regime the vaporization is sufficiently intense to generate enough pressure to push the molten metal aside, resulting in the creation of a thin capillary called a ‘keyhole’. The keyhole is held open due to a dynamic balance between the 260 WPNL2204

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Airborne audible acoustic emissions

UV–VIS plasma optical emission

Weld pool infrared emissions Structure-borne ultrasonic acoustic emissions Bottomside IR emissions

Bottomside plasma optical emission Transmitted laser

11.1 Signals of different nature used for real-time laser welding monitoring.

surface tension of the surrounding melt pool and the pressure that comes from the ablation of its walls and the flow of viscous hot vapours through the capillary. By traversing the beam and thus the keyhole along the joint edges, laser welding is performed. The energy transfer ‘via the keyhole’ is more efficient than the conduction regime since the thin capillary traps the incident beam, increasing the absorption of the laser radiation deep inside the workpiece. Two mechanisms of energy transfer take place during deep penetration laser welding: direct Fresnel absorption of the beam on the keyhole walls or plasma absorption inside the capillary with subsequent re-irradiation to the keyhole walls. Plasma formation originates from the partially ionized metal vapours generated inside the keyhole. The ionization of the plasma is increased due to the free electrons inside the gaseous volume that absorb the incoming laser radiation by inverse Bremsstrahlung. As far as the electron density is increased, the plasma absorption coefficient at the laser wavelength increases, resulting in avalanche ionization and an expansion of the plasma towards the keyhole opening (Fig. 11.2). The efficiency of deep penetration laser welding is mostly affected by the existence of this plasma plume above the keyhole. Under proper conditions it is responsible for the major and rapid heat input to the

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11.2 Deep penetration laser welding process. (Tu et al.1 ‘Characterizing keyhole plasma light emission and plasma plume scattering for monitoring 20 kW CO2 laser welding process’ reproduced by permission from Journal of Laser Application, Volume 14, August 2002. Copyright 2002, Laser Institute of America, Orlando, Florida. The Laser Institute of America disclaims any responsibility or liability resulting from the placement and use in the described manner.)

workpiece. More often, however, the laser-induced plasma acts in a negative way. There are three energy loss mechanisms that can occur in the plume: defocusing, absorption and scattering. Light suffers strong refraction in laser-generated plasma because the steep radial temperature gradients cause it to bend from regions of high electron density to regions of lower electron density. The refractive index of the plasma is a function of the electron density. When the density of the ionized species in the plasma is above a critical value, defocusing and reflection of the laser beam will occur. The plume absorbs part of the laser beam energy by inverse Bremsstrahlung. Furthermore, scattering of the laser beam may take place, due to small particles of melted material ejected from the keyhole. This kind of scattering is negligible at the 10.6 μm wavelength of CO2 lasers but can be significant for 1.064 μm in the case of Nd :YAG lasers. In other words,

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the plasma plume affects the coupling of the laser energy to the workpiece. This will cause a change in the weld shape, and even total collapse of the keyhole. Therefore, in order to achieve the advantages of rapid heat transfer and reduce its negative effects, the plasma must be carefully controlled. The close relationship between plasma and weld quality thus justifies using the plume behaviour as an indicator of welding performance. Sensing and control of the laser-induced plasma are key factors for real-time process monitoring, and understanding the plasma behaviour and its influence on the welding process will lead to improved performances and reliable processes.

11.3

Optical sensing

Optical sensors for laser welding process monitoring are based on the detection of the plasma plume emission, the thermal radiation of the weld pool and the reflected laser light. The characteristic blackbody radiation (up to the boiling temperature of the welding metal) from the melt pool is in the infrared region of the spectrum. The plasma plume radiates in the visible and ultraviolet regions at temperatures up to 104 K. Extensive studies have been conducted on the analysis of plasma optical emission because it is readily observable for being outside of the keyhole and can be easily collected with optical fibres. Among the manifold approaches for the development of optical sensors, the most effective are the systems based on the measurement of the spatially integrated optical intensity or the spectroscopic analysis of the VIS/UV emission. The main recent results of these two approaches will be discussed in the following paragraphs.

11.3.1 Light intensity monitoring Monitoring the light intensity from the plasma produced during laser welding is a common method of diagnostics and yields information on the presence of defects and on the depth of penetration. A number of quality monitoring systems based on one or more photodiodes have been reported by several authors. Beyer and coworkers2 investigated the effect of the shielding gas and the relationship between full penetration and plasma signals in the VIS and UV range at the top and bottom of the workpiece. In recent works1,3 the keyhole plasma was studied using several photodiodes arranged at different viewing angles with respect to the beam axis. In this way each detector could inspect a different region into the plasma and, by combining the respective signals, it was possible to estimate the plasma intensity distribution inside the

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keyhole and predict the penetration depth on thick samples of steel sheets. Park and Rhee4 obtained qualitative information on the bead shape and on the full penetration through an analysis of the UV plasma and the IR spatter emission detected with photodiodes. All these results show that simultaneous measurements of spectral line intensities from the plasma emission, together with the IR radiation from the weld pool, provide a way to relate changes in plasma characteristics to the size and the temperature of the weld pool itself. The reliability of these techniques may be compromised by the fact that IR emission from the plasma is also present, making it difficult to separate it from the IR signal arising at the surface of the weld pool. In addition, the thermal time constant for temperature changes in the molten metal can be much longer than the plasma fluctuations timescale. The information about the degree of penetration during laser welding can be obtained by looking down the laser keyhole plasma with detectors mounted near the focusing optics or, when complete penetration is achieved, under the workpiece. The basis of all these systems is the plasma emission intensity measurement over one or more spectral regions. Changes in timeaveraged integrated intensity can be used to infer welding conditions. In case of partial penetration the keyhole plasma is localized near the entrance of the cavity and oscillates around this position so it is not free to expand towards the closed end of the keyhole. Under complete penetration the plasma can expand in both directions and the optical intensity detected above and inside the keyhole will be correlated. It is worth noting that this behaviour may not be straightforward by merely comparing the emission intensities. It is more apparent through a frequency analysis of the optical signals that will be discussed in the following paragraph. Optical intensity is most useful in detecting weld fault conditions because these commonly involve significant changes in total plasma emission. As will be shown, the analysis of the signal frequency spectrum of the transient can provide additional information but adds an extra level of complication to the monitoring system. Other authors have investigated the stability of the photodiode signal, in terms of amplitude fluctuations, in order to determine the optimal process parameters such as gas shielding. A stable signal is expected to correspond with a steady and reliable process5. A closed-loop control system has been developed by Bagger and Olsen6 for regulating the laser output power by observing the light emission from the root side of the sheet. The basic assumption of this work is that a higher laser power results in a higher root-side photodiode signal. In principle this should make it possible to control the delivered amount of laser energy in such a way that a precise and predefined root-side seam width could be obtained, regardless of the type of material, thickness, speed and focus position. The control system demonstrated the ability to respond to a stepwise change in the sheet

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thickness. The main drawback of this system was that it was not able to establish steady welding conditions for lap welding of thicknesses greater than 1.25 mm. Moreover, the laser power controller had a response time which was too long with respect to the optimized welding speed that, for particular materials and applications, exceeded several m/min. A non-intrusive optical sensing technique for Nd :YAG laser welding monitoring has been demonstrated by Peters et al.7, which consisted of the detection of the light radiated by the plasma plume above the surface of the weld and back propagating in the cladding of the laser beam delivery fibre8 (cladding power monitor, CPM). Filters were used to block the backreflected laser light. The CPM was incorporated into the delivery fibre optic by coupling the light coming from the cladding into the core of the monitor fibre and then delivering it to a UV/VIS photodiode. The results demonstrated that the optical signals from the plume had a clear correlation with laser welding faults, including out-of-focus errors and shielding gas interruption. A similar system was used by the same authors for real-time closedloop focus control during Nd:YAG laser welding9–11. The optical signal collected from the delivery fibre was split into two spectral bands (Fig. 11.3). A simple algorithm computing the difference between the normalized IR and the UV/VIS emissions gave a signal proportional to the focal error as a result of the chromatic aberrations in the optical delivery system. This real-time focus control has been demonstrated for bead-on-plate welds on

Fibre-optic detection system

Camera Lens Fibre-optic delivery system

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11.3 Schematic of the real-time closed loop power and focus control system developed by Bardin et al.11.

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11.4 Case study of the application of the system developed by Bardin et al.11 to the monitoring of a laser welding of a tilted 2 mm thick Inconel sample: (a) longitudinal cross-section; (b) photographs of rear seams obtained for welds generated with open-loop and closed-loop controls; (c) focal error signals obtained in each case.

different thicknesses and different materials. It was able to maintain the right focal position on tilted and non-flat samples with step changes of the thickness (Fig. 11.4). The resolution of the system was nevertheless limited by noise due to the intrinsic instability of the optical signal oscillations of the plasma plume. It was thus fundamental to the choice of the optical filters, looking for a compromise between noise and the detected bandwidth.

