Communication Flexible production systems for the apparel and metal-working industries: a contrast study on technologies...
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Communication Flexible production systems for the apparel and metal-working industries: a contrast study on technologies and contributions F. Frank Chen
Apparel and metal working industries 11 Received May 1996 Revised July 1996 Accepted August 1997
Department of Mechanical, Industrial & Manufacturing Engineering, The University of Toledo, Toledo, Ohio, USA Introduction The original concept of flexible manufacturing systems (FMS) emerged in the mid- to late 1960s as a logical outgrowth of progress in applying numerical control within processes as well as company-wide operations. The flexible manufacturing concept represents a relatively new strategy to increase flexibility, productivity, and quality. The technology is especially attractive for manufacturers who produce in the middle range of production volumes, neither mass production nor one of a kind. Boosting productivity and responding quickly to an increasing fickle marketplace seem to be mutually exclusive goals. Yet these could overlap in the realm of an FMS – a marriage of transfer line volume and stand-alone machine flexibility. With FMS, setup time and its related costs are eliminated or drastically reduced; it becomes as economical to produce products in small lots as it is to produce in large lots. This situation has made economic order and production lot sizing concepts, as well as conventional methods of economic justification, obsolete (Saloman and Biegel, 1986). Strategic benefits — such as increased flexibility and reduced production lead times — may well be more important factors for successful competing in world markets than the financial savings themselves. A typical definition for FMS that has been frequently cited in studies performed for metal cutting/removal industries is: a computer-controlled configuration of semi-independent work stations and a material handling system designed to efficiently manufacture more than one part type at low to medium volumes (Draper, 1984).
Such a definition leads to three required physical components of an FMS: (1) standard numerically controlled machine tools; The author’s work on this paper has been funded through a grant awarded to the University of Southwestern Louisiana from the Louisiana Board of Regents via the Louisiana Education Quality Support Fund (LEQSF) – Enhancement Program 1993-1994.
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 11-20. © MCB University Press, 0955-6222
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(2) a conveyance network to move parts and perhaps tools between machines and fixturing stations; and (3) an overall control system that coordinates the machine tools, the partmoving elements, and the workpieces. Such a perceived set of system requirements has been closely followed by the pioneering FMS installations in the USA such as the early 1970s Sundstrand FMS at the Caterpillar Tractor manufacturing facility in East Peoria, Illinois. Interestingly, the AAMA Technical Advisory Committee has a very loosely defined interpretation of flexible manufacturing in 1988: Any departure from traditional mass production systems of apparel toward faster, smaller, more flexible production units that depend on the coordinated efforts of minimally supervised teams of workers (Hill, 1991).
Choosing such a generic definition for apparel manufacturers is certainly well justified owing to the substantial difference in the nature of operations performed in apparel versus metal cutting/removal industries. While the flexible manufacturing concept has already been well accepted in many hard-goods manufacturing industries, the soft-goods apparel industry seems to have struggled in recognizing the potential benefits and limits of the flexible manufacturing technology. The purpose of this paper is to perform a contrast study on the technologies and contributions of FMS in apparel and metal cutting/removal industries. Owing to the much earlier adoption of FMS in metal cutting/removal industries and a much more abundance of published studies on metal-working FMS, this paper is also intended to exploit lessons learned from existing metal-working FMS for use by the apparel professionals who are interested in employment of the flexible manufacturing technology. Two critical published papers are to serve as the prime source for this study. They are, Chen and Adam (1991) who summarized an extensive empirical study on 84 metal-working FMS project cases, and Hill (1991) who visited and performed a survey on the use of flexible work group (FWG) and unit production systems (UPS) in 12 US and five Japanese apparel manufacturing companies. Recommendations for further research are also provided. Comparison of FMS technologies and operations An FMS, as perceived by the metal-working professionals, requires technology intensive mechanism such as automated guided vehicles (AGV), robots, and coordinate measuring machines (CMM) as its core construct. Conversely, FMS in the apparel industry mainly refers to the implementation of FWG or UPS. FWG is basically a management concept involving a team of apparel associates, while UPS may well be considered an FWG with automated material handling mechanism, according to Hill (1991). Cellular manufacturing is the term widely used by traditional metal-working professionals to be the distinct philosophy from the high-volume, fixed automation (transfer-line settings). Similarly, modular manufacturing is used by the apparel manufacturing professionals as an alternative manufacturing concept to the traditional progressive bundle
system (PBS). Manufacturing cells for metal-working processes can be implemented with fully automated components (CNCs, AGVs, automated tool handling system, automated part loading/unloading system, etc.) or simply a group of semi-automated machining centers with no or minimum cell control and intra-cell material handling functions. Simple machining cells usually require several tending operators who are interchangeable among tasks to be performed within the cell. This is much like the flexible work groups (or modular manufacturing systems) in the apparel industry except there may be more operators in a modular work group than in a machining cell owing to the more labor-intensive nature of the apparel manufacturing process. Table I summarizes the basic difference in technology and operational characteristics of FMS in apparel as well as metal cutting/removal industries. It becomes clear that the migration into flexible manufacturing systems for the apparel manufacturers is much less capital intensive owing to the comparably lower level of hardware and software automation requirements as exhibited in FWG and UPS. It is the quality and lead-time (responsiveness) that drive the apparel manufacturers to migrate from the progressive bundle system (PBS) to FWG or UPS, not the automation itself. The results are that only very limited new hard technologies are needed to implement FWG and UPS, but philosophical changes in both the management and hourly workers seem enormous. With the intent of not trying to replace workers by automated machinery but trying to create a more productive work group and team spirit, the apparel manufacturers will certainly face much less resistance in adopting
Apparel (FWG and UPS)
Apparel and metal working industries 13
Metal-working (FMS or FMC)
Machine/equipment Mostly manual or semi-automatic
Materials handling
System control
Labor Management
Mostly standard numerically controlled machining centers, and are linked together via central computer control Manually move garment units Computer-controlled conveyance between stations (FWG), or use network to parts and tools between transporter to move a single unit machines and overhead fixturing between stations (UPS) stations Little or no computer control and Overall computer (cell) control coordination among stations, system that coordinates all production mostly relying on manual machines and equipment, and tracks coordination workpieces and tools throughout the entire manufacturing process Intensive – operators are needed Minimum – operators are usually to operate nearly all production needed only at load/unload and tool stations pre-setting stations Self-directed teamwork with Planned and controlled by a system employee empowerment and manager (foreman) with constraints continued problem solving given by the upstream MRP and the training (especially true for FWG) downstream assembly plans
Table I. FMS technology and operational contrast
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the FWG or UPS concepts. With the apparent more labor-intensive nature, FWG and UPS certainly call for more human coordinations than in metalworking FMS. Self-directed team work, employee empowerment, and employee problem-solving skill are keys to successful FWG and UPS. On the contrary, metal-working FMS are usually designed to minimize human interventions and possibly to operate an “unmanned” third shift operation for justification of the intensive capital requirements. Initial training for metal-working FMS operators may be burdensome owing to the need to operate sophisticated computer-controlled machinery and equipment, but continued training is deemed unnecessary. Comparison of FMS contributions Owing to the significant difference in technologies and operational philosophies found between the apparel FWG/UPS and the metal-working FMS, contributions resulting from flexible manufacturing implementations for hardgoods and soft-goods manufacturing are expected to show some difference. Table II provides a summary of the comparison by looking into various dimensions such as quality, direct labor, productivity, flexibility, work-inprocess inventory, etc. The comparison was mainly based on information provided by Hill (1991) who studied 30 apparel FWG/UPS and by Chen and Adam (1991) who studied 84 metal-working FMS cases. The purpose of this comparison is to identify any missing dimension(s) that should be examined when measuring the effectiveness of apparel FWG/UPS in future studies if deemed necessary. Quality improvement on apparel products which resulted from changing from the progressive bundle system (PBS) to FWG is very significant, as measured both by the number of defects and customer returns. Conversely, a very weak relationship has been found between metal-processing FMS and quality improvement of metal part processing judging from the fact that only four projects specifically reported improvement figures, though 22 FMS reported some quality improvement. The apparel FWG seemed to be a very effective tool for quality improvement as the human factor plays the key role for apparel manufacturing process. According to Hill (1991), the facts that the modular team became responsible for final product quality (rather than inspector/supervisor), the sense of ownership via making a complete product or at least complete components of the product, the peer pressure/support, and very low level of WIP have all explained the superior quality achieved by FWP. The team work synergism and the care for team performance evidently did make the difference. With the material handling mechanization, UPS did not contribute as much to quality improvement as FWG did. Maybe a more “humanized” UPS (i.e. a UPS with similar employee empowerment and team spirits that of FWG) will greatly improve the quality performance. Direct labor savings from implementing FWG is considered trivial (0.3 percent) compared to the 46 percent average direct labor reductions reported among metal-cutting FMS. It is not difficult to comprehend such a difference
Apparel (FWG and UPS) (Hill, 1991)
Metal working (FMS or FMC) (Chen and Adam, 1991)
FWG: defects reduced by 12-97% with an average of 65.3% UPS: improved by an average of 11.1%
Out of 84 projects, 22 mentioned improvement in quality; four installations specifically reported rework-defect reduction from 35-50% with an average of 46%
Direct labor
FWG: reduced by an average of 0.3% UPS: reduced by an average of 9.7%
Out of 84 projects, 24 reported reduced direct labor; 17 projects provided figures ranging from 0-94% with an average of 49%
Productivity
FWG: increased by 13.4% UPS: increased by 18.4%
Out of 84 projects, 11 reported throughput increase; seven projects provided figures showing 1.5-10 times of production throughput
Indirect\direct labor ratio
FWG: reduced by 10% UPS: reduced by 11.8%
Not reported
Throughput time
FWG: reduced by 71.1% UPS: reduced by 60.4%
Out of 84 projects, 41 mentioned significant throughput time reductions; 19 cases reported reductions ranging from 40-96% with an average of 71.6%
Flexibility
All reported easier product style changes and easier operator/ machine regrouping, additions, and replacements
Out of 50 Kearney and Trecker FMSs, 37 had flexibility as one of the key objectives: eight of the remaining FMSs reported increased flexibility in process, volume and design
Morale
Significant improvement evidenced by accelerated work pace, less time spent time in restrooms/breakrooms, and arrival at work earlier
Not reported
Turnover rate
FWG: reduced by 39.7% UPS: reduced by 29.5%
Not reported
Attendance/ absenteeism
FWG: improved by 2.6% UPS: improved by 1.1%
Not reported
Space
FWG: reduced by 36.9% UPS: reduced by 28.7% (square feet per operator)
Out of 84 projects, 15 reported to have utilization reduced floor space; two projects specifically provided reduction figures at 50% and 71%, respectively (production space per system)
WIP inventory
Not specifically reported Some drastic reductions were mentioned
Out of 84 projects, 33 experienced inventory reduction; nine projects indicated reduction numbers ranging from 50-97% with an average of 74.6%
Quality
(Continued)
Apparel and metal working industries 15
Table II. Comparison of flexible manufacturing contributions
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Apparel (FWG and UPS) (Hill, 1991)
Metal working (FMS or FMC) (Chen and Adam, 1991)
Setup time
Not reported
Out of 84 projects, 21 reported reductions in setup time with two projects giving specific percentages at 75 and 85%
Materials handling
Not reported
Out of 84 projects, 17 experienced reduced material handling requirements, but none provided a specific figure
Unit/system cost
Not reported
Out of 84 projects, 14 reported reductions in unit product cost or overall system cost; 11 projects gave figures ranging from 18-80% with an average of 37.7%
16
Table II.