11.3.2 Spectroscopic techniques Spectroscopic analysis of the plasma plume optical emission is known to be a promising technique for real-time monitoring of laser welding processes, since it offers several advantages compared with other techniques. Like the

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11.5 Visible spectrum of the plasma optical emission during a CO2 laser welding process of AA5083 Al–Mg alloy.

previously mentioned photodiode-based sensors, spectrometers are nonintrusive, low cost and easy to embed in a production line, but they give much more detailed information on the plasma plume dynamics. Furthermore, thanks to the recent developments in detector technology, fast spectrometers with acquisition rates up to 103 spectra/s are available on the market, making these systems suitable for real-time industrial process monitoring and control. In spite of this, the development of a reliable and robust on-line welding sensor based on plasma spectroscopy is still an active area of research. Plasma optical spectra are characterized by several emission lines of the excited atoms or ions belonging to the metals to be welded or to the gaseous environment (shielding gas or ambient air). It is thus possible to determine the chemical composition and the dynamics of interaction of the different chemical species inside the plume (Fig. 11.5). Moreover, spectroscopic analyses of the welding plasma optical emission in the UV–VIS and near-IR ranges can give information on such important physical parameters of the plume as electron temperature, electron density and absorption coefficient to the laser wavelength. These parameters are widely recognized to be related to the stability of the laser welding process12,13 and, in particular, the plasma electron temperature and its electron density, have been shown to be highly correlated to the quality of the laser-welded joints14–19.

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The laser-induced welding plasmas are generally supposed to be optically thin and in local thermal equilibrium. This means that inside the plasma volume the governing mechanisms for energy transfer among particles are collisional processes over radiative ones, so that it can be assumed that atoms and ions follow Maxwellian energy distributions. In order to fulfil this criterion, the electron density must overcome a critical threshold which may be slightly different according to the laser wavelength and the welding metal alloy20. Experimental investigations, based on the measurement of the spectral broadening of selected emission lines21, have shown that typical electron densities in laser welding plasmas exceed this threshold by at least two orders of magnitude, so the local thermal equilibrium hypothesis is assumed to be valid. The plasma electron temperature can be calculated by using the Boltzmann plot method. It consists of measuring the relative intensities of several emission lines, free from self-absorption, of a chosen chemical species of the optical spectrum. The optical intensity Imn of the generic emission line associated with the transition from the upper energy level Em to the lower energy level En is related to the energy of the emitted photons hc/ λmn, the transition probability Amn and the population of the excited state Nm by the following equation: Imn = NmAmnhc/λmn

[11.1]

Assuming Boltzmann statistics for the energy levels populations, Nm can be expressed as: Nm = (N/Z)gmexp(−Em/kT)

[11.2]

where N is the total density of the states, gm is the statistical weight of the energy level and Z is the partition function. From Eqs. (11.1) and (11.2):

( )

Nhc E ⎛I λ ⎞ ln ⎜ mn mn ⎟ = ln − m ⎝ Amn gm ⎠ Z kTe

[11.3]

By plotting the first term of Eq. (11.3) versus Em for several lines of the same chemical species, one can estimate the electron temperature Te, which is related to the slope of the linear fit. The electron temperature has been also estimated by use of the intensity ratio of just a couple of emission lines (labelled (1) and (2) in the following equations) among those selected for the Boltzmann plot: I (1) A(1) gm(1)λ ( 2 ) E (1) − Em( 2 ) ⎤ = exp ⎡ − m ⎣⎢ ⎦⎥ I ( 2 ) A( 2 ) gm ( 2 )λ (1) kTe Extracting Te from Eq. (11.4):

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Monitoring laser welding Te =

Em ( 2 ) − Em (1) I (1) A( 2 ) gm ( 2 )λ (1) ⎤ ⎡ k ln ⎢ ⎣ I ( 2 ) A(1) gm (1)λ ( 2 ) ⎥⎦

269

[11.5]

Since the emission line parameters are known, this method is particularly advantageous because it does not require too many calculations and can easily be implemented in suitable software for real-time temperature measurement. The real-time sensor system developed by Ancona et al.12 was based on this technique. The laser welding plasma electron temperature was contemporarily monitored for different chemical species inside the plasma plume, belonging both to the welding metal and the shielding gas. Non-uniform radiation behaviour of the chemical species, caused by a non-uniform temperature distribution, was observed. The corresponding electron temperature signals were recorded and analysed during several welding tests carried out by varying the main process parameters: power, speed, gas flow rate and beam focus position. A clear correlation between the signal mean value, the standard deviation and the quality of the welded joint was found. This information was used to optimise the welding parameters. Furthermore it was demonstrated that local perturbations or variations in the temperature signals are related to weld defects. The introduction of several temperature signals allowed detection of either plasma core instabilities or perturbations in the peripheral part of the plume. In this way, even though both kinds of instabilities originated weld defects, the driving cause could be addressed to specific process parameters affecting different plasma regions. A wide range of weld defects, such as lack of penetration, weld disruptions, crater formation and seam oxidation was successfully detected (Fig. 11.6). Suitable sensor software allowed the acquisition of the signal and its analysis in real time. The defect detection algorithm consisted of comparing the electron temperature of the welding process under examination with a reference baseline and two adjustable error thresholds. The reference signal was computed during a self-learning procedure in which preliminary sound welds were carried out. The upper and lower error thresholds were defined by adding or subtracting an adjustable fraction of the average standard deviation of the sample signals, according to the desired sensitivity of the monitoring system. Compared with other optical sensors, the main advantage of this system is that it showed a great flexibility upon variation of the welding metal or the joint geometries. In fact, once the chemical composition of the alloy was known and most plasma emission lines were identified, only a slight calibration of the software settings was necessary. It also showed promising results in semi-automatic and automatic arc welding process monitoring22.

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11.6 Electron temperature signals correlated to the welded joints positions in case of: (a) defect-free weld; (b) weld disruptions; (c) bead surface oxidation due to gas shielding interruption; (d) gaseous inclusions due to surface pollutant.

Qualitative variations of specific spectral features may also be an indicator of changing operating conditions, which may induce a weld defect. Palanco et al.23 correlated spectroscopic measurements with the occurrence of laser welding defects as notches and blowholes, during continuous wave (CW) laser welding of aluminium alloys. Plasma plume optical emission was spatially resolved by collecting light at different heights from the weld surface. Abrupt variations of the laser power were produced in order to generate the weld defects. Blowhole and notch defects were preceded by a sudden alteration of specific spectral features, also confirmed by fast video recordings of the plasma plume behaviour. Denkena et al.24 extracted some characteristic spectral features by analysing the plasma optical radiation. These signals like temperature, radiance and spectral lines, are normally superimposed within the same spectrum but they were demonstrated to be independent among each other and thereby suitable for a robust multiple input classification of weld defects. The developed monitoring algorithm was able to classify the occurrence of some defects such as weld failure, spatter and hole formation during lap-welding of zinc-coated steels.

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Sibillano et al.25 focused on the detection of shielding gas flaws and the formation of surface oxide layers on aluminium alloy laser welded buttjoints. Several optical spectra were acquired and investigated using different gas delivery systems, and changing the gas flow rate and the nozzle standoff distance. Experimental results showed that in the case of defective gas shielding there was an increase in the intensity of existing emission lines or the appearance of new ones belonging to the surrounding contaminant ambient air or to highly volatile alloying elements. This behaviour was correlated to the seam oxidation or to a significant loss of alloying elements, both detrimental to the mechanical properties of the joints. Unfortunately, a qualitative analysis of the optical spectrum, that is strongly influenced by the working conditions, is often unreliable and thus not suitable for effective industrial process monitoring, even though it may be useful to monitor particular aspects of the welding process. A new kind of spectroscopic investigation of plasma plume optical emission aimed to find a relationship between plasma dynamics and welding quality and was developed by Sibillano et al.26–28. The so-called covariance mapping technique (CMT) is based on the assumption that the acquired spectrum can be considered as a sampling function for the signal coming from the detector. Under this hypothesis, if xk(λi) is the optical intensity recorded at the wavelength λi of the kth spectrum, the covariance matrix of a series of N spectra is given by: Cij =

N N 1 N ⎤⎡ 1 ⎤ ⎡1 ( ( ) ) ( ) x λ x λ − x λ k i k j k i ∑ ⎥⎦ ⎣⎢ N ∑ xk (λ j )⎥⎦ ⎢⎣ N ∑ N k =1 k =1 k =1

[11.6]

The covariance matrix is clearly symmetric upon exchange of i and j. The normalized form of the matrix is generally used: nij =

Cij

[11.7]

(Cii ⋅C jj )1 2

In this way the values of nij lie in the range between −1 and 1, and the diagonal elements nii = 1 (obviously, each chemical species is fully correlated with itself). Covariance values close to 1 indicate correlated pairs of species, whereas matrix elements close to −1 denote anti-correlated pairs. A positive correlation value between two chemical species present in the spectrum, identified by their emission wavelength, indicates that they change as a function of a known parameter, i.e. that they are formed by a process that has similar characteristics. On the other hand, a negative correlation signifies that the two species are formed by competing processes. In case of non-correlation the species evolve through unrelated mechanisms and no useful information can be gained. In the analyses carried out, only the values of nij corresponding to a confidence level above 95% have been considered (Fig. 11.7). The threshold value fulfilling