when taking into consideration the level of automation in metal cutting FMS is far more advanced than that of the apparel’s modular work groups. In FWG, while bundle handling and piece work ticket functions are generally eliminated, this reduction is offset by the time which must be allowed for the movement of operators between workstations. Furthermore, the much less capital intensive apparel FWG does not need to use direct labor savings for justification. With the mechanized material handling functions, UPS reported to have achieved some visible direct labor savings (9.7 percent). Productivity increase resulting from FWG and UPS were considered moderate. On the other hand, with the massive automated machinery/equipment in metal-cutting FMS, throughput rates were reported to be 1.5-10 times higher. Yet, all such figures should be used with a great deal of caution. The true productivity measure should be based on a so-called total productivity formula (Adam et al., 1986): Outputs Productivity = Labor + Capital + Material + Energy Hill (1991) compared the number of apparel units produced by a group of individuals in the PBS versus the same number of individuals producing a like product in FWG. This method clearly indicated that a partial productivity (output relative to labor only) measure was made. Many of the metal-cutting FMS project cases collected and analyzed by Chen and Adam (1991) also showed that either partial productivity measures were used or the term productivity was used without specifying output and relative input. Indirect/direct labor ratios decreased by approximately 10 percent for FWG and 11.8 percent for UPS. There was no such report or discussion in metal cutting FMS projects. With the shifting of quality responsibility to modular
workers and empowerment for workers to make many decisions which used to be made by supervisors or corrected by service personnel, it should be easy to comprehend such a trend. On the contrary, with the need for maintaining sophisticated machinery/equipment and extensive system programming/ control/planning personnel, indirect labor required by metal cutting FMS is likely to be increased. This explained the reason why this factor was not mentioned or discussed in all project cases collected by Chen and Adam (1991). Throughput time reduction is certainly the main justification factor for the flexible manufacturing technology currently employed by soft-goods apparel as well as hard-goods metal cutting industries. To respond quickly to smaller but more frequent orders of numerous styles, some apparel manufacturers had successfully used FWG/UPS to cut the work-in-process time by 60-70 percent. Similarly, an average of approximately 70 percent reduction in throughput times was reported in metal-cutting FMS. Flexibility is another key dimension that should be very desirable for implementation of flexible manufacturing technology. With FWG, apparel manufacturers reported to have easier product style changes, easier operator/machine regrouping, additions, and replacement. Similarly, FMS in the metal-working industry have provided flexibilities for engineering design changes and product mix and volume changes, and the ability to weigh alternatives for in-house production or out-sourcing. However, the issue of flexibility has seemingly been poorly understood and managed. There is a great need to develop a common understanding of the term flexibility. Flexibility should be defined and measured by various attributes with respect to the functional capabilities of an FMS. In the Appendix, Chen and Adam (1991) present a modified and augmented discussion for the eight types of flexibility defined by Browne et al. (1984). Morale, turnover rate, and attendance/absenteeism are all key factors in the US apparel industry. However, they are rarely mentioned (with no statistics reported) in metal-working FMS cases. Significant improvement in morale of modular members was evidenced by an accelerated work pace, less time spent in restrooms/breakrooms, and arrival at work earlier. Employee turnover improved significantly in plants with properly functioning FWG and/or UPS. The modular group member’s sense of ownership, team spirit in a noncompetitive environment, and opportunity to learn and grow personally were the factors contributing to reduced turnover rate (Hill, 1991). For the same reasons mentioned above, attendance rates improved after successful implementation of FWG. Peer pressure and peer support were also cited as important factors in improving attendance. Space utilization as measured by square feet per operator has been considerably reduced by employing either FWG or UPS for apparel manufacturing. With the intent to eliminate labor content in manufacturing processes, the metal-cutting FMS also reported some significant reductions in shop floor space needed to install the systems, but not on the basis of square feet per operator. The space saving in FWG was mainly attributed to drastic
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reduction in WIP levels, according to Hill (1991). In addition, machines in FWG tend to be arranged closer together than machines in the PBS to facilitate some direct manual handling of work units between stations. Reductions in WIP inventory were mentioned in Hill (1991) after the FWG/UPS were implemented for apparel manufacturing, but no specific figures were reported. Those metal-working FMS reporting figures have achieved an average of nearly 75 percent reduction in WIP inventory – an amazing result which was also expected to have happened in apparel FWG or UPS. Since the amount of WIP inventory normally provides a good indication of quality performance in addition to the concern for the monetary investment of the in-process units themselves, WIP inventory in apparel manufacturing seems to be a critical dimension that deserves closer attention and precise measurement. The issues on setup time and material handling requirements were not mentioned or discussed in Hill (1991). Reduction in setup time is usually considered as one of the prime justification factors to adopt the flexible manufacturing technology for metal-cutting processes. Many FMS reported to have totally eliminated the setup process with the numerically controlled machinery/equipment and flexible fixturing/palletizing devices (Chen and Adam 1991). Similarly, a good portion of FMS projects experienced reduced material handling requirements though none provided specific numbers. Product unit cost or overall system cost were not discussed in Hill (1991), either. However, among those FMS cases studied by Chen and Adam (1991), 11 metal-working FMS projects produced a cost saving ranging from 18 percent to 80 percent, but only two indicated a payback within a two-year period. Summary and discussions It appears that there are clear distinctions in technological and operational characteristics between apparel FWG/UPS and metal-working FMS (FMC). Instead of seeking automation to replace labor as shown in sophisticated metalworking FMS, the more labor intensive apparel industry seems to have taken evolutionary steps on the journey to flexible manufacturing settings. An FWG, in essence, utilizes the fundamental cellular manufacturing concepts but at a less capital intensive manner. A UPS, with its mechanized material handling system, is a step closer to the metal working FMS. Considering the complex human assisted processes involved in various sewing, assembling, and packaging operations, unmanned apparel manufacturing cells (modules) will still be theoretically impossible in the foreseeable future. The much lower wages in the US apparel industry will also make it very difficult to justify any further significant automation efforts in modular apparel work groups. Since FWG have enabled a dramatic improvement in quality and a drastic reduction in throughput times of apparel products among other benefits summarized in Hill (1991), there is practically little need for further hard automation. On the other hand, soft automation in management systems integration and control is
necessary as the apparel industry is moving on the course of computer integrated manufacturing (CIM). Some leading US apparel manufacturers have taken advantage of the FWG concepts to strengthen their competitiveness worldwide. The Levi Strauss & Co. started to implement so-called “alternative manufacturing systems (AMS)” in 1991 in its 26 plants and four finishing centers in the USA (Ansel, 1991). The AMS employ the prime concepts of FWG team work, group problem solving, and cross-training in several job functions. With AMS, Levi Strauss has reaped most of the typical benefits found in FWG with a highlight of reducing the supply chain lead time from 49 days in 1989 to 28 days in 1992. The company expects to further cut it down to 13 days by 1994. Levi Strauss’ experience in its AMS has provided a solid and vivid example of using flexible manufacturing concepts to achieve competitiveness and to maintain leadership in the apparel manufacturing industry. Future research recommendations It is believed that research projects leading to address the following issues will further enhance the understanding of flexible manufacturing concepts, and thereby provoke a wider adoption of FWG/UPS by the smaller US apparel manufacturers : (1) Further surveys on FWG and UPS cases should be carried out to collect information on WIP inventory, setup time, material handling requirements, and the impact on bottom-line product costs in addition to those dimensions investigated in the study by Hill (1991). (2) A detailed planning and implementation procedure for migrating from the conventional PBS to FWG/UPS should be researched and documented. It will also serve as the complete guideline for apparel manufacturers to assess the appropriateness of FWG/UPS for their respective operations and market domains. (3) The needs of soft automation in management system integration and control along with implementation of FWG/UPS should be carefully enumerated in order to assist US apparel manufacturers in the transition into computer-integrated manufacturing enterprise. References Adam Jr, E., Hershauer, J. and Ruch, W. (1986), Productivity and Quality: Measurement as a Basis for Improvement, 2nd ed., Research Center, College of Business & Public Administration, University of Missouri-Columbia. Ansel, J. (1991), “Alternative manufacturing systems at Levi Strauss & Co.”, Proceedings of the 20th International Apparel Research Conference, Atlanta, GA, December. Browne, J., Dubois, D., Rathmill, K., Sethi, S. and Stecke, K. (1984), “Classification of flexible manufacturing systems”, The FMS Magazine, Vol. 2 No. 2, February, pp. 114-17. Chen, F. F. and Adam Jr, E.E. (1991), “The impact of flexible manufacturing systems on productivity and quality”, IEEE Transactions on Engineering Management, Vol. 38 No. 1, February, pp. 33-45.
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Table AI. Flexibility type and measurement
Draper, C. (1984), Flexible Manufacturing Systems Handbook, Automation and Management Systems Division, The Charles Stark Draper Laboratory, Inc., Noyes Publication. Hill, J.E. (1991), “Flexible manufacturing systems”, Proceedings of the 18th International Apparel Research Conference, November, Atlanta, GA. Saloman, D.P. and Biegel, J.E. (1986), “Assessing economic attractiveness of FMS applications in small batch manufacturing”, Industrial Engineering, Vol. 96 No. 1, January, pp. 88-96. Appendix. Type
Definition
Measurement
Machine flexibility
The ease of making changes required to produce a given set of part types
Time to replace worn-out or broken cutting tools, time to change tools in tool magazine to produce a different subset of part types, time to assemble or movement of new fixtures required
Process flexibility
The ability to produce a given set Number of part types that can of part types possibly using simultaneously be processed different materials in several ways without using batches
Product flexibility
The ability to change over to produce a new set of products very economically and quickly
Time required to switch from one part mix to another, not necessarily of the same part types
Routing flexibility
The ability to handle breakdowns and to continue producing the given set of part types
Robustness of the FMS when breakdowns occur – the production rate does not decrease dramatically and parts continue to be processed
Volume flexibility
The ability to operate an FMS profitability at different production volumes
The smallest volume can be for all part types with the FMS still being run profitably
Expansion flexibility The capability of building a system, and expanding it as needed, easily and modularly
The magnitude the FMS can become
Operation flexibility
The number of alternative operation orders for each part type that the FMS can accommodate
The ability to interchange the ordering of several operations for each part type
Production flexibility The universe of part types that the FMS can produce Source: Chen and Adam, 1991
The level of existing technology
Handling the assembly line balancing problem in the clothing industry using a genetic algorithm Keith C.C. Chan Department of Computing, The Hong Kong Polytechnic University, Hong Kong and
Assembly line balancing in the clothing industry 21 Received June 1997 Revised July 1997 Accepted December 1997
Patrick C.L. Hui, K.W. Yeung and Frency S.F. Ng Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 1. Introduction In clothing production under the progressive bundle system, garment components are assembled through a sub-assembly process until they are gathered into a finished garment. The production process involves a set of workstations in each of which a specific task in a restricted sequence is carried out. In order to avoid the completion time of the task at a workstation exceeding the predetermined cycle time, it is important that tasks be allocated to each workstation as evenly as possible. Since the assembly line consists of different sections involving different operations being performed at different production rates, balance control is necessary to make sure that the right person be assigned the right task. Balance control depends mainly on the supervisor’s interpretation and prediction of the line performance; the skill and experience of supervisors are therefore important for it to be successful. Unfortunately, since the skill and experience of supervisors are difficult to capture, as they vary from one to the other, it is not easy for a model of the supervisory behaviour in line balancing to be formulated. Over the years, several algorithms have been developed to solve this assembly line balancing problem, but they do not seem to have provided a satisfactory solution (Bowman, 1960; Johnson, 1983; Kao and Queyranne, 1982). Owing to competitive market forces, a trend of decreasing contract size, increasing product complexities, and the demand for quick response, existing line balancing techniques have to be improved. In response to this need, this paper introduces a new approach to dealing with line balancing by using genetic algorithms. This technique is able to improve line efficiency as well as minimise the time spent in balance control planning.
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 21-37. © MCB University Press, 0955-6222
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2. Classic definition of an assembly line balancing problem The classic definition of an assembly line balancing problem (Hoffman, 1990) involves: • a set of n tasks each of which takes ti time to complete; • a set of precedence relationships between the tasks; and • the cycle time C. The problem is to assign the tasks in such a manner so as to minimise the number of work stations, N, on the line without violating the precedence constraints or without having the sum of the task times at any work station exceed the cycle time. The difference between the cycle time and the sum of the task times at any one work station is referred to as the “slack time” or sj. The “total slack time”, S, is the sum of sjs over all work stations. S can also be calculated by multiplying the cycle time by the number of work stations and subtracting the total of the task times, T. The “theoretical” minimum total slack, S*, is simply N*C–T. Tackling line balancing problems, therefore, requires that the issues of precedence, task time and cycle time be dealt with. Based on these three aspects, there are three different approaches to handle the line balancing problem. The first approach was to find the minimum possible cycle time with a given precedence and task time set. The second approach was to search for the minimum number of work stations for a given cycle time and with a restricted precedence. The third approach was to vary the task time by holding the precedence relationships constant and with the cycle time being fixed (Hoffman, 1990) An assembly line is said to be perfectly balanced if the total slack (i.e. the sum of the idle time of all the stations along the line) is zero (Baybars, 1986). In real situations, it is very difficult to achieve perfect balance because the production rate of each work station is not equal. Slack time may occur as a result of line perturbations caused by operator absenteeism, machine breakdowns and repair, variations on material handling, and also operators’ varying performance. 3. The assembly line balancing problem in the clothing industry Conceptually, if each unit being manufactured is processed in a definite order, and no two stations operate on the same unit simultaneously, thus an assembly line can be run smoothly without any balancing problem. However, in practice, the assembly line of garment manufacturing is arranged in a hybrid approach, i.e. the combination of serial and parallel order sequences for operation. Line balancing problems will occur when parallel sub-assembly lines exist that can be integrated into the main line. In order to ensure the flow of work through each station be as smooth as possible, such combination has to be carefully considered by the supervisor. The supervisor will play a role of allocating the resources such as workers, machinery, etc. among the main line and sub-
assembly lines in order to maintain the precedence relationships, and to ensure Assembly line the sum of the task times at any work station does not exceed the cycle time. balancing in the In garment manufacturing, since the layout of a factory and the number of clothing industry work stations are fixed, it is difficult for a supervisor to balance an assembly line by minimising the number of work stations along the assembly line. Therefore, instead of assigning more than one task to a work station, the supervisor 23 generally will attempt to balance the assembly line by minimising the total slack time (i.e. the sum of the idle times of all the stations along the line). The slack time can be minimised by upgrading the skill of operators, or reducing process delay by strengthening the material handling method, such as unit production system. In practice, the supervisor prefers to allocate different skill level of workers to each work station in order to minimise the total slack time for a production. As standard allowable minutes (SAM) is a standard measuring unit to determine the completion time of a particular task, i.e. the sum of task time and slack time, thus, minimising the slack time results in reducing the SAM. Several models of line balancing have been proposed. For example, the procedure for implementing line balancing in a progressive bundle system was studied by Whitaker (1973), but validation of Whitaker’s model is relatively unclear and insufficient. A discrete event simulation model for trousers manufacturing was studied by Rosser et al. (1991), but it is concerned primarily with material flows and the problems resulting from the absence of supervision. The simulation of the bundle system described by Oliver et al. (1994) fail to make reference to supervisory issues. Fozzard et al. (1996) adopted the visual interactive simulation for modelling balancing control, but the approach is not universal as it is limited only to some types of experiment. They concluded that an exploration of a knowledge-based approach to clothing production supervision would be a feasible solution to solve the problem encountered in their study. 4. A genetic algorithm-based solution approach Genetic algorithms (GA) are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. It was first proposed by Holland (1975) and has been used in a diverse number of optimisation applications. GA employs a random directed search for locating the globally optimal solution. They are superior to many “gradient descent” techniques as they possess the ability to locate the globally optimal solution for a multimodal objective function. Thus, GA is suited for applications in nonlinear function optimisation and the nonlinear programming problem (Man et al., 1996; Srinivas and Patnaik, 1996). GA works with a population of individuals representing potential solutions to a problem. Each individual is usually represented by a single string of characters. At every iteration of the algorithm, a fitness value, f(i), is calculated for each of the current individuals. Based on this fitness function, a number of individuals are selected as potential parents. Two new individuals can be obtained from two parents by choosing a random point along the string,
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splitting both strings at that point and then joining the front part of one parent to the back part of the other parent and vice versa. This process is usually called crossover, at an operation rate with a typical value of between 0.6-1.0 (Man et al., 1996). Individuals may also change through random mutation when elements within a string are changed directly at a smaller probability with a typical value of less than 0.1 (Man et al., 1996). The processes of crossover and mutation are collectively referred to as reproduction. The end result is a new population (or the next generation) and the whole process repeats. Over time, this algorithm leads to convergence within a population with fewer and fewer variations between individuals. When a GA works well, the population converges to a good solution of the underlying optimisation problem and the best individual in the population after many generations is likely to be close to the global optimum. In summary, a GA works as follows: • Create the initial generation. • Evaluate the fitness of each individual in the initial generation. • Perform the following steps until the termination condition is true: – create new individuals by mating individuals in the current generation using the genetic operators of select, crossover and mutation; – evaluate the fitness of each newly created individual; – create a new generation by inserting new and deleting old individuals in the current generation. • Return the best individual(s). We discuss a GA for the ALB problem in terms of the five components: representation, initialisation, evaluation, operators and parameters. 5. An order-based genetic algorithm solution to assembly line balancing problems 5.1 Chromosome representations Solutions in genetic algorithms are represented by strings of different types (genomes). The choice of solution representation (structure) in GA is related to the nature of the problem. It is encoded to form a chromosome. In our case, each operation is associated with a fixed position on the string and the code is simply the worker number with arbitrary skill level to which that operation is assigned. A chromosome representation for the ALB problem is shown in Figure 1. In this chromosome representation, we have put the tasks into an order depending on the precedence graph.
Figure 1. Example of chromosome representation
W1 W2
W3
–
–
–
–
–
Wi
Wi = worker i
T1
T3
–
–
–
–
–
Tj
Tj = task j
T2
5.2 Initialisation Assembly line A starting population is initialised by randomly generated bit strings. The balancing in the format of a bit string is the same as the representation of a chromosome clothing industry mentioned in section 5.1. 5.3 Population size The choice of population size can have an impact on the performance of a GA. If a large population size is chosen, the evolution process may be too slow. But if the size is too small, the population may not contain enough alleles for the best chromosomes to evolve. For our GA, we choose a population size of 50.
25
5.4 The fitness function In a GA, the fitness function provides a way of evaluating the status of each chromosome. It is used to help determine which individual survives into the next generation. Since the objective of ALB in the clothing industry is to minimise the total slack time along the assembly line, the fitness function is defined in terms of it. To describe the fitness function we used, an illustrating example for ALB in clothing industry is presented in Figure 2. In this example, let us introduce the following notation: s = SAM, standard allowable minutes to complete a particular task. = skill level of the worker. Sw Ti = time taken to complete task i, where i = 1, 2, …, n = s/Sw, if Sw > 0 = 0 otherwise. TB1 = time taken to finish all tasks of branch 1 3
( ∑ Ti ) i=1
TB2
= time taken to finish all tasks of branch 2 7
( ∑ Ti ) i= 4
B1
T1
T4
T2
T5
T3
T6
T7
B2
Figure 2. An illustrative example for ALB in the clothing industry
IJCST 10,1
26
TB1+B2 = the longest time to complete B1 and B2. To find the best solution, a GA is used to minimize the total slack time among all solutions. In other words, we are to find the total slack time = min[i = 1 ∑7 (Ti – TB1+B2)]. To make a fast convergence towards the local optimum, the previous research works (Anderson and Ferris, 1990; Goldberg, 1989; Zbigniew, 1996) proposed that a linear scaling of the fitness values is implemented. The scaling is performed in such a way that the average fitness remains constant but the maximum fitness is a multiple (usually 1.5) of this average value (Anderson and Ferris, 1990). In other words, the actual chromosome’s fitness is scaled as f(i)′ = a*f(i) + b, where a and b are chosen to enforce the scaled average fitness values (Man et al., 1996). 5.5 Selection of parents The selection technique used by our GA is the roulette wheel selection technique. The reason for such a name is that it can be viewed as allocating pieshaped slices on a roulette wheel to population members, with each slice proportional to the member’s fitness. Selection of a population member to be a parent can be viewed as a spin of the wheel, with the winning population member being the one in whose slice the roulette spinner ends up. Although this selection procedure is random, each parent’s chance of being selected is directly proportional to its fitness. Over a number of generations this algorithm will drive out the least fit members and contribute to the spread of the genetic material in the fittest population members. Even though it is possible for the worst population member to be selected by this algorithm each time it is used, the odds of this happening are negligible (Zbigneiw, 1996). 5.6 Operators: uniform order-based crossover and scramble sublist mutation In our GA, reproduction involves two parents. After two chromosomes are selected from the current population, our GA applied a “uniform order-based crossover” operator by recombining the generic materials in the two parent chromosomes to create two children. A crossover rate used in our case is 0.65. The working principle and an illustrative example of the uniform order-based crossover is presented in Figure 3. Working principle: • Generate a bit string that is the same length as the parent.
Figure 3. Working principle and illustrative example of uniform order-based crossover
An illustrative example: Parent 1 1 2 Parent 2 8 6
3 4
4 2
5 7
6 5
7 3
8 1
Ordered List 0
1
1
0
1
1
0
0
Child 1 Child 2
2 4
3 5
4 2
5 6
6 7
7 3
1 1
8 8
Fill in some positions on Child 1 by copying them from Parent 1 Assembly line wherever the bit string of an ordered list contains a 1. balancing in the • Make a list of elements from Parent 1 associated with a 0 in the bit string clothing industry of an ordered list. • Permute these elements so that they appear in the same order on Parent 2. 27 • Fill these permutated elements in the gaps on Child 1 in the order generated in statement 4. • To make Child 2, carry out a similar process. To ensure that a GA is able to escape from a local optimum, a scramble sublist mutation operator (Anderson and Ferris, 1990; Zbigniew, 1996) is used. This operator selects a sublist of the items on a parent order-based chromosome and permutes them in the child, leaving the rest of the chromosomes as they were in the parent. An illustrative example of a scramble sublist mutation is presented in Figure 4. The rate of mutation used in our ALB problem is 0.008. •
Parent = ( 2 4 7 1 4 8 3 5 9 ) Child = ( 2 4 4 8 1 7 3 5 9 ) Remarks: with the beginning and the end of the selected sub-list marked by .
5.7 Delete members of the population to make room for the new chromosome The deletion technique used in our GA is to delete all members of the old population when reproduction has occurred. For each iteration, each old population is replaced by a new one. Thus, this reproduction is by generational replacement. 6. Experimental results and discussions In our experiment, the 41-tasks precedence relationship of men’s shirt manufacturing with simulated processing times is used as shown in Figure 5 and Table I respectively. The name of each task is tabulated in Table II. To justify the results of our experiment, two test cases were used. Test case 1 Forty-one workers with arbitrary skill level ranging from 0 (no-skilled) to 1.5 (fully-skilled) for each task are generated randomly. Thus, these workers with arbitrary skill level for each task are listed in Tables III and IV. Using our GA, set out in the previous section, the results of six trial runs are shown in Table V. Each trial run was carried out for about 5,000 seconds. Therefore, the best solution that has been obtained in the six trials on our numerical experiments is: • Workers’ assignment from task 1-41 is 6, 30, 39, 31, 2, 40, 32, 35, 17, 28, 22, 20, 27, 5, 26, 9, 1, 19, 10, 33, 37, 34, 29, 12, 24, 7, 15, 23, 38, 21, 16, 8, 25, 14, 13, 41, 11, 18, 4, 3, 36 respectively.
Figure 4. Example of scramble sublist mutation
IJCST 10,1
1
28
34
2
13
25
35
3
14
26
36
4
15
21
27
37
5
16
18
22
28
38
17
19
23
29
39
6
7
8
20
9
24
10
30
11
31
12
32 33
Figure 5. The 41-task precedence relationship of men’s shirt manufacturing
40
41 Task No.
Table I. Processing time for 41 tasks in SAM per 100 pieces
1 2 3 4 5 6 7 8 9 10 11
SAM
Task No.
SAM
Task No.
SAM
Task No.
SAM
41 30 25 48 30 65 28 46 57 56 32
12 13 14 15 16 17 18 19 20 21 22
41 37 19 28 30 29 47 66 54 33 20
23 24 25 26 27 28 29 30 31 32 33
34 55 47 67 44 36 65 58 64 23 32
34 35 36 37 38 39 40 41
28 57 49 50 55 32 43 48
Task No.
Task name
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Spot fuse collar fall Top fuse collar fall Sew collar stay pocket Runstitch collar fall Trim, turn and press collar fall Topstitch collar fall Hem collar band Attach collar band Turn and press collar band Topstitch collar band Sew collar band buttonhole Sew collar band button Set centre front placket Hem right front edge Trim neckline Sew centre front buttonhole Sew right front button Hem pocket mouth Crease pocket Set pocket Sew yoke pleats
•
Task No. 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Task name Set yoke label Set yoke Join shoulder Set sleeve under placket Set sleeve top placket Finish sleeve placket Sew sleeve placket buttonhole Sew sleeve placket button Set sleeve Topstitch armhole Join side seam Hem bottom Hem cuff Runstitch cuff Turn and press cuff Topstitch cuff Sew cuff buttonhole Sew cuff button Set cuff Set and close collar
Final solution is 474.567 minutes required to produce 100 pieces. Thus, the running time required for making up 1,000 pieces is 2.2 working days assuming that there are eight hours in one working day (i.e. 480 minutes).
Test case 2 The same set of workers is also used in this case but the worker’s assignment on each task is made by a greedy algorithm. A greedy algorithm performs optimisation by proceeding through a series of alternatives by making the best decision at each point in the series. In this case, this algorithm is to assign the most skillful worker on each individual task. Using a greedy algorithm, the best solution of worker’s assignment from task 1-41 is 6, 16, 37, 38, 10, 9, 31, 3, 26, 29, 34, 21, 30, 20, 41, 2, 32, 19, 7, 23, 15, 22, 35, 28, 33, 36, 11, 5, 8, 39, 14, 17, 1, 25, 24, 13, 27, 12, 18, 4, 40 respectively. By simulation, the running time required for making up 100 pieces is 2,877 minutes. To complete 1,000 pieces is many times slower than the GAs solution. Thus, the solution of Case 2 is worse than Case 1. As a result, it was proved in our experiment that using a greedy algorithm as currently adopted by a line supervisor cannot effectively reach a more optimal solution than using GA within a reasonable time limit. It demonstrated that a GA is a more powerful than the current practice used in line balancing in apparel manufacturing.