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11.7 Detail of the covariance map showing the correlation between aluminium and oxygen emission lines of the spectrum, changing according to the inert gas flow rate: (a) effective shielding; (b) lack of gas shielding.

this criterion depends only on the number of spectra used in the covariance matrix evaluation. One of the main advantages of CMT resides also in its capacity of extracting useful information even from weak lines, provided that the change in their intensity can be statistically correlated to the variation of other lines of the spectrum. The CMT is thus particularly powerful to assess the contribution of spectral components with a low signal-to-noise ratio. Experimental results showed that CMT can be reliably employed to better understand the physical interactions inside the plasma plume and the influence of the variation of the main process parameters on the weld quality. In particular, it was demonstrated how, in case of laser welding of Al–Mg alloys, a change from correlation to anticorrelation or vice versa between these two species may indicate either a collapse of the keyhole due to a

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wrong welding speed or a major loss of magnesium in the melted zone caused by an excessive laser power29. Spectroscopic results were consistent with post-process energy dispersive X-ray (EDX) analysis, that measured the loss of ligand elements across the joints with respect to the base metal. Further investigations on the same alloy showed that correlation/anticorrelation between aluminium and emission lines of environmental gases (N and O) can be an excellent indicator of ineffective gas shielding conditions, leading to the formation of aluminium–magnesium oxides on the weld seam. CMT requires quite long acquisition and computation times, and therefore it cannot be used for real-time applications. Nonetheless once a preliminary study has pointed out the most significant correlations, couples of spectral lines can be selected whose correlation coefficient can be real-time monitored during the process. The temporal evolution of the correlation coefficients between aluminium, magnesium and oxygen emission lines, along the workpiece length, have been recorded in cases of sound and flaw welds. A significant drop of the signal below the significance level have been registered in cases of local defects, such as seam oxidation, lack of penetration or spatters (Fig. 11.8). Also, this spectroscopic technique was successfully demonstrated to be suitable for on-line monitoring of the laser welding process. Speed (mm/s) Correlation coefficient

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11.8 Temporal evolution of the correlation coefficient between Al(I) and O(I) and Al(I) and Mg(I) emission lines of the plasma radiation during a CO2 laser welding of Al–Mg alloy performed with variable welding speed. The two line thresholds indicate the 95% (α = 0.5) significance level of correlation/anticorrelation. For speeds below 50 mm/s the correlation drop is due to spatters; for speeds greater than 90 mm/s the correlation signal drop is caused by switching from full penetration to partial penetration regime; for too high welding speeds keyhole collapses and a conduction regime is established, during which there is no more plasma and all the wavelengths of the broadband optical emission from the weld zone are significantly correlated.

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11.4

Frequency analyses of acoustic and optical emissions

Generally the keyhole strongly fluctuates since any distortion of the pressure balance inside the capillary generates oscillations of its radius and shape. These fluctuations influence the expanding plasma dynamics. Oscillations of the keyhole modulate the vapour ejection and the plasma plume volume. Its consequent pulsating behaviour is the source of broadband acoustic waves, due to the compression of the ambient atmosphere, furthermore determining the fluctuation of the emanating optical radiation. Acoustic emission (AE), produced during laser welding of metals, hence contains information concerning plasma generation, interactions between hot vapours and the melt pool, thermal stresses in the workpiece and cracking. The frequency components of these emissions extend up to 1 MHz but identification of individual frequencies with specific physical processes is still an open field of research. Nonetheless the potential use of acoustic sensors for quality issue monitoring is attractive because of their simplicity and low cost. Detectable AE can be divided into airborne and structure-borne emissions. They are initiated by different physical mechanisms and their characteristic frequency ranges are different; nonetheless both emissions can contain useful information for monitoring the weld quality. Airborne emission is typically within the human audible sound range (between 20 Hz and 20 kHz). Its detection usually involves a microphone placed near the weld zone. The frequency range of structure-borne emission is in the ultrasonic range, from 50 to 600 kHz. Piezoelectric transducers, mounted on the backside of beam guide or focusing mirrors, at the end of the laser processing nozzles or directly on the workpiece, are employed to detect this kind of acoustic emission. Computer data acquisition systems usually complete the experimental set-up, performing the frequency analyses of the acquired signals or other conditioning algorithms, in order to extract the desired information on the weld quality. It has been demonstrated that ultrasonic emissions are mainly generated by the laser beam back-reflection modulated by the melt pool oscillations30. First studies addressed structure-borne emissions to the optical feedback of the laser back-reflections from the workpiece, entering in the laser cavity and returning amplified31. However, it has been experimentally observed that these signals are stronger on the mirrors closest to the workpiece and are significantly reduced when the formation of a strong plasma and a deep keyhole, trapping laser radiation, occurs, owing to lower transverse speeds or higher laser powers. These results confirm that the dominating physical mechanism originating ultrasonic emissions is the laser beam back-reflection from the melt pool. Structure-borne emission sensors

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have given good results for detecting material phase transformation and crack formation. Acoustic emissions in the audible frequency range are more likely to be related to the vapour-plasma ejection from the keyhole rather than laser modulation. Amplitudes of the AE do not generally bring useful information on the process since they are strongly affected by the noisy environment. Therefore, acquired signals are generally processed with a fast Fourier transform (FFT) in order to analyse their frequency components. Since the total intensity of the AE is a strong function of processing parameters, normalised power spectra are computed to facilitate a comparison among AE generated under different welding conditions. According to a theoretical model developed by Kroos et al.32,33, describing the dynamic behaviour of a keyhole for low welding speeds, the lowerfrequency components of the spectrum, below 2 kHz, can be associated with radial oscillations of the keyhole. Higher-frequency components correspond to the excitation of ‘organ-pipe’ axial and azimuthal resonances (Fig. 11.9). This model was able to predict the system eigenfrequencies, once the physical and thermal constants for the material were known. Many authors have adopted this model in order to explain their experimental results, trying to match the discrete components of the observed spectra with the predicted allowed frequency bands. Gu and Duley35 normalized to unity the FFT power spectrum in the 0–20 kHz range and established 20 subfrequency blocks with 1 kHz bandwidth each. The AE of different welds performed by varying the speed, the power, the focus position and the surface condition of the samples were compared. Even if it was not possible to assign specific frequency components to particular aspects of the physical interactions, the authors found that the lower frequency range can be associated with transient closure or collapse of the keyhole due to hydro-dynamical instabilities. A higher

Radial

Azimuthal

Axial

11.9 Three basic oscillation modes of the cylindrical keyhole model developed by Kroos et al. (adapted from Klein et al.34).

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frequency band, around 10 kHz, was shown to arise from ablation of surface oxides. Furthermore, the spectrum, as a whole, was found to contain sufficient information to distinguish acceptable welds from defective ones. By applying simple statistical criteria to the normalized spectra, it was possible to discriminate among overheated, full and partial penetration welds. An airborne acoustic signal has often been acquired in combination with the integrated optical emission (OE) from the plasma plume. Lenses or collimators, pointing at the weld area, were generally used to collect the light then transmitted, via an optical fibre, to UV, VIS or IR photo-detectors. Sampling at the same rate both acoustic and optical signal, it was possible to compare amplitudes and FFT spectra in the same frequency ranges. Assuming that the origin of the AE is the displacement of the ambient fluid created by the hot vapours exiting from the keyhole, Farson and Kim36 found that the sound pressure generated by the laser weld is proportional to the time derivative of the flow velocity of vapour entering the plume. Since the optical emission was supposed to increase approximately linearly with the vapour flow rate, it was predicted that the AE amplitude should vary as the time derivative of the optical signal. Experimental measurements confirmed this relationship, supporting the thesis that plasma fluctuations, modulated by keyhole oscillations, are the source of variation of both optical and acoustic airborne emissions. The relationship between AE and OE has been later corroborated by Szymanski et al.37 who supposed that the source of the acoustic signal is the pulsating movement of the plasma plume. Their results showed that by modelling the plasma as a pulsating sphere it is possible to describe the AE. The connection between the two signals in the frequency domain was clearer if the radiant and acoustic energy fluxes, proportional to the square of the two amplitude signals, were analysed. An exact comparison with the theoretical model of Kroos et al. was not possible, even if the experimentally observed peaks of the FFT spectra were in the frequency range predicted by the same model. A small laser power modulation caused the excitation of discrete frequency peaks in the spectrum. Process perturbations were ascribed to variations of absorption and refraction of the laser beam in the plasma plume that affect the energy transfer to the metal and the vapour pressure. Nakamura et al.39 compared the FFT spectra of the AE and the OE of spectral lines in the UV range, at different welding speeds. They found that for both signals the main frequency components move to higher frequencies with an increase in the welding speed, i.e. with a decrease in the penetration depth. In this way they could evaluate the transition between the deep and the shallow penetration mode welding. Several attempts have been made to develop non-contact AE sensors in order to detect specific kinds of defective welds. They are all based on the