Assembly line balancing in the clothing industry 29
Table II. The 41 tasks required in men’s shirt manufacturing
3
4
5
6
7
8
9
10
11
Worker number 12
13
14
15
16
17
18
19
20
0.712 1.022 0.266 1.150 0.465 1.470 0.283 0.216 1.172 0.919 1.056 0.983 1.156 0.868 1.041 0.422 0.823 0.570 0.037 0.639
0.106 0.889 0.865 1.111 1.356 0.232 0.018 0.006 0.025 1.201 1.171 0.269 0.287 0.557 0.610 0.966 0.905 0.444 1.004 1.055
1.261 1.433 0.747 0.828 0.803 0.059 0.337 0.794 0.549 0.696 0.474 1.441 1.342 0.564 0.733 1.321 1.269 0.412 1.002 1.474
0.090 0.966 0.428 1.085 0.491 0.277 1.349 1.143 0.480 0.253 0.053 0.053 1.217 1.183 0.782 0.380 0.640 0.103 1.269 0.644
0.440 1.498 0.234 0.617 0.664 0.015 0.484 0.856 0.112 0.952 0.907 0.486 0.962 1.372 0.150 0.936 0.032 0.447 0.541 0.052
1.376 0.366 0.915 0.813 1.351 0.568 1.294 0.346 1.176 0.112 0.438 1.342 0.731 1.022 1.407 0.068 1.417 1.263 1.242 1.110
0.552 1.014 1.110 0.894 0.521 0.592 1.145 1.294 0.390 1.443 0.088 0.701 1.172 0.860 0.487 0.711 0.057 0.363 1.207 0.164
1.162 0.444 1.120 1.321 0.531 0.546 1.450 0.093 1.283 1.181 0.699 0.771 1.121 0.791 1.426 1.234 1.099 0.562 1.379 0.892
0.492 0.129 0.121 1.003 0.732 0.123 0.411 0.728 1.377 1.305 0.329 1.316 0.287 1.394 0.411 1.301 1.360 0.877 0.120 0.921
13
14
15
16
17
18
19
20
(Continued)
0.123 0.309 0.402 0.445 0.025 0.658 1.446 0.628 1.099 0.048 0.753 1.087 0.872 0.754 0.178 0.869 1.342 0.155 1.258 1.206
0.243 1.156 1.441 1.354 1.046 0.349 0.443 0.237 1.074 0.656 0.326 0.462 0.996 0.293 0.955 0.627 0.818 0.232 0.137 1.069
8
12
0.478 0.976 0.816 1.038 0.832 0.223 0.401 0.048 1.154 0.298 1.199 0.924 0.121 0.336 0.094 0.886 0.837 0.509 0.527 0.135
7
11
1.007 1.162 0.410 0.985 1.300 1.121 1.163 1.027 1.499 0.359 0.897 0.806 1.034 0.806 0.156 1.232 1.072 0.197 0.870 0.617
6
0.639 0.836 0.236 0.718 1.003 1.071 0.685 0.619 1.492 1.139 0.606 0.612 0.928 1.455 1.206 1.263 1.099 0.747 0.874 0.324
0.409 0.014 0.242 0.472 0.153 0.146 0.280 1.327 0.990 1.494 0.143 0.684 1.350 0.771 0.281 1.341 0.661 1.488 0.350 1.042
5
0.558 1.062 0.262 0.478 0.151 1.320 1.054 0.233 1.341 1.034 0.712 0.360 0.022 0.623 0.409 0.448 0.773 0.627 1.284 1.454
0.304 0.889 1.316 0.472 0.082 0.811 1.353 0.384 0.767 0.728 1.309 1.219 0.058 0.793 0.008 0.686 1.386 0.206 0.639 0.454
4
9
1.292 0.033 0.420 1.256 0.459 1.037 1.128 0.033 1.384 0.748 1.200 1.137 0.372 0.121 1.452 0.826 0.243 0.242 1.001 0.862
10
0.047 0.751 0.943 0.549 1.480 0.945 1.256 1.344 1.203 1.428 0.223 0.954 0.516 0.582 0.124 1.496 1.054 0.419 1.002 0.655
3
0.215 0.140 0.382 0.190 1.494 1.047 0.849 0.890 0.954 0.659 0.645 0.414 0.354 0.619 0.271 1.147 1.401 0.064 0.937
2
2
–
1
Table III. Table of workers (worker Nos. 1-20) with arbitrary skill level (0-1.5) for each task
1
30
Task No.
IJCST 10,1
41
40
39
38
37
36
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
Task No.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
0.212 0.432 0.856 0.547 1.036 0.308 0.983 0.048 1.215 0.703 0.976 1.121 1.384 0.549 0.096 0.859 1.109 0.684 0.525 1.368
0.030 1.034 0.341 1.420 0.277 0.686 1.184 0.746 0.969 0.841 0.553 0.265 0.416 0.273 0.698 0.504 0.760 0.715 0.149 0.709
1.241 1.297 1.009 0.345 0.340 1.170 0.495 1.470 0.766 1.225 0.632 0.233 0.601 1.298 0.534 0.906 0.704 1.255 0.290 0.388
1.332 0.884 0.291 0.237 0.672 0.933 1.058 1.145 0.534 0.026 0.376 1.285 0.143 0.413 0.725 0.830 1.182 0.395 0.204 0.235
0.739 1.176 0.090 0.996 0.489 0.546 0.115 0.320 0.925 0.285 0.657 1.233 0.537 0.983 0.627 0.377 0.911 0.933 1.047 0.563
1.465 1.370 0.101 0.750 0.811 0.541 0.439 0.173 0.036 1.238 1.291 0.994 1.483 0.591 0.487 0.768 0.034 1.211 0.055 1.307
1.159 0.215 0.140 1.102 1.392 1.318 0.227 1.432 1.485 0.992 1.327 0.496 1.107 1.231 0.263 0.736 0.039 0.793 0.670 0.108
0.431 0.297 0.212 1.051 0.935 0.837 0.391 1.205 0.152 0.231 0.522 0.216 1.019 1.298 0.724 0.344 0.580 0.360 0.135 0.265
1.312 1.124 1.226 0.416 0.165 1.045 0.822 0.152 1.453 0.319 0.055 0.698 1.371 1.057 1.065 0.624 1.188 0.144 0.887 1.328
0.224 1.161 1.228 1.391 0.881 0.386 0.593 0.088 0.765 1.472 0.378 0.203 0.860 0.577 0.879 0.037 1.479 0.006 0.931 0.488
0.723 1.483 0.512 1.022 1.211 0.912 0.469 0.070 0.944 1.082 1.213 0.725 0.634 1.497 0.542 0.912 0.880 1.088 1.393 0.017
0.419 1.058 0.741 1.258 0.311 1.312 1.064 0.241 0.512 1.289 0.126 1.225 0.530 0.092 1.460 1.212 1.417 0.664 0.211 0.567
1.239 0.011 1.111 1.383 0.605 0.874 0.225 1.488 1.044 1.305 0.615 0.921 0.517 0.649 0.648 0.529 0.305 0.773 0.709 0.109
0.370 0.508 0.655 0.121 1.339 0.559 0.001 0.059 1.120 1.397 1.085 0.470 0.163 0.143 0.639 0.718 1.323 1.069 1.337 0.392
0.699 1.027 0.918 1.010 0.191 0.380 0.634 0.628 0.633 0.711 1.375 0.230 0.890 1.240 0.502 0.354 0.539 0.045 0.255 0.408
0.494 1.430 0.572 0.823 0.740 0.850 0.762 0.782 0.567 0.088 0.707 0.706 0.448 0.310 1.089 1.359 0.144 0.623 0.582 1.307
0.244 0.860 1.209 0.081 1.384 1.226 0.079 1.206 1.034 1.324 0.458 1.463 1.092 1.456 0.429 0.402 0.085 1.146 0.675 0.938
0.460 0.765 1.193 0.172 0.271 1.013 0.370 0.309 0.732 0.866 0.514 0.217 1.312 0.743 0.756 1.051 1.173 0.114 1.167 0.479
1.077 1.323 0.160 0.605 1.140 0.691 0.031 0.996 1.386 0.333 1.284 0.874 0.790 1.469 0.631 1.003 1.401 0.591 1.120 1.159
1.266 0.743 0.090 0.708 1.276 0.757 1.254 0.551 1.341 0.009 0.983 0.622 1.359 0.034 1.283 1.467 0.966 0.396 0.773 0.914
1.047 0.158 0.114 0.169 0.107 0.681 0.594 0.113 0.073 0.645 0.126 1.010 0.567 0.343 1.190 0.332 0.389 0.085 0.397 1.344
1
Worker number
Assembly line balancing in the clothing industry 31
Table III.
Table IV. Table of workers (worker Nos. 21-41) with arbitrary skill level (0-1.5) for each task 37
38
39
40
41
0.253 0.852 0.905 0.826 1.213 0.547 1.132 0.502 1.264 0.966 0.054 0.288 0.364 0.571 0.945 1.049 0.080 1.479 0.292 0.033 0.554
1.115 0.015 0.763 0.502 0.623 1.390 0.245 0.969 0.230 0.524 0.594 1.190 1.427 1.465 0.990 0.268 0.693 1.480 0.994 0.610 0.453
1.145 1.320 0.468 1.014 0.010 0.416 0.666 0.519 1.040 0.552 0.194 0.185 0.359 0.767 0.115 0.510 1.158 1.471 0.999 1.034 1.284
1.038 0.873 0.489 0.836 1.397 0.557 0.242 0.349 0.250 1.397 0.227 1.451 0.657 0.279 1.208 1.181 0.706 0.215 1.455 0.382 0.365
0.244 0.593 1.170 0.551 0.813 1.403 0.266 0.217 1.203 0.664 1.011 0.455 0.156 1.355 0.628 0.787 0.104 0.483 0.158 1.291 0.481
0.535 1.394 0.125 0.488 0.701 0.813 1.409 0.209 0.882 0.664 1.290 0.825 0.153 0.229 0.532 0.700 0.989 0.421 1.456 0.159 1.457
0.443 1.371 1.027 0.594 0.697 0.404 0.099 1.064 0.134 1.146 0.587 1.168 0.125 1.091 1.153 0.624 0.226 1.298 0.107 0.381 0.902
1.386 0.866 1.336 0.145 0.816 0.634 0.173 1.342 0.611 1.096 0.015 1.478 0.772 0.750 0.965 0.749 0.586 0.505 1.250 0.134 0.132
0.623 0.975 0.069 0.436 1.241 0.413 1.338 0.835 0.085 0.519 1.425 0.081 0.035 0.624 0.007 0.787 0.151 0.909 0.192 0.669 1.231
0.328 0.583 0.586 1.190 0.149 1.084 1.221 0.084 1.346 0.870 0.488 0.517 0.830 0.415 1.160 0.812 0.629 0.979 0.284 1.286 0.373
0.363 0.186 1.365 0.804 0.078 1.319 1.338 0.493 1.128 1.397 1.469 0.981 0.696 0.555 1.164 0.861 0.253 0.418 0.850 0.005 1.258
11
12
13
14
15
16
17
18
19
20
(Continued)
1.256 1.337 0.430 1.039 1.126 1.426 1.362 1.072 0.108 1.002 0.789 0.651 1.230 1.375 0.879 0.861 1.106 0.183 0.590 1.390 0.521
9
10
1.279 0.621 0.781 1.079 1.454 0.301 1.464 0.037 0.786 0.818 0.557 1.072 0.653 0.160 0.939 1.147 0.943 0.885 1.240 1.433 0.989
36
0.857 1.330 0.224 1.466 0.649 1.173 1.101 0.163 0.947 0.998 1.480 1.097 0.306 0.111 0.134 1.315 0.060 0.216 0.020 1.283 0.258
35
8
34
7
33
0.588 1.177 1.216 0.991 0.333 0.546 1.223 0.178 0.968 0.351 0.331 1.007 0.044 0.381 0.018 0.422 0.365 1.019 0.967 1.129 0.641
32
0.457 0.649 1.439 0.051 1.062 1.462 1.128 0.491 0.118 0.135 1.344 0.404 1.067 0.134 1.333 0.892 1.124 1.230 0.934 0.062 0.594
31
6
30
Worker number
0.950 0.647 1.477 1.013 1.174 1.114 0.756 0.585 1.433 0.171 0.516 0.777 1.269 1.000 0.560 1.057 0.698 1.498 1.082 0.486 0.601
29
5
28
4
27
0.182 0.627 0.295 0.155 0.770 1.212 0.392 0.715 0.276 0.121 0.215 1.447 0.644 0.690 1.440 0.220 1.497 1.115 1.017 0.979 1.361
26
0.228 0.182 0.391 0.600 0.199 0.592 0.989 1.471 1.304 0.039 1.129 1.431 0.724 0.438 0.275 0.173 1.053 0.293 1.143 0.138 0.810
25
3
24
2
23
0.631 1.037 0.017 0.357 0.653 1.156 0.359 0.773 1.221 1.416 1.470 0.278 0.696 1.173 0.475 0.441 1.194 0.967 0.907 0.564 0.788
22
1
21
32
Task No.
IJCST 10,1
41
40
39
38
37
36
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
Task No.
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
1.341 0.399 1.066 0.801 0.456 0.305 1.395 0.874 1.006 0.426 1.169 1.207 1.201 0.817 1.192 0.729 1.003 0.368 0.712 1.234 0.577
0.389 1.469 0.423 0.894 0.363 0.668 0.445 0.890 0.081 0.092 1.341 0.957 1.299 0.284 1.133 1.405 0.963 0.102 1.274 0.555 0.225
0.139 1.429 0.001 0.483 0.099 1.094 0.554 1.280 0.286 0.947 0.779 0.544 0.559 0.947 0.977 0.406 0.044 0.247 0.724 0.528 1.279
1.345 0.959 0.158 0.985 0.093 0.613 0.850 0.169 1.395 0.631 0.911 1.246 0.130 1.259 0.167 0.781 0.676 1.349 1.314 1.273 1.106
1.326 0.921 1.416 0.740 0.003 0.363 1.489 0.022 0.748 0.327 1.410 0.769 0.122 0.837 1.022 0.547 1.156 1.274 1.046 0.712 0.984
0.246 0.237 1.168 0.316 1.427 0.361 1.061 1.142 0.123 0.324 0.732 1.335 0.489 0.771 1.070 0.584 0.548 0.225 1.461 1.477 0.835
0.370 0.827 1.281 1.082 0.435 0.497 0.505 0.757 0.065 0.204 1.062 0.144 1.106 0.368 0.297 0.520 1.010 0.648 0.202 0.152 0.210
1.092 0.317 0.587 0.861 1.483 0.412 0.039 0.646 0.068 1.020 0.124 1.305 0.823 0.649 0.029 0.767 0.875 0.226 0.332 1.040 0.239
0.269 1.318 1.430 0.409 0.157 0.996 0.606 0.073 0.110 0.496 0.683 0.159 0.849 0.209 1.309 1.274 0.652 0.625 0.515 0.373 1.293
1.181 0.224 0.367 0.965 0.011 1.012 1.120 0.415 1.026 0.409 0.357 1.271 1.459 1.435 0.953 1.023 1.262 0.993 0.465 1.478 1.108
0.695 1.138 0.130 0.646 1.402 1.027 0.052 0.895 0.577 1.041 0.521 1.307 0.130 1.340 0.948 0.060 0.456 0.050 0.776 1.284 0.978
0.843 0.125 1.217 1.359 0.038 1.161 0.425 0.516 0.657 1.430 0.139 0.901 1.459 0.800 1.432 0.852 0.609 0.610 1.465 1.032 0.753
0.820 0.097 1.364 0.789 1.287 1.147 0.784 1.281 1.310 1.398 0.142 0.713 1.200 0.596 0.535 0.159 1.333 1.077 0.896 1.324 1.156
09.44 0.705 0.195 0.326 0.210 1.137 1.129 0.592 0.383 0.776 1.202 0.413 1.381 0.709 0.974 0.808 1.074 0.662 1.257 0.679 0.498
0.425 1.008 0.423 0.845 0.078 0.285 0.051 1.131 0.304 0.688 0.182 0.795 0.143 0.667 0.020 0.726 0.271 1.024 0.279 0.762 1.293
0.786 0.603 1.305 0.443 1.221 1.134 1.085 0.874 0.808 0.629 0.621 0.929 0.171 0.570 0.039 0.908 0.850 0.194 0.175 0.595 0.630
0.276 0.416 1.020 0.474 1.329 0.717 1.216 0.101 0.660 0.447 0.827 0.713 1.481 1.325 0.879 0.280 1.426 0.300 1.364 0.866 1.011
0.022 1.450 0.136 0.752 1.055 0.301 1.297 1.487 0.249 0.394 0.172 1.233 0.588 0.004 0.572 0.771 0.211 1.225 0.165 1.461 1.317
1.257 0.772 1.198 1.011 0.014 0.099 0.993 0.948 1.251 0.711 0.937 0.759 0.259 1.295 1.238 0.462 0.513 0.756 0.589 0.443 0.396
1.295 1.491 0.622 0.045 0.265 1.275 0.121 0.456 0.715 0.621 0.628 0.185 0.026 0.858 0.977 0.575 0.787 1.447 0.844 0.690 1.409
0.055 0.131 0.721 0.819 0.950 0.805 0.358 1.403 1.353 1.336 0.660 0.625 0.450 1.332 0.956 0.387 0.861 0.189 0.975 0.164 0.561
21
Worker number
Assembly line balancing in the clothing industry 33
Table IV.
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Table V. Results of six trial runs
Run time (seconds)
Minutes required for producing 100 pieces
For 1st trail run 0.0 0.2 0.4 0.5 0.6 0.9 1.1 1.2 1.8 3.7 4.5 5.1 14.8 21.7 128.3 325.2 332.1 2,727.7 3,787.0 4,622.5 4,915.7 5,000.2
1,755.132 1,545.480 1,498.528 1,257.384 1,159.672 924.878 908.817 874.643 816.412 744.159 661.867 620.456 617.957 587.683 568.423 558.130 540.996 537.703 530.217 518.043 515.454 515.454
For 2nd trial run 0.0 0.1 0.1 0.2 0.3 0.3 0.8 0.9 1.2 5.9 17.5 31.1 32.4 1,057.2 1,057.6 1.058.3 1,639.1 2,042.6
23,976.244 3,027.356 2,908.633 1,734.647 1,217.030 857.192 791.374 782.822 732.812 647.108 613.117 609.480 555.051 549.690 544.105 533.953 532.080 522.420
Worker’s assignment from task 1 to task 41
{29, 26, 18, 6, 8, 19, 36, 2, 33, 20, 3, 37, 16, 15, 22, 23, 10, 35, 25, 11, 28, 7, 27, 12, 34, 31, 39, 21, 4, 14, 32, 41, 5, 38, 40, 17, 30, 9, 13, 24, 1}
{10, 40, 18, 6, 28, 3, 17, 11, 32, 19, 31, 23, 26, 39, 38, 25, 1, 41, 9, 36, 12, 22, 34, 20, 4, 21, 2, 35, 15, 16, 8, 7, 30, 14, 5, 33, 37, 27, 13, 29, 24}
(Continued)
Run time (seconds) 2,719.4 2,766.7 5,000.1
Minutes required for producing 100 pieces
Worker’s assignment from task 1 to task 41
517.487 505.078 505.078
Assembly line balancing in the clothing industry 35
For 3rd trial run 0.0 0.1 0.3 0.6 1.4 1.7 1.9 27.5 62.8 215.4 223.3 764.1 4,916.3 5,000.1
2,137.128 1,660.912 1,039.710 910.904 734.345 666.692 651.979 607.998 568.656 557.481 526.235 517.744 504.968 504.968
{39, 19, 25, 14,31, 35, 6, 27, 37, 36, 12, 2, 29, 24, 13, 18, 23, 5, 32, 40, 33, 26, 30, 38, 8, 17, 20, 11, 10, 34, 22, 15, 28, 21, 9, 16, 4, 41, 1, 7, 3}
For 4th trial run 0.0 0.1 0.3 0.3 0.7 1.5 7.9 24.0 24.2 36.1 71.2 71.5 1,249.5 1,544.6 2,798.3 4,529.1 5,000.2
10,234.165 1,172.557 914.375 905.982 794.460 652.810 642.656 629.464 612.067 590.272 538.931 527.517 522.944 518.418 513.117 496.882 496.882
{34, 36, 7, 10, 17, 9, 20, 2, 1, 25, 19, 41, 6, 11, 3, 40, 28, 35, 32, 22, 39, 27, 23, 29, 26, 30, 33, 31, 38, 13, 14, 8, 21, 18, 15, 37, 5, 16, 4, 24, 12}
For 5th trial run 0.0 0.2 0.3 0.4
1,720.063 1,579.982 1,431.533 1,102.631
{19, 16, 4, 29, 7, 24, 14, 38, 3, 31, 36, 5, 10, 27, 15, 25, 9, 12, 22, 23, 33, 13, 20, 35, 26, 6, 2, 17, 21, 34, 11, 30, 28, 39, 37, 32, 40, 41, 1, 8, 18} (Continued)
Table V.
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Table V.
Run time (seconds)
Minutes required for producing 100 pieces
0.6 0.9 0.9 2.0 3.2 7.0 8.6 9.3 26.7 27.4 222.2 304.8 305.0 5,000.1
1,014.611 910.961 855.581 834.057 762.128 731.472 691.279 647.269 636.713 579.979 561.292 503.399 495.709 495.79
For 6th trail run 0.0 0.1 0.2 0.3 0.4 0.5 0.9 1.1 1.4 1.6 1.6 2.1 2.4 2.8 5.2 6.1 9.5 10.9 18.9 21.9 26.7 52.2 107.0 165.5 225.6 226.0 226.2 448.7 514.8 5,000.2
2,333.627 2,054.006 1,458.752 1,186.272 1,111.605 1,006.604 1,003.631 830.645 814.738 791.902 784.169 780.760 746.748 705.693 699.834 685.861 682.148 672.053 653.879 611.126 605.234 595.430 584.864 567.486 565.132 557.635 556.989 552.983 474.567 474.567
Worker’s assignment from task 1 to task 41
{6, 30, 39, 31, 2, 40, 32, 35, 17, 28, 22, 20, 27, 5, 26, 9, 1, 19, 10, 33, 37, 34, 29, 12, 24, 7, 15, 23, 38, 21, 16, 8, 25, 14, 13, 41, 11, 18, 4, 3, 36}
7. Conclusion Assembly line In this paper, we present how a GA can be used for solving the ALB problem in balancing in the the clothing industry. By using GA, the ALB problem can be solved in an clothing industry effective manner to meet the realistic production conditions. The result of our numerical experiment showed that the performance of GA in handling ALB is much better than the performance of a greedy algorithm. We can conclude that 37 GA is an appropriate tool to solve the assembly line balancing problem, particularly for a dynamic and complex production environment like the clothing industry. References Anderson, E.J. and Ferris, M.C. (1990), “A genetic algorithm for the assembly line balancing problem”, Computer Science Technical Report No. 926, University of Wisconsin, March. Baybars, I. (1986), “A survey of exact algorithms for the simple assembly line balancing problem”, Management Science, Vol. 32 No. 8, p. 909. Bowman, E.H. (1960), “Assembly line balancing by linear programming”, Operational Research, Vol. 8 No. 3, p. 385. Fozzard, G., Spragg, J. and Tyler, D. (1996), “ Simulation of flow lines in clothing manufacture – part 1: model construction”, International Journal of Clothing Science and Technology, Vol. 8 No. 4, pp. 17-27. Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, pp. 122- 4. Hoffmann, T.R. (1990), “Assembly line balancing: a set of challenging problems”, International Journal of Production Research, Vol. 28, October, pp. 1807-15. Holland, J.H. (1975), Adaption in Natural and Artificial Systems, MIT Press, Cambridge, MA. Johnson, N.V. (1983), “A branch and bound algorithm for assembly line balancing problems with formulations irregularities”, Management Science, Vol. 29 No. 11, p. 1309. Kao, P.C. and Queyranne, M. (1982), “On dynamic programming methods for assembly line balancing”, Operations Research, Vol. 30 No. 2, p. 375. Man, K.F., Tang, K.S. and Kwong, S. (1996), “Genetic algorithms: concepts and applications”, IEEE Trans. on Industrial Electronics, Vol. 43 No. 5, October, pp. 519-33. Oliver, B.A., Kincade, D.H. and Albrecht, D. (1994), “Comparison of apparel production systems: a simulation”, Clothing and Textiles Research Journal, Vol. 12 No. 4, pp. 45-50. Rosser, P.S., Sommerfeld, J.T. and Tincher, W.C. (1991), “Discrete-event simulation of trousers manufacturing”, International Journal of Clothing Science and Technology, Vol. 3 No. 2, pp. 1831. Srinivas, M. and Patnaik, L.M. (1996), “On modelling genetic algorithms for functions of unitation”, IEEE Trans. on Systems, Mans. and Cybernetics, Vol. 26 No. 6, December, pp. 80921. Whitaker, D. (1973), “A study of a production line in the garment industry”, Clothing Institute Journal, Vol. XXI, pp. 113-20. Zbigniew, M. (1996), Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed., Springer-Verlag Berlin Heidelberg, New York, NY.
IJCST 10,1
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International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 38-49. © MCB University Press, 0955-6222
A new collision detection algorithm for garment animation G. Stylios and T.R. Wan University of Bradford, UK 1. Introduction With the rapid development of computer technology, it is possible to simulate fashion design with dynamic garment draping performance, using real mechanical properties of textile materials. One may choose a given fashion design and be able to see it on himself/herself, performing a “virtual wear trial” (Stylios, 1995) or to automatically view it on a super model as in a virtual fashion show before it is actually manufactured. When these techniques are fully developed, home shopping could become a reality (Stylios, 1995). Modelling of fabric drape has been challenging for many years and tremendous efforts have been made towards achieving this target (Amirbayat and Hearle, 1986; Breen et al., 1994; Carignan et al., 1992; Eberhardt et al., 1996; Lafleur et al., 1991; Lloyd, 1988; Moore and Wilhems, 1988; Stylios et al., 1996; Terzopoulos et al., 1987; Volino et al., 1995; Volino and Thalmann, 1994, 1997). One of the difficulties is due to the complex mechanics of textile materials, which appear non-linear, visco-elastic, history dependent and have large deformations. In the computer animation field, people are more interested in the appearance rather than in the precise simulation of materials, and hence compromises with heuristic methods may be acceptable, but introduce nongeneric solutions to this problem. These difficulties have slowed down the progress of research, especially in textile engineering areas. Review of the work concerning the use of conventional continuum mechanics and finite element approaches for simulating complex fabric draping (Amirbayat and Hearle, 1986; Lloyd, 1988; Werner, 1993) concludes that these methods have to overcome great obstacles, and only limited success has been achieved in some cases, because of the nature of the textile materials undergoing complex and large deformation. Recently, particle-based approaches for the modelling of fabrics (Breen et al., 1994; Eberhardt et al., 1996) appeared to be successful and has shown some potential. A “particle model” is based on a set of particles interacting with each other according to certain physical laws. Although the particle model is successful in simulating fabric drape, it has difficulties in modelling the actual textile materials, in a practical This project is conducted at the Center of Objective Measurement Technologies, which is supported by RETEX II project. The visualization tool has been, kindly, provided by ALIAS Inc. Thanks also go to OCF Ltd. for their support for the hardware facility provided.