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principle that, during keyhole formation, any perturbation of the process of intense vaporisation that leads to a defective weld, will result in a change in AE detection. Even though laboratory prototypes of AE sensors are still being investigated and published in the scientific literature, their application in the industrial field has mostly failed due to some intrinsic drawbacks. The changes in laser variables and the addition of a pressurised air-jet and/or inert gas jet, protecting the focusing optics from spatters and shielding the weld pool from oxidation, make the industrial environment noisy and it is difficult to detect with microphones the acoustic signal in the audible sound range. On the other hand, piezo-transducers need contact installation, which is not suitable for mass production. Many authors have focused their work on the investigation of the oscillations of the optical broadband emission only, assuming that they are strictly connected to welding seam defects. In fact, as previously mentioned, keyhole fluctuations must be reflected in the dynamics of the plasma plume. On the other hand, an unstable plasma above the workpiece surface can temporarily defocus the laser beam and additionally absorbs a certain amount of the laser power, lowering the transfer of energy to the material. In this way the coupling between laser radiation and the metal can be disturbed by a strongly fluctuating plasma, causing instabilities of the welds and leading to localized weld defects or shallower penetrations. Weld pool39 and keyhole models34 have predicted a number of free and forced oscillations modes with their eigenfrequencies. Klein et al.34 analysed the FFT spectra of the UV and near-IR light emission during welding with an industrial CW CO2 laser and they found a resonance response to modulated power. Their results cannot be explained by keyhole oscillations alone but must include weld pool oscillations as well. Ancona et al.40 showed experimental evidence of the validity of the keyhole oscillations model and used the same model to optimise the process parameters and the quality of the welds. FFT spectra of the welding plasma optical VIS/UV emission, acquired during CO2 laser welding of stainless steels, have shown a characteristic frequency distribution depending on the welding conditions. The whole FFT power spectra were divided and analysed into two distinct frequency ranges (500 Hz to 2 kHz and 2–20 kHz), where the theoretical model, respectively, predicted the radial and axial– azimuthal oscillations. In the range of radial oscillations, for incident laser power or travel speeds higher or lower than the optimised value, the detected spectra showed a shift towards lower frequencies, and the corresponding welds were, respectively, either partially penetrated or overheated. A clear correlation was also found between the penetration depth and/or the volume of the fused zone and the FFT spectra in the 2–20 kHz range (Fig. 11.10). The potential application of this technique was thus demonstrated to process quality monitoring and control of some important

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Real-time weld process monitoring 2000 W

100 FFT power spectrum (%)

1400 W 600 W 10

1

0.1

0.01 4

6

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10 12 14 Frequency (kHz)

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11.10 Normalised FFT power spectra from 2 to 20 kHz of the integrated optical emission acquired during a CO2 laser welding process on AISI304 (3 mm) performed at different incident powers: 600 W (partial penetration), 1400 W (full penetration) and 2000 W (overheating).

parameters such as the travel speed, the incident power and the beam focus position. Similar results were obtained by Haran et al.41 They developed a welding alarm system which analysed the frequency spectrum of the near-IR and UV/VIS radiation emitted during a Nd:YAG laser welding. They found a distinct frequency peak around 3 kHz in the case of a fully opened keyhole and studied how this peak vanished or changed its amplitude according to the variations of process parameters. They showed how specific issues of overlap welding, like excessive gap between the plates and seam-tracking, could be managed by monitoring this defined peak of the optical FFT spectrum.

11.5

Smart systems

There are a lot of interactions during the laser keyhole welding that can be significantly influenced by the process parameters and external factors. These phenomena make the process very complex. However, they also provide many sources for monitoring the laser welding process itself. As we have already seen, most studies have focused on the relationships between particular signals and weld defects but it has been shown that no single sensor can reliably monitor all laser welding defects. By combining multiple sensors, the advantages of each sensor are pooled and ‘intelligent’ algorithms can be developed to detect several weld defects and realise more robust systems.

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Park et al.42 used a fuzzy multi-feature pattern recognition to design an algorithm that estimated the quality of the welds starting from the optical signals acquired by an IR photodiode and two UV photodiodes, positioned at different angles to the laser beam. The system was able to detect weld defects caused by changes in power, classifying weld qualities as optimal, slightly low or low heat input, and focus misalignment. Spatters were also detected with a different algorithm. Both algorithms worked well in tailored blank applications. Badyanov et al.43 showed that it is possible to classify the welds simply by fitting the experimental data of several photodiodes with empirical formulas that change according to the heat input. Sun et al.44 focused on real-time weld penetration monitoring by using a sensor fusion of IR, UV and acoustic sensors. A frequency analysis of the acquired signals was accomplished and then data fusion was studied for lap welds that were either adequately or inadequately penetrated. Several fusion methods were tried in order to successfully classify laboratory and industrial welds. Zhang et al.45 fused the signals of a photodiode sensor and a plasma charge sensor in order to realise a double closed-loop control system of the focal point position and of the nozzle–workpiece distance. Kawahito et al.46 measured the laser reflected light and the heat radiation at 1300 nm from the laserirradiated area with two photodiodes and used them as in-process monitoring signals in micro-butt welding with a pulsed laser beam. It was found that heat radiation was proportional to the diameter of the molten pool as well as the penetration depth and the reflected light was sensible to the presence of large gaps; therefore an adaptive system for gap monitoring was developed by combining these two signals. Artificial neural networks (ANN) are non-linear systems with adaptation and generalisation capabilities that make them good candidates in modelling and predicting such a complex process as laser welding. The success of an ANN depends on its appropriate design and training, together with the proper signal processing of the input data. Ghasempoor et al.47 used the main process parameters (speed, gas nozzle and beam location, stand-off distance) and some coefficients calculated from the power spectrum of the signals collected from IR, UV and visible photodiodes, as inputs to a neural network. The output variables for the training experimental set consisted of the fusion zone area and other geometrical features of the joint crosssection. After optimisation of the architecture of the ANN, it was possible to detect lacks of fusion and promising results are claimed by the authors for also detecting porosity. A very similar approach was used by Kawahito and Katayama48 for in-process monitoring of microspot laser lap welding of copper sheets (Fig. 11.11). The input layer of their ANN was the IR radiation measured from the weld pool and the reflected laser radiation. The network was trained in order to get information on the spot-weld diameter and on the shear strength of the joint. In fact, it was found that

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Real-time weld process monitoring Infrared ray sensor (wavelength: 1300 nm) High-speed camera

Interference filter Notch filter

He–Ne laser

Laser head optics

Reflected beam sensor Fibre

Intelligent control system (neural network)

Dichroic mirror Feedback (adaptive) control Fundamental wavelength YAG laser Sample x,y table

AOQ-switched SHG–YAG laser Mirror

11.11 Schematic drawing of experimental set-up of monitoring and laser control system (SHG–YAG: second harmonic generation–yttrium aluminium garnet). Developed by Kawahito and Katayama.48 (‘Inprocess monitoring and feedback control during laser microspot lap welding of copper sheets’ reproduced with permission from Journal of Laser Application, Volume 16, May 2004. Copyright 2004, Laser Institute of America, Orlando, Florida. The Laser Institute of America disclaims any responsibility or liability resulting from the placement and use in the described manner.)

the reflected intensity decreases with the increase of the spot diameter. The reason was ascribed to the mechanism of keyhole formation and laser radiation trapping. The IR heat radiation increased linearly as the molten pool expanded. It was deduced that these two signals could be effective for developing a control system based on an ANN, capable of terminating the laser irradiation properly once the desired spot weld dimensions had been achieved. Another application of ANN to diagnose weld faults in laser processes has been provided by Luo et al.49 They performed an FFT and wavelet analysis of the audible signal coming from the laser weld zone. Results of this analysis were used as inputs of the ANN. After some training the neural network was able to distinguish good welds from defects due to excessive gaps or edge misalignments.

11.6

Other systems and future trends

The feasibility of using electric and magnetic fields as a tool for reducing the perturbing effect of the plasma and improve bead profiles and

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penetration depths in high-power laser welding has been demonstrated by Tse et al.50,51 and Peng et al.52 The underlying principle is that by using electromagnetic fields it is possible to drive away the plasma plume or at least reduce its electron density, so that a better coupling of the laser beam with the workpiece is achieved. The following step has been to develop charge sensors capable of detecting the charge potential induced by the plasma in the space between the nozzle and the workpiece, and to correlate this signal with the quality of the joints. The charge observed is due to the mass and mobility difference between the electrons and the positive ions in the plasma cloud. Electrons are about 50 times faster than ions, so they separate as they leave the keyhole opening and flow towards the nozzle. It is assumed that the plasma plume in the middle has a high conductivity, so an electric field is established between the electrons concentrated near the nozzle (cathode) and the positive ions near the workpiece (anode). A deeper or wider keyhole results in a greater number of charged particles within the plasma and a higher plasma temperature, and therefore the voltage is proportional to the plasma temperature and to the weld penetration. Li et al.53 used an electrically insulated welding nozzle, hence called the plasma charge sensor (PCS), as a passive electric probe to detect this potential. Since there was no voltage applied to the nozzle and thus no additional field created, the sensing method did not influence the welding process. The PCS has been applied to different weld types: lap welds, butt welds and spot welds. It has been able to give qualitative but not unique information on the gas shielding, since the signal increases with the gas flow at lower rates but then decreases in the higher flow regime. The increasing characteristic is possibly due to the enhancement of ionisation of the shroud gas that usually has a lower ionisation potential than the workpiece material. However, at higher flow rates the gas flow may blow away the plasma or charged vapour, reducing the number of electrons reaching the nozzle. The mean PCS voltage was shown to provide a measure of the penetration. As mentioned before, a higher signal corresponds to higher penetrations and plasma temperatures. It was thus possible to detect weld humping due to excessive welding speed or incorrect laser power. Weld disruptions were clearly indicated by drops of the PCS signal close to zero, due to the collapse of the plasma plume. In case of excessive gap, although a keyhole may be formed and a plasma cloud sustained, the opening along the weld can allow some of the plasma to be blown through to the opposite side of the sheet. When this occurs, the charge formation mechanism is reversed, due to the plasma position on the opposite side of the workpiece. This reversed charging mechanism was very clearly seen as a polarity reversal in the PCS signal. Additional experiments were also