way, and has also difficulties in simulating complex cases, for example, the dynamic performance of garments worn by animated virtual humans. Another physical approach is the use of deformation energies with dynamic constraints (Boulic et al., 1990; Carignan et al., 1992; Stylios et al., 1996; Terzopoulos et al., 1987), which appears most suitable in simulating complex fabric drape, but has difficulties in handling various garment collision situations. A recent particle approach (Volino and Thalmanny, 1997) appears effective in simulating deformation of garments by using complex collision and deformation control. Our work is based on the development of a dynamic fabric drape model (Stylios et al., 1996) and on our synthetic human model (Stylios et al., 1996), which aims to provide a complete solution for garment design and animation, and to try to establish the basis for the next generation of 3D CAD systems for fashion design, textile garment manufacture and retailing industries. The major parts of the work are as follows: • Textile material modelling. • Drape model stability and efficiency. • Virtual human modelling. • Collision detection and response. • Virtual fashion show implementation. We report the latest work of the drape model, with particular reference to the collision detection technique, which results in the production of a virtual fashion show. The implementation of the virtual fashion show has verified the stability and efficiency of our drape model. 2. Introduction to the fabric drape model In order to understand the concept of modelling of dynamic drape of fabrics, it is necessary to provide a general description of our model. Our approach is based on a physical analog to deep shell system. The fabric is treated as a continuum shell system initially, and then is discretized by lumping the distributive mass of the fabric and its mechanical properties to a large number of deformable node elements according to the mesh layout employed, where the elements can be equal or unique in size. It should be noted that textile materials display macrostructure (Lloyd, 1988), and the sizes of these basic fabric elements are selected to be large enough in comparison with their macrostructure but still very small compared with the size of the fabric itself. This is because fine fabric elements could provide accurate prediction of the behaviour of fabric. Using the approach described above, the behaviour of fabric is represented by the deformation process of the basic fabric elements. As shown in Figure 1, the deformation of a fabric element can be described by u and v displacements, along the first two surface coordinates α1, α2 within the tangent plane, and w displacement along the third surface coordinate α3 (the normal direction). The material properties of the continuum in all elements can be lumped at these
A new collision detection algorithm 39
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deformable nodes by integrating all the energies within those elements. The governing differential equations of the deformation of fabric are then derived from the discretization of the system energies over all fabric elements of fabric. Considering all energies of fabric elements, using Euler-Lagrange equations (Moiseiwitsch, 1966), a general description of the deformation of fabric can be determined by: 3 ∂L ∂ ∂L ∂ ∂ =∑ – L ( i = 1, 2, … , 3 ∂ψ i k =1 ∂X k ∂ (∂ψ i / ∂uk ) ∂t ∂ψ˙ i
where ψi are general functions –of energy component, representing ui (i = 1, 2, 3). – Using matrix notation ψ and X gives:
[
ψ = uvw and
[
]
T
X = α1 α 2 α 3 t
]
T
where t is the time variable. 3
w 2
v 0
Figure 1. Configuration of a basic fabric element
4
u 1
When considering energy function derivation within one basic fabric element in the model, we assume that the virtual energy density of strain will change continually and smoothly within the basic fabric element. This indicates that the energy density function, which is related to stresses and strains, is continuous. We also assume that they have continuous derivatives everywhere within the basic element and that the resultant effect on the whole shape of fabric, especially at locations of each node, will be identical to the effect of using lumped node-bar element treatment. Our model is also capable of including visco-elastic properties of fabrics. In order to represent precisely the behaviour of fabric, we have also added viscoelastic terms in the general energy equation. The simulation of the whole fabric drape process is divided into small time step sequences. At each time step, the initial locations of each point in the fabric are first identified and all the forces, such as gravity, boundary forces and collision forces between the fabric and the synthetic human are calculated, so that we can find all the energy terms in the equations of the fabric model. Then,
energy minimization is applied to find the new locations at each point in the fabric. 3. Collision detection One of the major problems of modelling the dynamic behavior of fabrics in garments is concerned with the interactions with fabric and the synthetic human body. This is called the collision detection and collision response which influence the movement of the garments when worn by a synthetic human. Generally speaking, when comparing textile material collision with collision in other areas of computer animation, the collision of fabric is more complex and difficult, for the following reasons: a fabric drape model involves a very large number of fabric elements, hence collision detection could be very time consuming. Therefore, since textile materials undergo complex, large deformation which appears non-linear, visco-elastic and history-dependent, the conventional continuum approaches appear unsuitable for the modelling of textile materials. These reasons can induce difficulties such as stability, versatility and precision of the model. A suitable approach should be able to incorporate complex properties of textile materials, have efficient and reliable collision detection. Such an approach should also be able to deal with various deformation situations such as dynamic folding and bulking etc. In the last ten years or so, some techniques dealing with collision have been developed. Lafleur et al. (1991) described a method to solve collision problems by simulating flexible fabrics with rigid bodies which were composed of polygons. Their method is a modification of Moore-Wilhelms methods (Moore and Wilhelms, 1988), which deal with surface collision problems. To avoid collisions when fabrics undergo deformation, they used a method consisting of creating a very thin repulsive force field around the obstacle surface. Volino and Thalmann (1994) presented a method for collision detection for garment animation, which was based on surface shape regularity, using a hierarchical representation of the garment surface. Recently, Volino et al. (1995) improved their techniques for fabric collision. In order to recognize the “right side” of a vertex from a surface polygon when considering a collision situation, they used extra memory to record the pre-collision “history”, so that, in case of collisions occurring, their system was able to correctly orientate the collisions and to correct wrong situations. In order to overcome the problem concerning garment surface orientation, we developed a precise technique which is able to detect the collision efficiently and reliably. The collision detection algorithm presented is based on a hierarchical fabric data base. Since the collisions occurring in garment animation are mainly related to the fabric folding, the following assumptions concerning garment deformations should be made: • The geometrical construction of fabric in garments is smooth and the basic deformation is formed in the ways of simple folding or bulking which indicates that the further folding is evolved naturally only by
A new collision detection algorithm 41
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basic body movements such as leg movements, and the forces such as fabric’s weights and collision forces between fabric and body, at different garment parts. Under this assumption, the deformations due to the consequence of complex actions such as grasping for instance, are not considered. • The initial draping occurs over the body shape of the synthetic human and then the fabric is deformed and driven mainly by basic body movements. Under the assumptions above, the collision cases, in principle, can be refereed as three basic types which are as follows: (1) The collisions between fabrics and body: this is a case of two colliding surfaces which belong to different objects. (2) The collisions between distant garment parts: the cases include the collisions occurring between separate parts of garments, such as a sleeve with the body panel. Cases such as the collisions occurring between one or more vertexes and the vertexes which are distant in geometrical structure are also treated as collisions between two separate parts of garments. Also, more than one vertex may be involved in collision at one time. (3) The local fabric collisions: collisions occur in a relative small region or locally. Different methodologies for dealing with collisions can be described as follows. Collisions occurring between fabrics and body surface The situation of collision between fabric and body is based on the collisions occurring between different objects. The procedure for collision detection and avoidance is as follows: given an existing position of a vertex (fabric element represented by a number of vertexes) at one time step, the system will send rays to its surroundings to check if any potential penetration or collision with body surface could occur. Actually, when real collision occurs, we can find exactly the collision position on the body surface. However, such a calculation is very timeconsuming. Instead of doing that, the system will calculate precisely the distances between the vertex examined and the nearest intersection point on the body surface nearby. If there is a case indicating that the two points are too close, this will be flagged as a collision case, then the collision response will be →c added to the corresponding fabric elements. Precisely, let V be the position of i → the examined vertex i, Vkb be the position of the nearest intersection point on the body surface, the collision between the two colliding→ surfaces can be →b c – V determined by examining if the projection of the vector V along the i k → normal direction Nhb is less than the minimum search distance tm, that is, if: r r r d i ,k = Vic – Vkb . N kb
(
)
then, a collision will occur and the collision response will be added to the fabric elements concerned. The forces, positions and the velocities of the corresponding elements will be adjusted to avoid evolving possible collisions. One of the simplest ways to add response is simply to cancel the components which lie along the normal direction of the collision point on the body surface. If di,k < 0, this indicates there are some penetrations occuring between two surfaces. The new position will be adjusted to adding an extra amount di,k but in the opposite direction.
A new collision detection algorithm 43
Collision between distant garment parts The situation of the collisions between distant garment parts can be treated like the collision between different objects, so that the same techniques can be applied. However, you need to remember or to code the part boundaries dynamically. In principle, the collision system will check the geometrical structure of the fabrics concerned when a collision is detected. If the two colliding points are far separated in view of initial structure, these two colliding parts will be catalogued as different parts of the garments and the techniques for collision between different objects will be applied. Figure 2 shows a typical case of the collision between two garment parts. Figure 2(a) shows the two parts move closer and Figure 2(b) shows a penetration occurred.
Figure 2. An example of the collision between the two garment parts (a)
(b)
Collision in a local region Compared with the collisions between distant garment parts, the situation of collisions occuring in a local region are more complex. Using only the surface information as described in the previous sections is not sufficient. A major problem is to recognize the orientation of surface of the garment parts, since the two colliding surfaces may penetrate each other. In order to deal with surface orientation, extra measures must be taken into consideration. The surface orientation problem can be illustrated by two simplified examples of typical cases, as shown in Figures 3 and 4. As can be seen, in order to find the surface orientation of the garment parts, when dealing with the matter of fabric collision and penetration, one must recognize whether the penetration is of the type of “inside to inside” of the surface, or “outside to outside”. For example, we define that the “outside” side of the surface is indicated by the direction of the
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→
→
normal Np and the “inside” side of the surface is opposite to the normal Np. In the case of the collision type of “inside to inside” as shown in Figure 3, two collision vertexes v1 and v2 move from lying inside with respect to each other’s normal direction to the outside with respect to each other’s normal position. Figure 4 shows an example of “outside to outside” type, which is a similar movement but in the reverse order. It is difficult to identify the two cases only use a current relative position of the two vertexes, since the cases are actually a history dependent. Np tm
Figure 3. An example of a simplified case of collision; inside to inside type (the figure only shows the outline of major collision section)
pm
outside
(a)
v2
v1
(b)
v1
v2 inside
dr
outside v2
pm
outside
inside
dr Figure 4. An example of a simplified case of collision; outside to outside type (the figure only shows the outline of major collision section)
Np tm
outside v1
v1
(a)
v2
(b)
Np
Np tm dr
pm
inside
tm
pm
inside
dr
Using the surface information as described in previous cases is insufficient to deal with the matter of the surface orientation of the fabric. In order to correctly recognize the surface orientation, extra measures concerned with the relative positions of local vertices with each other and their normal direction were taken into consideration. The algorithm is as follows: we first compute the common normal vector Np in the local collision region, as shown in Figures 3 and 4 and the intersection point Pm. The tangential plane of the surface at Pm can then be determined. Let Vi and Vj be the position vectors of the potential collision vertexes vi and vj, and dij = Vi – Pm, and tm is the standardized tangential vector along the curve with maximum curvature on the tangential plane at Pm. If dij. Np > 0, the collision type is properly of the “inside to inside”, as the case in Figure 5. If dij. Np < 0, the collision type is properly of the “outside to outside”, as the case in Figure 4. If dij. tm > 0, the direction of the collision avoiding will be set along the direction of tm and if dij. tm < 0, the direction of the collision avoiding will be set along the direction opposite to tm. In this way, we know exactly the right side of surface when a collision or penetration occurred.
Hierarchical data base: in order to improve the efficiency of the collision detection, our system used a hierarchical data structure. In principle, we define the vertices of a garment surface according to a hierarchical structure as shown in Figure 6. The collision process will start in the highest level nodes and a measure will be added at this stage. If a distance threshold is reached, which indicates potential collisions could occur in lower level nodes, the system will search further in a lower node level. The same principle will be applied to each level except the base level. If the threshold is not reached, the system will skip the search to lower node levels.
A new collision detection algorithm 45
Figure 5. An example of the collision in a local region
node (1,k)
higher level nodes
node (1,k+1)
base level nodes node (b,i)
node (b,i +1)
node (b,i +2)
node (b,i)
node (b,i +1)
node (b,i +2)
4. Modelling of synthetic humans For modelling an animated synthetic human, the following tasks need to be taken into consideration: • Development of an animated skeleton model incorporating various motion data, which can be used to simulate a real cat walk and to be able to animate a skinned human body. • Development of a skinned human model, which can be animated by a skeleton motion. • A method for the relocation of the shape data of the human model when the body is in motion. In order to model a synthetic human, a skeleton model was first developed, which can be regarded as a special linkage system, like an industrial robot, or a mechanical manipulator. There are a number of body modelling research of
Figure 6. A hierarchical structure of garment data
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complex cases reported (Badler, 1986; Boulic et al., 1990; Bruderlin et al., 1994; Sturman, 1986), in which the researchers try to model complex body movements precisely and efficiently. However, our main interest is to first, focus on fabric dynamic behaviour, where a relatively simple animation model may be adequate. At the current stage, our synthetic human model is able to complete a number of basic body movements, like lady’s or man’s walk, running and cat walking. The current skeleton model used consisted of 22 body segments arranged in accordance with the hierarchical structure shown in Figure 6. The model uses a group of rotational joints, each of which has three degrees of freedom. The skeleton movement is calculated by the joint angles and its reference point. Figure 7 shows an example of motion curves which control the skeleton joints over given time steps. These motion curves are generated in a local coordinate system relative to the position and orientation of the joint above in the hierarchical structure.