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conducted on a multi-sector sensor nozzle in a quadrant configuration to allow study of the symmetry of the plasma and thus to detect beam misalignment. Furthermore, the PCS voltage was found to increase as the nozzle was moved closer to the workpiece. This effect is probably due to the higher availability of electrons when the nozzle subtends a greater solid angle at the plasma. In addition, the plasma electron density is a reducing function of the distance from the thermal energy source. A plasma charge current was therefore used by Zhang et al.54 as a tool to develop a closed-loop control of the nozzle-to-workpiece distance and the focal position of the beam. The PCS was therefore shown to be a reliable and robust non-contact method for on-line weld monitoring, well suited to installation on existing laser welding stations, despite the fact that unfortunately the nozzle is not very well insulated and so the PCS voltage can be strongly affected by the surrounding noise of an industrial environment. Plasma sensors described so far are single-point or spatially integrated detectors. They have a high temporal resolution of the recorded signal and a low price. Recent investigations revealed that the reliability of weld quality prediction can be significantly improved by using systems, such as charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS) cameras, allowing a spatial resolution of the welding zone. Additional illumination is needed to visualise the workpiece surface and the weld pool without the disturbing effect of the plasma plume. For this purpose, a diode laser is deflected into the optical path of the working laser and a CMOS camera observes, through a dichroic mirror, the interaction zone likewise coaxially to the laser beam. This method makes it possible to measure the melt pool geometry with a high spatial resolution. To determine the melt pool boundary, the computed image has to be cleared from noise, which is done by filtering. Regaard et al.55 correlated significant changes of the melt pool contour to lack of fusions and gaps. Kratzsch et al.56 and Bardin and coworkers57,58 found that, from the analysis of the longitudinal sections of the intensity distribution of the acquired images, it is possible to discriminate a fully opened keyhole from a partially penetrated one (Fig. 11.12). In the case of full penetration welding, the camera images show a doughnut shape and the intensity minimum in the keyhole centre is due to the loss of plasma from the bottom of the workpiece. This minimum is not visible for a partially penetrated keyhole. Suitable analyses of cross-sections and longitudinal sections of the keyhole image also allowed detection of beam misalignment of the seam track, gaps in butt-joints and weld spatters. The main drawbacks of camera-based systems are the disturbing effects of vapour or pollutant to the image-processing algorithm and the surface

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160 Partial penetration 140 Full penetration

Partial penetration

Intensity (a.u.)

120 100 80 60 40 20 0

0

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11.12 Images of the keyhole and their corresponding intensity profiles during a full and a partial penetration laser welding process. The intensity profile represents the average profile along the three lines (adapted from Bardin et al.57).

conditions (non-reflecting coatings or contaminants) or a high angle of illumination that does not allow a good external illumination, besides the image distortions due to thermal effects for short focal lengths. Although cameras with frame rates up to 30 kHz have been developed, it is still less than that required for a real-time closed-loop control. Furthermore, the high cost of ultra-fast cameras and the high computing time for processing such a huge amount of images make this approach very effective for scientific research but too expensive for industrial applications. A wide variety of laser welding sensors is already commercially available. Seam tracking systems (LPF from Precitec, Robo-Trac from ServoRobot) are based on laser illumination of the welding edges and aim to automatically follow the joint geometry within the tolerances. The so-called plasma monitoring systems are multi-photodiodes (LWM-C from Precitec, WeldWatcher from 4D, PM7000 from Prometec) or spectroscopic optical sensors (TRWOC from T.E.R.N.I. Research) for supervision of each laser weld. Camera-based sensors (PD2000 from Prometec, Digi-Las from ServoRobot) perform geometrical melt pool analysis to gather relevant quality information. All these systems have been demonstrated to work well once the welding procedure is highly reproducible, but for any change of the working conditions (i.e. joint geometry, welding metal, etc.) they need new calibration and

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settings. Their flexibility is therefore limited, like their capability of identifying the cause of the defect, once it has been detected. In the future the ‘intelligent’ fusion of various sensor technologies will increase the degree of reliability of process monitoring and will hopefully give the chance to develop flexible and robust closed-loop control systems. One possibility would be to embed in a unique system pre-process (seam tracking), inprocess (camera and multiple photodiodes) and post-process (bead profile vision camera) sensors.

11.7

References

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35 h. gu, w.w. duley, A statistical approach to acoustic monitoring of laser welding, J. Phys. D: Appl. Phys. 29, 1996, 556–560. 36 d.f. farson, k.r. kim, Generation of optical and acoustic emissions in laser weld plumes, J. Appl. Phys. 85(3), 1999, 1329–1336. 37 z. szymanski, j. hoffmann, j. kurzyna, Plasma plume oscillations during welding of thin metal sheets with CW CO2 laser, J. Phys. D: Appl. Phys. 34, 2001, 189–199. 38 s. nakamura, m. sakurai, k. kamimuki, t. inoue, y. ito, Detection technique for transition between deep penetration mode and shallow penetration mode in CO2 laser welding of metals, J. Phys. D: Appl. Phys. 33, 2000, 2941– 2948. 39 n. postacioglu, p. kapadia, j. dowden, Capillary waves on the weld pool in penetration welding with a laser, J. Phys. D: Appl. Phys. 22, 1989, 1050–1061. 40 a. ancona, t. sibillano, t. maggipinto, f. ottonelli, p.m. lugarà, Experimental detection and theoretical prediction of laser welding plasma oscillations, Proc. Int. Workshop on Detecting Environmental, Industrial and Biomedical Signals, Bari 2002, 41–59. 41 f.m. haran, d.p. hand, s.m. ebrahim, c. peters, j.d.c. jones, Optical signal oscillations in laser keyhole welding and potential application to lap welding, Meas. Sci. Technol. 8, 1997, 627–633. 42 h. park, s. rhee, d. kim, A fuzzy pattern recognition based system for monitoring laser weld quality, Meas. Sci. Technol. 12, 2001, 1318–1324. 43 b.n. badyanov, a.a. elizarov, y.f. kolupaev, Application of the step-by-step approximation method for the computer control of laser welding processes, Meas. Tech. 46(2), 2003, 162–165. 44 a. sun, e. kannatey-asibu, m. gartner, Monitoring of laser weld penetration using sensor fusion, J. Las. Appl. 14(2), 2002, 114–121. 45 x. zhang, w. chen, p. jiang, c. liu and j. guo, Double closed-loop control of the focal point position in laser beam welding, Meas. Sci. Technol. 14, 2003, 1938–1943. 46 y. kawahito, m. kito, s. katayama, In-process monitoring and adaptive control for gap in micro butt welding with pulsed YAG laser, J. Phys. D: Appl. Phys. 40, 2007, 2972–2978. 47 a. ghasempoor, p. wild, m. auger, r. mueller, Automatic detection of lack of fusion defects in CO2 laser gear welding, J. Las. Appl. 15(2), 2003, 77–83. 48 y. kawahito, s. katayama, In-process monitoring and feedback control during laser microspot lap welding of copper sheets, J. Las. Appl. 16(2), 2004, 121–127. 49 h. luo, h. zeng, l. hu, x. hu, z. zhou, Application of artificial neural network in laser welding defect diagnosis, J. Mat. Proc. Tech. 170, 2005, 403–411. 50 h.c. tse, h.c. man, t.m. yue, Effect of electric and magnetic fields on plasma control during CO2 laser welding, Opt. Las. Eng. 32, 1999, 55–63. 51 h.c. tse, h.c. man, t.m. yue, Effect of magnetic field on plasma control during CO2 laser welding, Opt. Las. Tech. 31, 1999, 363–368. 52 y. peng, w. chen, c. wang, g. bao, z. tian, Controlling the plasma of deep penetration laser welding to increase power efficiency, J. Phys. D: Appl. Phys. 34, 2001, 3145–3149. 53 l. li, d.j. brookfield, w.m. steen, Plasma charge sensor for in-process, noncontact monitoring of the laser welding process, Meas. Sci. Technol. 7, 1996, 615–626.

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54 x. zhang, w. chen, p. jiang, j. guo, z. tian, Modeling and application of plasma charge current in deep penetration laser welding, J. Appl. Phys. 93(11), 2003, 8842–8847. 55 b. regaard, w. fiedler, s. kaierle, Error detection in lap welding applications using on-line melt pool contour analysis by coaxial process monitoring with external illumination, Proc. Fourth Int. WLT-Conf. LIM 2007, Munich, 471–475. 56 c. kratzsch, p. abels, s. kaierle, r. poprawe, w. schulz, Coaxial process control during laser beam welding of tailored blanks, Proc. SPIE 3888, 2000, 472–482. 57 f. bardin, a. cobo, j. m. lopez-higuera, o. collin, p. aubry, t. dubois, m. hogstrom, p. nylen, p. jonsson, l.d.c. jones, d.p. hand, Optical techniques for real time penetration monitoring for laser welding, Appl. Opt. 44(19), 2005, 3869–3876. 58 a. cobo, f. bardin, j. mirapeix, d.p. hand, l.d.c. jones and j.m. lopez-higuera, Optoelectronic device for non-intrusive focal point measurements and control of the laser welding process, design note, Meas. Sci. Technol. 16, 2005, N1–N6.

WPNL2204

WPNL2204

Index

A-scan signals 117 absorption 262 variation of 276 acoustic emission (AE) 250–1, 252, 274–8 sensors 279 testing 104–5, 110–14, 124 acoustic transducers 106 electromagnetic (EMAT) 107–8, 119, 120, 122 acoustic wave spot welder 121–2 actuation, electrode 239, 241, 245, 251 adaptive control method 25, 121–2 feedback 5 partitioned 24 air cylinders 241 weld heads 251 aluminum 90–1 analytical equations 4 anode heating 26, 28 arc behavior interference 82, 83, 214 length regulation 31–5 light radiation 48, 84 -metal interaction zone 70 variations 27 weld pool surface and 200 arc sensors 15–43 consumable electrode processes 25– 31 further information 41 future trends 39–41

historical perspective 15–20 non-consumable electrode processes 20–5 power supply 31–6 with rotating arc 135–8, 138–44 signal processing and control 36–9 with torch weaving 132–5 arc signals see signal processing and control arc welding 80, 113, 115, 131, 132, 191 plasma (PAW) 20, 199–201, 205–7 submerged (SAW) 18, 25, 82, 88 see also GMAW; GTAW arc-light-based applications, optical sensor 69–70 area scan 191 argon shielding gas 16, 22, 34, 149–50, 215, 218 artificial factors decomposition of complex system 9–10 intelligence techniques 257 neural networks (ANN) 279–80 automated on-line monitoring sensor 260 automatic seam tracking system 152, 155 automatic voltage control (AVC) 16, 19–20, 20–1, 25 closed-loop 22–4 automotive industry 260 AWS standard system (US) 164

289 WPNL2204

290

Index

back-face technique 86 barium titanate (BaTiO3) 106 base metal composition 88 bead, weld 8–9, 158, 165, 170, 264 width 83–5, 86, 180, 198–9 bead-on-plate welding 215, 217, 225, 265 bidirectional reflectance distribution function (BRDF) 153 blackbodies 75–7 blowhole defects 270 Boltzmann method 78, 268 Bragg cell 120 Bremsstrahlung, inverse 261–2 B-scan technique 120 burnoff rate model 26, 34 burn-through 176 butt joints 119, 121, 166–8, 175, 202, 281 categories 166 cameras -based systems 282–3 complementary metal oxide semiconductors (CMOS) 60–2, 282 Control Vision, Inc. 63, 195 high-speed 62, 72, 217, 229 LaserStrobeTM stroboscopic video system 63–4, 229, 232 monochrome 45 near-IR 62 Olympus i-Speed high-speed 62, 234 red-green-blue (RGB) threechannel 46 spectral 62 stroboscopic video 63–4 thermographic infrared (IR) 62, 82 ultraviolet (UV) 62 see also CCD cameras CCD (charge-coupled device) cameras 64, 72, 174, 196, 282 characteristics 53–4 comparison with CMOS 60–1 detector array 70 pixel structure 57–8 pool surface and 213–14, 214–15

principle of 57–60 seam monitoring and 153, 155, 158–60 sensors 66 structure of 54–7 two-phase 58–9 center of gravity defuzzification 24 charge light to signal conversion 57–8 readout technique 59–60 sensors 281 transfer technique 58–9 cladding power monitor (CPM) 265 clamping force 241 closed loop control systems 260, 264, 279 CLUETM laser receiver 120 coaxial viewing 195 compact muon solenoid (CMS) conductor 118 complementary metal oxide semiconductors (CMOS) cameras 60–2, 282 complex system, artificial decomposition of 9–10 compression waves 105–6, 108, 117–18 computers 70, 215, 219 data acquisition systems 274 embedded technology 36, 39–40 hardware/software 238, 257 vision field 68 visual technique 180 conditions, welding 3, 4–5, 6, 7–10, 264, 275 constant (long-term) deviations 6 consumable electrode processes 18, 25–31 dynamic models 27–31 steady-state models 26 contact-tube-to-work distance (CTWD) 18–19, 27–30, 32–5, 40 continuous wave (CW) laser 270 interferometer 108–10 control variables (inputs) 3–4 Control Vision, Inc. camera 63, 195 cooling rate 97–9

WPNL2204

Index coupled system 4 covariance mapping technique (CMT) 271–3 cracks 119 crater 170 growth 110–11 hot 88 surface 170 craters 170–1, 176 cruciform joints 168 current stepping 248–9 cylindrical keyhole model 275 deep penetration laser beam 260–2 defects 121, 176 blowhole 270 detection algorithm 269 shaping 165, 170–2 deflection 144 defocusing 262 deviations see variations diffuse reflection 49–50 Digi-Las system (ServoRobot) 283 digital signal processing (DSP) technology 36, 40–1 diode laser 153 disturbances 3–4, 7 double butt joint U-shape 166 V-shape 166 double side butt joint J-typed 168 V-typed 168 double-side visual sensing system 196 dynamic model 5, 6–7 non-linear 5–6 edge -flange butt joint 166 points 219 tracking method 215 welding, laser 158–62 efflux plasma charge sensor (EPCS) 199–200, 205 Einstein, Albert 52 electrical contacts, theory of 242 electrical signals 251–2

291

variations 17 electrodes 238, 241, 243, 250–1 actuation 239, 241, 245, 251 consumable processes 25–31 deflection 246 displacement 253–5, 256–7 -electrode resistances 243 emissions 82 force 249, 252 non-consumable processes 20–5 RWMA class 2 253 -sheet contacts 243 shift 150 wear 248 electromagnetic (EM) factors acoustic transducers (EMAT) 107–8, 119–22 emissions 81 fields 280–1 GMAW 150–2 oscillation sensor 144–9 radiation 74 spectrum 47–8 electrons 201–2, 261–2 -to-voltage conversion 61 beam (EB) welding 111, 118 temperature, plasma 267–70 electro-optics 45, 52–3 element effects, minor 88–92 embedded computer technology 36, 39–40 encoders 251 end point detection 138–44 energy dispersive X-ray (EDX) analysis 273 European EN standard system 164 expulsion event 255, 257 external-light-based applications, optical sensors 70–2 fast Fourier transform (FFT) 275–8, 280 feedback adaptive 5 control 252 sensor 205–9, 210 signals 131

WPNL2204

292

Index

Fermat’s principle 194 fiber optic device 85 field of view (FOV) 155 fillet welding 138–44 finite element analysis (FEA) 194 Firestone, F.A. 104 floating diffuse amplifier (FDA) method 59–60 fluctuations 3–4 fluid flow 224 flux cored arc welding (FCAW) 18, 25 force change 253–4 4D (company) 283 frame transfer (FT) CCD 55–6 free surface deformation and GTAW 223 Fresnel absorption 261 front-face method 87 full frame transfer (FFT) CCD 54–5 full penetration 179, 186–91 passim, 220–7 passim, 234, 263–4, 276 fusion lack of 170–1 level of 2 methods 279 fuzzy logic controller 24–5 fuzzy multi-feature pattern recognition 279 gain-scheduling adaptive controller 23–4 gamma-rays 48, 104 gas metal arc welding see GMAW gas shielding see shielding gas gas tungsten arc welding see GTAW Gaussian characteristics 21 GMAW (gas metal arc welding) 18, 20, 25, 27–8, 30–1 bead, weld and 165 crack formation and 111 electromagnetic monitoring 150–2 future trends 72 head 147 laser visual monitoring 152–62 penetration and 186 pool boundary sensing 217–20 pulsed 20, 31–2, 36, 38, 68

robots and 131 with rotating arc 141 temperature and 81, 121 through-arc monitoring 132–50 governing equations 4 grads (gradients) space concept 180–1 grounding practices 251 GTAW (gas tungsten arc welding) 15– 16, 18–19, 20–1, 25 fluid flow/heat transfer and 224 free surface deformation and 223 numerical simulation of 221 oscillation and 144 penetration and 88, 116, 186, 192, 221 pool surface and 67, 232 pulsed-current 214 spectral analysis and 48 structured light and 178 temperature and 81 vision-based sensors and 213–17 Hall effect current sensors 137, 249 heat -affected zone (HAZ) 2 radiative 74, 76–80 transfer in weld pool and GTAW 224 heterodyne detection 120 high frequency direct current (HFDC) 239–40, 253 high-performance solid-state power electronics 39 high-speed cameras 62, 72, 217, 229 historical perspectives 15–20 hot cracking 88 Idaho National Engineering Laboratory (INEL) 120 illumination laser 232–3 see also light image enhancement 215 processing software 218–19 Imasonic (F-Besancon) 118 incident mechanism, structured light 231