125
Figure 7. An example of a set of motion curves
0
00
125
250
375
In order to produce a realistic shape of body and its movement, a measure of a real body was used in the current work. This is the basic requirement for modelling the movement of a garment as it is being worn by the virtual human, since this information is required by the fabric drape model as a constraint to the garment, and also, to stop penetrating the skin of the synthetic actor during the motion of the garment. The next task is then to attach the skin in terms of body data to the skeleton. The body movement generation was created by keyframe techniques (Sturman, 1986). Figure 8 shows a skinned synthetic human lady with an animated skeleton. 5. Implementation The garment animation can be divided into two processes. The first stage is the drape of the garment as it follows the 3D body shape of the synthetic human. The resultant shape of the garment is determined by material mechanical properties, body shape and the defined garment design pattern. The second stage is the fabric movement driven by the animated synthetic human, whereby the shape of the fabric will try to follow the movement of the body. An example has been implemented and presented in this paper. Figure 9 shows an instance
A new collision detection algorithm 47
Figure 8. An example of a skinned synthetic lady
Figure 9. A virtual fashion show sequence
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of a cat-walk sequence taken from a virtual fashion. The skirt shown in these sequences was modelled using 1,440 (20 × 72) deformable node elements. The model is implemented in C++ with Alias Openmodel Library and the work was carried out on a SGI Indigo 2. 6. Conclusion and further work We reported on an approach for modelling fabric drape and its dynamic performance. Our model uses deformable node-bar notation which is based on a physical analog to a deep shell system. The major advantages of this model over other models are that its configuration is based on the surface coordinate system and that it uses fabric mechanical parameters. A new collision technique has been presented and addressed. The implementation of this collision system to our fabric model has shown that it is reliable and capable of dealing with complex deformation of garments during walking, using fully skinned virtual humans. Our aim is to provide new generic methodologies for garment design and manufacture as well as entertainment. Consequently, the fabric model in development should be able to model various textile materials. It would be then possible for people to choose the design or garment in accordance with their specific requirements, and to also establish a basis for the next generation of CAD systems for fashion design and garment industries. It may be possible in the near future for home shopping to become a reality. People could see a virtual fashion show in the comfort of their lounge. In this way, people can purchase garments by conducting virtual wear trials using their own body size and shape. And as far as the making up of the chosen garment is concerned, we have already developed an intelligent sewing machine (Stylios and Sotomi, 1994), which can automatically deal with optimum sewing data for the fabrics concerned. References Amirbayat, J. and Hearle, J.W.S. (1986), “The complex buckling of flexible sheet materials: part 1 – theoretical approach”, International Journal of Mechanical Sciences. Badler, N.I. (1986), “Animating human figures: perspectives and directions”, Graphics Interface ’86, Proceedings, pp. 115-20. Boulic, R., Thalmann, N.M. and Thalmann, D. (1990), “A global human walking model with realtime kinematic personification”, The Visual Computer, Vol. 6 No. 6, pp. 344-58. Breen, D.E., House, D.H. and Wozny, M.J. (1994), “A particle-based model for simulating the draping behaviours of woven cloth”, Textile Research Journal, November, pp. 663-85. Bruderlin, A., Teo, C.T. and Calvert, T. (1994), “Procedural movement for articulated figure animation”, Computer Graphics, Vol. 18 No. 4, pp. 453-61. Carignan, M., Yang, Y., Magnenat-Thalmann, N. and Magnenat-Thalmann, D. (1992), “Dressing animated synthetic actors with complex deformable clothes”, Computer Graphics (Proc. SIGGRAPH), Vol. 26 No. 2, pp. 99-104. Eberhardt, B., Weber, A. and Strasser, W. (1996), “A fast, flexible, particle-system model for cloth draping”, Computer Graphics in Textile and Apparel, IEEE Computer Graphics and Applications, pp. 52-9.
Lafleur, B., Magnenat-Thalmann, N. and Thalmann, D. (1991), “Cloth animation with self-collision detection”, Proceedings of the IFIP Conference on Modelling in Computer Graphics, pp. 179-87. Lloyd, D.W. (1988), “The analysis of complex fabric deformation in mechanics of flexible fibre assemblies”, in Hearle, J.W.S., Thwaites, J.J. and Amirbayat, J. (Eds), NATO Advanced Study Institute Series E: Applied Science No. 38, Sijthoff and Noordhoff, pp. 311-42. Moiseiwitsch, B.L. (1966), Variational Principles, John Wiley & Sons Ltd, London. Moore, M. and Wilhems, J. (1988), “Collision detection and response for computer animation”, Computer Graphics, Vol. 22 No. 4, pp. 289-98. Sturman, D. (1986), “Interactive keyframe animation of 3D articulated models”, Graphics Interface ’86, Tutorial on Computer Animation. Stylios, G. (1995), “Living without frontiers: the global retailer”, International Journal of Clothing Science and Technology, Vol. 7 No. 4, pp. 5-8. Stylios, G. and Sotomi, O.J. (1994), “A neuro-fuzzy control system for intelligent sewing machines”, Intelligent Systems Engineering, IEE Publication, No. 395, pp. 241-6. Stylios, G., Wan, T.R. and Powell, N.J. (1996), “Modeliing the dynamic drape of garments on synthetic humans in a virtual fashion show”, International Journal of Clothing Science and Technology, Vol. 8 No. 3, pp. 95-112. Terzopoulos, D., Platt, J., Barr, A. and Fleischer, K. (1987), “Elastically deformable models”, Computer Graphics, Vol. 21, 4 July. Volino, P. and Thalmann, N.M. (1994), “Efficient self-collision detection on smoothly discretized surface animations using geometrical shape regulatory”, EUROGRAPHIC 94 Proc., Vol. 13 Part 3, pp. 155-66. Volino, P. and Thalmann, N.M. (1997), “Interactive cloth simulation; problem and solutions”, from Desktop to Webtop: Virtual Environments on the Internet www and Networks, International Conference. Volino, P., Courchesne, M. and Thalmann, N.M. (1995), “Versatile and efficient techniques for simulating cloth and other deformable objects”, Computer Graphics Proceedings, Annual Conference Series, pp. 137-44. Werner, S. (1993), Vibrations of Shells and Plates, 2nd ed., Marcel Dekker, Inc. New York, NY.
A new collision detection algorithm 49
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50 Received March 1996 Accepted August 1997
Lightweight wool garment wrinkle performance A wear experiment C. J. Salter, A.F. Roczniok CSIRO Division of Wool Technology, Ryde, NSW, Australia and
L.G. Stephens Biometrics Unit CSIRO IAPP, Delhi Road, North Ryde, NSW, Australia Introduction Wool fabrics have traditionally shown superior wrinkling performance when compared to other natural fibres such as cotton and linen. Recently there has been an increased use of wool in lightweight garments. The wrinkles that form in lightweight and thin fabrics tend to be sharp and may not recover as well as they usually do in bulkier fabric constructions. Wear trials are the most direct method for accurately establishing the wrinkling performance of fabrics. Fabric wrinkling during wear is in practice due not only to a combination of fabric properties such as fabric construction, fibre stress relaxation, and fibre content, but also to wear variables such as the wearers themselves, the garment fit, the care procedures and ambient conditions during wear. In order to determine the relative contributions of these wear factors to the final wrinkling performance separately from the more traditionally considered fabric properties, some control over them is needed until their action and mechanisms are adequately understood. Background Wear trials have been conducted in the past to establish and quantify the effect of some of the important characteristics in the process of wrinkling in wear. Work has been carried out on the effect of fabric composition (Looney, 1969; Phillips, 1982) and treatments applied to the fabric (Makinson, 1970), as well as the effect of fabric temperature and regain during wrinkling (Sørensen and Høg,
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 50-63. MCB University Press, 0955-6222
Support for this project was provided by the Australian woolgrowers and the Australian Government through the International Wool Secretariat and CSIRO. The authors particularly appreciate the assistance of Mr P.G. Minazio from IWS (Biella) and Mr J. Mills from IWS (Ilkley) for the sourcing and supply of the fabrics from the wool industry, and also Fletcher Jones Pty Ltd, of Australia for the sourcing of fabric. The garments were tailored by Rundles Ltd. We are also indebted to Mr J. Nilon from CSIRO Division of Wool Technology, Geelong Laboratory for his useful comments on the commercial fabrics used in the trial. Finally, thank you to the wearers and observers for your time and effort put into this trial as without your help it would not have been possible. © CSIRO, Australia, published under licence.
1971). The changes in wrinkle performance due to the ageing of wool during wear (Mohar et al., 1987) as well as the effect of the wear and laundering (Wilkinson and Hoffman, 1959) have also been studied. Wear trials have also been compared directly with different test methods (Bostwick and Kärrholm, 1965; Lako and Veer, 1962; Matsuoka et al., 1984). In most of these trials the garments were worn during the normal activities of the wearers, without any control of the ambient conditions or activity of the wearer. In many cases the design of the trials did not attempt to control the care of the garment during wear. In most cases, where the wrinkle performance of worsteds was examined, the fabrics weighed more than 200gm –2 and sometimes over 300gm–2. Information on heavier weight fabrics was gained from these trials. However, there is little information available relating to the wrinkle performance of lightweight wool fabrics. Also, owing to the large costs associated with conducting wearer trials, the statistical design was often compromised. Generally either limited numbers of garments and wearers were used, or the subjective evaluation of the garment appearance was completed by only two or three observers. The garment’s wrinkled appearance was assessed by comparison to either another garment in the trial, or to a set of three dimensional wrinkle standards (AATCC, 1984). This allowed a judgement on garment performance in that particular trial to be made in comparison with another garment, or a split side garment, but did not provide quantitative information on wrinkle severity. Wear studies which have considered the effect of ambient conditions during wear have shown the large influence ambient conditions have on the resultant wrinkling performance. Sørenson and Høg (1971) measured the temperature and moisture regain profiles during normal and controlled wear. They found considerable variations in the monitored temperature and relative humidity from day to day, and season to season for the two fabrics tested. They showed that there was a positive correlation between the rated wrinkle appearance of garments and the temperature decrease which occurred in the garments after sitting. These temperature changes were significantly influenced by the room temperature, which in turn affected the temperature of the wearer’s skin and the temperature of the body/cloth microclimate. The moisture regain was found not to increase or change during sitting, and to decrease or remain constant during recovery in the experiments conducted. This current study used six wearers and six lightweight wool fabrics to determine the effects of wearer, fabric, condition of wear, and pressing on the resultant wrinkle performance, with garments worn in controlled ambient conditions for two-hour sessions. Experimental Design A balanced cyclic design (John, 1986) was used for this experiment which allowed four aspects of wear to be tested and is shown in Table I.
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Fabric Wearer
1
2
3
4
5
6
1 2 3 4 5 6
1 2 4 3 3 2
4 2 3 1 3 4
3 4 2 4 2 1
2 1 3 1 4 4
1 3 4 2 1 2
4 3 1 2 1 3
Notes: Code 1 Table I. 2 The cyclic design used in the wear trial with the 3 four treatments defined 4
Treatment 20°C 40% rh – no pressing 20°C 40% rh – pressing 25°C 75% rh – no pressing 25°C 75% rh – pressing
Six wearers tested the performance of six lightweight wool fabrics in two controlled ambient environments. The wear sessions were two hours in duration. Figure 1 shows the detailed structure of the wear protocol. The assessment of the garment wrinkling was made by photographing the garments at specific times after wear. Ten observers assessed the wrinkled appearance of the photographs, using a method that quantified the amount of the wrinkling. The factors wearer, fabric, condition of wear, and pressing, were defined at the beginning of the trial. This cyclic design allowed each factor to be estimated from each wearer testing a garment made from each fabric so that 36 combinations of wearer by fabric were used rather than the 144 combinations required in a full factorial design. However, only one interaction effect could be estimated using this design, i.e. condition of wear * pressing. Wearers Six male wearers were chosen for the trial. A brief qualitative description of the wearers is given in Table II. No measurements of the physiological changes of the wearers were made before, during or after the wear periods. Shimizu et al. (1993) have since shown that the sweat rate of sitting wearers increases over 25 minutes in ambient temperatures of 30°C, and similarly decreases in conditions of 20°C. Consequently, it was assumed that some physiological changes in the wearers would occur during the wear sessions, but only those that would usually occur in normal wear. Fabrics Six plain weave fabrics were chosen for the wear trial, their features are described in Table III. Manufacturing and finishing information was available for fabrics 1 and 6, as they were experimental fabrics. Qualitative comments on the probable finishing routes for the commercial fabrics have been provided.