WPNL2204

Index inflections technique 192–3 infrared (IR) emissions 264, 265 thermography 81, 99 wavelengths 48 infrared (IR) sensors 74–99 cooling rate 97–9 penetration depth 85–92, 191–3 photodetectors 276 photodiodes 48, 279–80 position control 92–7, 98 temperature distribution 80–2 theory of 74–80 weld bead width 83–5, 86 inputs 4–5, 6–7 anode power 28 control variables 2–3 history of 5 welding parameters 7–9 Institute of Material Joining (Shandong University) 70 intelligent algorithms 278 interfacing, sensors and 249–51 interference, arc radiation 82, 83, 214 interferometers 120 interline transfer (IT) CCD 56–7 intermediate variables 8–10 inverse Bremsstrahlung 261–2 inverse problem 5 inverter-type power supplies 137, 239–41 iron sulfide 91–2 I-shape butt joint 166 ISO welding standard system 164, 165, 170 isolated gate bipolar transistor (IGBT) 201 isolation practices 251 isothermal lines 191 I-typed butt joint, no gap 175 I-typed groove butt joint 166–7 Japanese JIS standard system 164 joints with gap 92–5 groove geometry 9 penetration 115–16

293

perfectly fitted 95–7 tracking 93–5, 95–6, 115–16 types of 165–9 see also butt joints; V-groove joints joule heating 26, 28, 30, 241, 243 Journal of Laser Application 262, 280 keyhole welding 202–8 passim, 260–4 passim, 272–7 passim, 281–3 cylindrical model 275 keyhole formation 111–12 Lagrange multiplier 221, 222, 223–4 Lamb waves 105–6, 117 Lambert surface 184 lap welds 120, 175, 265, 270, 281 Laser Institute of America 262, 280 laser visual monitoring, GMAW 152–62 neural network for 3D seams 157–8 pulsed Nd:YAG edge welding 158–62 seam tracking 153–7, 158–62 laser welding 111–12, 118, 260–84 acoustic emission (AE)/optical emission (OE) and 274–8 future trends 280–4 illumination 70, 72, 195, 217, 283 light intensity and 263–6 optical sensors and 263–73 principles of 260–3 smart systems and 278–80 spectroscopic techniques and 266–73 stripes 198, 231, 233, 235–6 ultrasonics 108–10, 119–21, 123, 193–4 LaserStrobeTM stroboscopic video camera system 63–4, 229, 232 LasirisTM SNF uniform intensity laser projector 64 lead metaniobate 106 lead zirconate titanate (PZT) 106 least squares method 87 Lesnewich’s equation 26, 28

WPNL2204

294

Index

light intensity 149–50, 263–6 laser 70, 72, 195, 217, 283 reflectance 181 to signal charge conversion 57–8 see also structured light detection line scan 81, 191, 192–3 linear discriminant function analysis 113 linear power supplies 240 linear variable displacement transducers (LVDT) 249 liquefied natural gas (LNG) tanks 153–5, 157 liquid-to-solid phase transformation 113–14 lithium niobate 106 long waves 48 longitudinal waves see compression waves long-term deviations 6 Lorentz forces 107, 144 LPF system (Precitec) 283 LWM-C system (Precitec) 283 Lyapunov stability 24 machine variables 247–8 vision 45 magnetic arc oscillation 144–6, 148 see also electromagnetic factors magnetic fields 251 magnetostriction 107 main welding current (MWC) 201, 203–4 manufacturing environment 249 martensite formation 112–13 material characteristics 247–8 mechanical factors strength 246 tip dressing systems 249 medium frequency direct current (MFDC) 239–40 melt-in welding 202 melting rate 30–1 metal oxide semiconductor (MOS) capacitor 54, 57–8

metal transfer process 72 microwaves 48 minor element effects 88–92 misalignment 165, 172, 173, 176 monochrome camera 45 Mulhauser, O. 104 narrow groove welding 145 GMAW 147–9 Nd:YAG laser welding 262, 265, 278 pulsed 82, 158–62 near-IR cameras 62 light emissions 277–8 range, spectroscopic analysis of 267 neural networks for 3D seams 157–8 artificial (ANN) 279–80 fuzzy controller and 39 noise 266 elimination 215 nominal constants 3, 6, 7–9 non I-typed butt joint 167 non-consumable electrode processes 17, 20–5 dynamic models 22–5 steady-state models 21–2 non-destructive testing (NDT) 111, 193 non-intrusive systems 265, 267 non-linear systems 279 dynamic model 5–6 interval model control 207–9, 210 non-transferred plasma charge 203–4 sensor principle 201 notch defects 270 nugget, weld dimensions 117, 121–2, 246, 251, 253, 256 resistance and 241, 243–5, 247–9, 252, 255 numerical simulations 4, 192, 221, 225, 257 observation, weld pool surface 232–6 Olympus i-Speed high-speed camera 62, 234

WPNL2204

Index optical emission (OE) 274–8 integrated 276 optical factors acoustic wave generation 122 encoders 249 fibre laser phased array 120–1 radiation 270 signal oscillations 266 triangulation 159 optical sensors 45–72 applications 69–72 CCD cameras 53–4 CMOS 60–2 electro- 52–3 high-speed cameras 62 laser welding and 263–73 reflection 49–50 refraction 50–1 spectrum 47–9 systems 62–8 thin lens principle 51–2 oscillation keyhole 274, 276–7 sensors 144–9, 187–91 sound waves 105 out-of-control values 247 outputs 2–4, 4–5, 6–7, 7–10 voltage 250–1 over-convexity defect 171–2 over-height defect 171–2, 176 overlap welding 278 defect 171–2 parameters, welding 2–3, 4 partial penetration 186–7, 188–91, 221–2, 264, 276–7 surface shape and 235–6 partitioned adaptive controller 24 parts, poor fit 249 PD2000 system (Prometec) 283 penetration depth 8, 80, 121, 186–210, 264 control system 82 infrared (IR) sensors and 85–92, 191–3 measurement 164–5 minor element effects on 88–92

295

oscillation sensors 187–91 plasma charge-based sensors 199–204 sag depression 196–9 sensor feedback 205–9, 210 ultrasonic sensors and 193–5 vision-based sensors 195–6 weld pool and 187–91, 221–2, 222–3 see also full penetration; partial penetration phased array technology 122–3 ultrasonic 108, 109, 118 photodetector diodes 81 see also electro-optics photodiodes 83, 86–7 signal 264 photons 57–8 -to-electron conversion 61 photosensors see electro-optics physical variables 245–6 piezoelectric factors load cells 249 polymers 106 sensors 106–7, 111–13, 123, 277 transducers 107, 108, 119, 123, 274 PIN diodes 53 pit defect 172, 176 Planck’s law 47, 52, 75, 77 plasma absorption 261 arc welding (PAW) 20, 199–201, 205–7 charge sensors (PCS) 199–204, 281–2 emission 263–4, 277 monitoring systems 283 radiation 273 spectroscopy 267 temperature 69–70, 201, 267–70 plasma plume 261–3, 265–6, 269, 272, 274, 281 optical emission 266–7, 270–1, 276–7 plate waves see Lamb waves PM7000 system (Prometec) 283 point sensing 81, 85, 88, 191 polyvinylidene fluoride (PVDF) 106–7 pool surface, weld 8–9, 213–36 deformation 221–2, 222–3, 225–9, 231

WPNL2204

296

Index

geometry 213–17 GMAW boundary sensing 217–20 GTAW and 213–17, 232 modeling and simulation of 220–9 observation of 232–6 optical sensors and 64, 67, 69–70, 72 penetration and 195, 200–4 shape 229–30, 231 specular reflection and 229–32 stereovision system and 68 pool, weld measurement 65–6, 115–16 oscillation 69, 187–91 penetration 71, 72 sag depression 196–9 shape 85, 195 traveling 190 porosity, in welds 119 position control, infrared (IR) sensors 92–7, 98 power supply 25, 30, 31–6 anode input 28 constant current 15 inverter-type 137, 239–41 output 245, 248 pulsed current 31 variations 82 Precitec 283 pre-pulses 248 process see welding process profiles, weld 164–84 joint types and 165–9 measurement necessity 164–5 shape from shading detection 180–4 structured light detection 173–80 surface shaping defects 165, 170–2 Prometec 283 PSQ 6000 ultrasonic inspection system 118 pulse current variation 36 frequency modulation 35–6 pulse echo technique 104–5, 115 compression/shear waves 118 pulsed techniques GMAW 20, 68

GTAW 214 laser 82, 108, 119, 158–62, 217 Q-switching 120 quality control 82–99, 111 quartz, crystalline 106 quasi-PAW (Q-PAW) 205–7 quasi-keyhole process 205–8 quasi-steady state conditions 224 R/D Tech (F-Villebon sur Yvette) 118 radiative heat 74, 76–80 radio frequency 48 radiography 104 ray tracing 51 Rayleigh waves 105–6 red-green-blue (RGB) camera 46 reference-beam interferometer 113 reflectance map equation 180–3 reflection diffuse 49–50 laser pattern 67 law 231, 234 specular 49–50, 69 ultrasound 115 refraction 50–1, 262 variation of 276 resistance spot welding (RSW) 111, 118, 238–58 algorithms 152–3 further information 258 future trends 255–8 interfacing and 249–51 sensors 149–50, 249–51 statistical process control (SPC) 247–9 systems and dynamic characteristics 239–41 weld formation and signals 241–5, 246, 251–2, 253–5, 256, 257 resistance temperature detectors (RTDs) 81 Robo-trac system (ServoRobot) 283 robots 24, 39, 131, 283 Rogowski coil 249–50 root-pass welds 115 root-side seam width 264

WPNL2204

Index rotating arc sensors 135–8, 138–44 Runge-Kutta method 142 RWMA class 2 electrodes 253 sag depression 178–9, 196–9 scanning infrared (IR) sensor 83 scattering 262 seam tracking 131–2, 153–7, 283 arc sensors 30, 148 automatic 158–62 seams, weld 9, 131–62 GMAW electromagnetic monitoring 150–2 GMAW laser visual monitoring 152–62 GMAW through-arc monitoring 132–50 monitoring methods 131–2 sensor classifications 132 self-regulation 18–19, 28, 31–3, 40 of arc length 137 sensors -to-workpiece distance 151–2 classifications 132 interfacing and 249–51 servomotors 241 ServoRobot 283 shading see shape from shading detection Shandong University (China) 70 shape defects, surface 165, 170–2 parameters, profile 164 pool surface 200–1 weld 174 shape from shading detection 180–4 application 183–4 reflectance map equation 180–3 shear waves 121–2, 193, 252 see also transverse waves sheet-sheet resistance 243–4 shielded metal arc welding (SMAW) 18 shielding gas 49, 145, 251 argon 16, 22, 34, 149–50, 215, 218 laser welding and 263, 269–71, 273, 277

shipping containers 153–4, 157 short-term disturbances 6–7 signal processing and control 36–9 differential method 36–7 fuzzy logic 38–9 laser welding 261 resistance spot welding (RSW) 241–5, 246, 251–2, 253–5, 256, 257 template matching 37–8 ultrasonic probing 119 weld formation and 241–5, 246, 253–5, 257 signature curves 118 silicon controlled rectifier (SCR) 239–40 single butt joint U-shape 166 V-shape 166 single side butt joint J-typed 168 V-typed 166, 168 smart systems 278–80 Snell’s law 50–1 Sokolov, S. 104 sound waves 105–6 spatially integrated optical intensity 263 spatters 279 spectrometer 70 spectroscopic techniques analysis 263 laser welding and 266–73 optical sensors 283 spectrum optical 47–9 spectral cameras 62 specular reflection 49–50, 69, 234 weld pool surface 230–1, 232 speed, welding 226, 235, 265 step experiment 165 spot welding 117, 118, 188, 281 see also resistance spot welding (RSW) squeeze time 252 stand-alone process monitoring systems 238

WPNL2204

297

298

Index

statistical process control (SPC) 247–9 steady-state 5, 28, 203 burnoff rate equation 19 models 21–2, 26 quasi- 224 welding conditions 192, 265 Stefan-Boltzmann law 78 stereovision system 68 Stocker Yale 64 storage array 55 strain gauge load cells 249 stress wave emissions (SWE) 113–14 stroboscopic video cameras 63–4, 70 structure-borne emissions 274–5 structured light detection 64–7, 70, 159, 229–31 application 178–80 experiment system 179 mathematical model of 174, 176–8 principle of 173–4, 175–6 weld profiles 173–80 submerged arc welding (SAW) 18, 25, 82, 88 sulfur 88, 90–1 surface pool see pool surface, weld shaping defects 165, 170–2 waves see Rayleigh waves temperature gradients 91–2, 262 plasma 69–70, 201, 267–70 pool surface 223, 225 profiles 87 ultrasonic sensors and 119 temperature distribution 75–6, 191–3, 269 infrared (IR) sensors and 80–2 joint gap 92–4 resistance spot welding (RSW) and 241–3 see also thermal distribution T.E.R.N.I. Research 283 thermal distribution 89–90, 92 infrared (IR) sensors and 95–7 see also temperature distribution

thermal factors conductivity 93 detectors 76 energy 83–4 infrared (IR) cameras 62 radiation 86–8 thermistors 81 thermocouples 81 thermographic infrared camera 82 thermopiles 76, 81 thin lens principle 51–2 through-arc electrical arc signal variations 17 through-arc monitoring, GMAW 132–50 electromagnetic oscillation sensor 144–9 fillet welding, end points 138–44 light intensity and resistance sensor 149–50 rotating sensor 135–8, 138–44 torch weaving sensor 132–5 time-of-flight (TOF) technique 105, 115, 121, 194, 252 tip-to-workpiece distance 132–5, 136 titanium dioxide coating 90 T-joints 168–9, 175 torch calibrated position of 94 oscillation cycle 36–7 rotating arc 134–8, 139–40 weaving sensor 132–5 transformers 239 transverse waves 105–6, 114, 117 see also shear waves travel speed 215–16, 220 traveling weld pool 190–1 triangle measurement, principle of laser 173 tri-joints 168 TRWOC system (T.E.R.N.I. Research) 283 T-typed weld 175 tungsten inert gas (TIG) 118

WPNL2204

Index

WPNL2204

ultrasonic factors emission 274 inspection 118, 250 measurements 252 transducers 250 ultrasonic sensors 104–24, 246 acoustic emission (AE) and 104–5, 110–14 further information 124 future trends 122–3 penetration depth and 193–5 principles of 105–10 temperature and 119 testing (UT) 105, 114–22 ultraviolet (UV) factors cameras 62 light emissions 277 photodetectors 276 photodiodes 279 plasma 264 waves 48 ultraviolet UV-VIS factors emissions 263, 265 photodiode 265 radiation 278 range 267 undercut defect 171, 176 University of Kentucky 64, 232 vapour ejection 274 variations (deviations) 3–4, 6 of arc length 132 experiments 165 of metal surface condition 249 of process measurements 247–8

299

spectral features 270 in temperature signals 269 in weld characteristics 80 V-groove joints 95, 121 butt 175 GMAW 138, 145–6, 149 visible light (VIS) 48 photodetectors 276 photodiodes 279 see also ultraviolet UV-VIS factors vision-based sensors penetration depth and 195–6 weld pool surface and 213–17 see also laser visual monitoring, GMAW; optical sensors waveform recorder 188 wave-front scrubbing 120 wave-particle duality 47 welding process as complex system 4–6 monitoring of 7–10 as system 1–4 as uncertain system 6–7 weld bead see bead, weld weld nugget see nugget, weld weld pool see pool, weld weld pool surface see pool surface, weld weld profiles see profiles, weld Welding Research Laboratory, University of Kentucky 64 Weldwatcher system (4D) 283 Wien’s displacement law 75 X-rays 47–8, 70–2, 104, 123

WPNL2204

Real-Time Weld Process Monitoring

Realtime

Realtime 03 - Marooned In Realtime

pipe weld procedures

Bayesian Process Monitoring, Control and Optimization

Realtime-Streaming

Across Realtime

Across Realtime

Across Realtime

Vinge, Vernor - Realtime 3 - Marooned in Realtime

Realtime Interrupt

Across realtime

Realtime Interrupt

Vinge, Vernor - Realtime 1 - Across Realtime

Weld Cracking in Ferrous Alloys

Metallurgy of Basic Weld Metal

Marooned in Realtime

Marooned in Realtime

Realtime 02 - The Ungoverned

Vernor Vinge - Across Realtime

Marooned in Realtime

Marooned in Realtime

Marooned in Realtime

Marooned in Realtime

Marooned in Realtime

Vernor Vinge - Across Realtime 3 - Marooned in Realtime

Vernor Vinge - Across Realtime 3 - Marooned in Realtime

Vernor Vinge - Across Realtime 3 - Marooned in Realtime (2)

Vernor Vinge - Across Realtime 3 - Marooned in Realtime

Real-Time Weld Process Monitoring

Realtime

Realtime 03 - Marooned In Realtime

pipe weld procedures

Bayesian Process Monitoring, Control and Optimization

Realtime-Streaming

Across Realtime

Across Realtime

Across Realtime

Vinge, Vernor - Realtime 3 - Marooned in Realtime

Realtime Interrupt

Across realtime

Realtime Interrupt

Vinge, Vernor - Realtime 1 - Across Realtime

Weld Cracking in Ferrous Alloys

Metallurgy of Basic Weld Metal

Marooned in Realtime

Marooned in Realtime

Realtime 02 - The Ungoverned

Vernor Vinge - Across Realtime

Marooned in Realtime

Marooned in Realtime

Marooned in Realtime

Marooned in Realtime

Marooned in Realtime

Vernor Vinge - Across Realtime 3 - Marooned in Realtime

Vernor Vinge - Across Realtime 3 - Marooned in Realtime

Vernor Vinge - Across Realtime 3 - Marooned in Realtime (2)

Vernor Vinge - Across Realtime 3 - Marooned in Realtime

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