Wrinkle performance
Garments Tailored Garments Pressed
Garments Aged 6 days (20°C 65%rh)
53 Garments Worn – Session 1 25 minutes sit Pressed stored overnight (20°C 65%rh)
Not Pressed stored overnight (20°C 65%rh)
5 minutes stand 25 minutes sit 5 minutes stand
Garments Worn – Session 2
25 minutes sit Pressed stored overnight (20°C 65%rh)
Not Pressed stored overnight (20°C 65%rh)
5 minutes stand 30 minutes sit
Garments Worn – Session 3
Assessment 1 hour
Figure 1. A flow chart outlining the wear protocol
Assessment 4 hours Assessment 24 hours
Wearer
Description
1 2 3 4 5 6
Small Tall Large Small Large Tall
Waist – cuff (cm)
Waist (cm)
97 110 91 103 96 109
84 95 126 78 110 87
The mean fibre diameter measurements were made on fibres taken from the fabrics, using Sirolan Laserscan. The fabrics were made up into tailored trousers of a plain, unpleated classical design as supplied to retail stores. The trousers were not dry cleaned during any stage of the trial, but were prepared
Table II. Garment sizes and description of wearers
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Description
1
2/46 21 µ wool, 2/50 Lycra, sirospun, 2% Lycra in warp and weft, light grey colour, crab, scour, piece dyed, tenter, crop, pressure decatised, 194gm–2 19.1 µ 100% wool, green, singles weft yarns, probably piece dyed, commercial fabric, 139gm–2 19.9 µ 100% wool, taupe, colour woven, probably pressure decatised, commercial fabric, 163gm–2 21.7 µ (mean) 20% polyester, 80% wool, black, commercial fabric, 161gm–2 19.6 µ 100% wool, checked, high twist yarn, probably pressure decatised, commercial fabric, 177gm–2 2/46 µ wool, sirospun, light grey colour, crab, scour, piece dyed, tenter, crop, pressure decatised, 190gm–2
2
54
3 4 5
Table III. Fabric characteristics
6
for the trial by pressing. This procedure is discussed below. After pressing, the trousers were hung from the waist in a standard conditioned laboratory (20°C, 65%rh) for six days prior to wear. When not being worn the trousers were stored in this environment. Conditions of wear Two ambient air conditions were chosen for the wear trial which could be expected to occur during the normal wear of garments made from these types of fabrics. The first condition, 20°C, 40 per cent rh, reproduced a typical office wear situation, the second condition, 25°C, 75 per cent rh, represented a fairly average spring day in Australia, or a representative average condition for a European summer day. The experiment was conducted in the Clothing Climate Chambers at CSIRO Division of Wool Technology, Ryde, Australia, which were specifically designed for comfort evaluation trials. Pressing In normal wear and care, garments tend to be pressed to improve or restore their appearance after wear. In this trial, each garment was worn for three sessions, over three consecutive days. This procedure was tested by pressing some garments after wear, prior to the next day’s wear, while others were not. All pressing in the trial was done as described in the pressing protocol. Pressing protocol Pressing was the only process used to standardise the garments prior to wear. There were three issues considered in the definition of the protocol: (1) The pressing action was to be as short as possible, as pressing in industry is done to achieve a good appearance in the shortest amount of time. (2) The pressing time had to be long enough to remove all of the wrinkles from wear sessions.
(3) The pressing operation had to be reproducible and the same for all garments. Table IV summarises the pressing procedure adopted for all pressing that occurred during the experiment. Garment wear Wear sessions were conducted in the mornings from 8 a.m.-11 a.m. Wearers tested each garment in three separate wear sessions over three consecutive days. They sat on canvas chairs for 25-minute periods, followed by five-minute periods of standing or walking. No restriction was placed on the way wearers sat, and some chose to cross their legs. The activity of the wearers was not regulated and they mainly talked, read, wrote, or used a computer. Except for wearers 1 and 2, all wearers sat for only one session per day. Wearer 1 sat two sessions a day for fabrics 3 and 5, and wearer 2 sat two sessions a day for fabrics 2 and 4. In the third week of the trial there was a malfunction of the climate rooms and five garments had to be retested after two wear sessions had been completed. In this case the garments were pressed and stored for one week and then treated as new.
Step
Action
1 2 3 4
5 seconds buck steam, head open, steam pressure 400kPa 5 seconds buck steam, head closed, steam pressure 400kPa 5 seconds, bake with no steam, head closed 5 seconds vacuum, head open
Assessment of wrinkles At this time there is no instrument or method to assess garment wrinkling objectively and so the assessment of the wrinkled trousers was done subjectively. As the garment wrinkles recover with time, it is physically impossible to get a significantly large number of assessors to rate the garments at the same time. The wrinkle performance of the garments was recorded by taking black and white photographs at several time periods during the recovery sections of the wear cycle. These times are identified in Figure 1. Using a viewing board, as set out in AATCC test method 128 (1984), garments were hung from the waist against a green background. The light source was incident from above casting a shadow over the garments to highlight the wrinkles. A camera was placed 1.2m from the hanging garment and photographs of the back view of the entire garment were made. A fine grained black and white film was used (Ilford PANF 50). The photographs of the back view were processed into A4 size prints on semi-matt paper. Each print was labelled with a random three digit number from 100 to 999 to identify it uniquely.
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55
Table IV. Pressing protocol
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Figure 2. The question sheet used in the subjective assessments of the photographs. The line in both questions was 150mm long
Ten observers were asked to view the prints of the garment. The question sheet given to the observers is shown in Figure 2. Two questions were asked for each photograph viewed, one on the wrinkled appearance of the garment, the other on its appearance acceptability. Two photographs of the AATCC three dimensional replicas (1 and 5), were supplied to define the ends of the scale “not wrinkled at all” and “extremely wrinkled”, in question 1. Observers were asked to use these as the reference points of the scale rather than their own personal idea of what the wrinkling intensity was at these extremes. To get the observers familiar with the task there was a training period where they were shown a variety of the photographs and asked to rate their appearance using the question sheet. The same information was given to all observers. When they asked questions about the features of the photographs they were not generally answered if they were specifically about the garment wrinkles. They were referred to use the reference photographs to make their assessment. The photographs were viewed by all observers in a standard conditioned laboratory (20°C, 65 per cent rh) under the same lighting conditions, where the 12 photographs were viewed at one sitting to prevent observer fatigue. The scores were collated by measuring the distance the mark made on the scale from the left hand side. These values were then used in the analysis.
Question 2 in Figure 2 was designed to establish how acceptable the observer found the wrinkled appearance of the garment. There were no guidelines provided on how to answer this question. The scores from this question were obtained in the same way as for question 1. A second set of photographs, showing only the back of the left knee at 1-2 hours recovery, was also tested. This was to establish if the observers had been assessing the behind the knee wrinkles or a full garment appearance parameter. A5 size prints were made and only the first question in Figure 1 was asked, as the results from the A4 size prints showed that the two questions were highly correlated to each other. Analysis The results obtained from the ten observers were averaged and then analysed using analysis of variance. The experimental design was not completely orthogonal. The factors of wearer and fabric were orthogonal as were the other factors of condition of wear and pressing. However, the factors of wearer and fabric were not orthogonal to the other two factors. The experimental design was balanced if the factor of wearer was fitted prior to the other factors of fabric, condition of wear and pressing. Fitting the wearer factor first was reasonable as the wearers were treated as a blocking factor in the experiment. The loss of information for the condition of wear and pressing factors was only small, the efficiency of estimation was 92.6 per cent. Recovery time was also analysed to determine the recovery relationship with the subjective assessments of wrinkles. It is well established that wool recovery behaviour is a viscoelastic phenomenon which occurs over logarithmic time. Initially several different analyses were completed on the data, which estimated the sources of variation in the trial at the specific recovery times. Time was treated as a factor with levels (0, 1, 2) corresponding closely to the logarithm of time to the base 5 (i.e. log5(time)). Three specific fabric contrasts were identified at the beginning of the trial: (1) Fabrics 3 and 5, two commercially produced pure wool fabrics, one of which was claimed by the manufacturer to perform better with respect to wrinkling. (2) Fabrics 1 and 6, two fabrics matched except for the inclusion of a small amount of lycra in fabric 1. (3) Fabric 4 with fabrics 3 and 5, a wool/polyester blend fabric compared with two pure wool fabrics. All of these were specifically tested for in the analyses. There were two data points missing from the data set and were treated as missing data in the analysis using substituted (estimated) values. Two different methods of calculating the significance of means were used, the Fisher’s protected least significance difference test (FPLSD) and Bonferroni’s method (BLSD) (Snedecor and Cochran, 1989).
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Results and discussion The analysis including time as a variate is given in Table V. There was a significant difference between the three assessment times tested and the resultant wrinkle performance, with the performance improving as the recovery time increased. The linear component of the time accounted for the largest amount of the variation in this factor, as is seen in the large value for the sum of squares term. It reflects that the photographs had a logarithmic feature which was most likely to be the change in the wrinkled appearance of the garments, and indirectly verified that the observers assessed a wrinkling feature of the garment. The quadratic component was also significant, but accounted for a much smaller proportion of the variation. The analyses at the individual times also provided similar results to these. The results from the assessments using the A5 photographs of wrinkling (behind the knee) of the trousers, produced similar results to the assessments using A4 photograph in terms of the significant factors and the extent of their significance. Only the results for the assessments using A4 photographs are presented here. The analysis of question 2 showed that there was a direct correlation between the acceptability of the wrinkled garment (question 2) and the wrinkled appearance of the garment (question 1), with poor appearance being synonymous with low acceptance. The nature of the experimental design meant that it was not possible to extract the fabric rankings for the different wearers, conditions of wear, pressing or their interaction, as the design was not completely orthogonal.
Source of variation (Wearer.occasion).stratum Wearer Fabric Fabric 5 vs fabric 3 Fabric 1 vs fabric 6 Fabric 4 vs (average of fabrics 5 and 3) Deviations Conditions of wear Pressing Conditions of wear * pressing Residual
Table V. ANOVA for wear analysed with time transformed to log5
(Wearer.occasion).time.stratum Log5(time) Linear Quadratic Residual Total
DF
5 5 1 1 1 2 1 1 1 22 2 1 1 70 107
SS
MS
F
36,670.2 6,734.0 10.52 28,356.1 5,671.2 8.86 4,407.9 4,407.9 6.89 11.1 11.1 0.02 1,970.8 1,970.8 3.08 21,966.5 10,983.2 17.16 71,253.4 71,253.4 111.34 226.9 226.9 0.35 807.9 807.9 1.26 14,079.7 640.0 5.64 15,517.7 7,758.8 11,411.7 11,411.7 4,106.0 4,106.0 8,613.8 123.4 172,522.8
63.05 92.74 33.37
P
9.87). The first column of Figure 3 shows the simple classical (1,0) mode of buckling. For larger normalised buckling forces, the fabric profile will “bulge” as the ends are brought together until they touch. There is then a region of no fabric buckling 24 < Pl2/B < 247, after which the more complex (3,2) mode of buckling occurs as y l 0.4
ψ 2
0.3 1.5 (3)
(2)
0.2 0.1
(1) –0.5
dψ ds
–0.25 (a)
0.2
0.4
0.5
0 x l 0.6
0.8
–1 (1) –2
(2)
(3)
1 (1) 0.2
0.4
0.6
0.8
(b)
1
d2ψ ds2 –1
s 0.2
0.4 (1) 0.6
–2 (2)
–3
–3
(2)
–4 (3)
–4 (c)
s 1
(3)
–5 (d)
0.8
1
Figure 2. The numerically computed output of the elastic fabric buckling model described by equation (1) expressed as: (a) the fabric profiles; (b) the actual output, the tangent angle to the horizontal ψ as a function of s along the curve; (c) the curvature, dψ/ds and (d) the change of curvature d2ψ/ds2 for the left half of the buckled fabric for inputs: (1) Pl2/B = 10, (2) Pl2/B = 14 and (3) Pl2/B = 21.1
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0.1
Pl2 = 248 B Pl2 = 10 B
x l
0.3
–0.5
–0.25
–0.25
Pl2 = 1308 y B 0.05 l
0.5
–0.1
0.1
–0.5
0.25 –0.05
0.2
308
0.05
y l
0.4
y l
x l 0
0.25
x l
0.5 –0.15
Pl2 B Pl2 = 14 B
0.1
= 392 0.05
y l
0.4
x l –0.5
0.3
0.15
y l
–0.25
0.25
–0.05
0.5
–0.05 0.05
0.2 –0.1
0.1
Figure 3. Numerically computed fabric profiles describing the output of the buckling model of equation (1a) as the input Pl2/B increases: the (1,0) buckling mode in the first column, the (3,2) buckling mode in the second column and the (5,4) buckling mode in the third column
–0.5
Pl2 B
–0.25
x l 0
0.4
= 21.2
0.25
Pl2 = 1440 B
0.5
0.1
Pl2 = 448 B
y l
y l
x l
y l
–0.15
0.15
0.05
x l –0.5
0.3
–0.25
0.25
–0.05
0.5 –0.05
0.2 –0.1
0.1
–0.5
–0.25
x l 0
0.25
0.5
shown in the second column of Figure 3. The profile develops into a “meandering river” shape until the sides of the material touch. After another flat region 452 < Pl2/B < 1,308 in which no buckling occurs, the profile of the buckled fabric evolves into the (5,4) buckling mode shown in the third column of Figure 3. As the normalised buckling force Pl2/B continues to increase, more complex higher modes of buckling evolve. e.g. the (7,6), (9,8), (11,10), … modes. always with intermediate regions between each mode where no buckling occurs. Nonlinear fabric dynamics We have seen very rapid progress in recent years in the study of nonlinear dynamics when applied to many problems in physics and engineering. Examples include oceanography, ultra-short pulses in electronic communi-
cations using optical fibres, propagation of a crystal dislocation and energy The dynamics of transport in complex biological systems. The mathematical analysis of these fabric drape problems has much in common with the mechanical and dynamical phenomena associated with fabric drape, folding, buckling, wrinkling and associated fabric characteristics. We shall now formulate the mathematical model in such a way that it is 309 capable of predicting the exact configuration of a fabric draped in three dimensions. To achieve this aim, it would be a major advantage if our nonlinear differential equation (1a) could be extended from two dimensions to three dimensions and solved analytically rather than by numerical computation as has been the case in previous sections of this paper. Numerical computation of solutions of nonlinear differential equations can be very tedious especially if iteration is involved. This is the case when interfibre friction within the fabric is considered. Furthermore, numerical computation is not always stable as the solution becomes chaotic for specific values of the input parameters. Such problems would be completely overcome if we could find true analytical mathematical solutions to equation (1a) as these solutions would be universally applicable and could be directly related to the fabric mechanical properties. In the previous sections of this paper, two-dimensional buckling and folding fabric deformations have been modelled by numerical computation. We now consider three-dimensional fabric drape to be analogous to the flow behaviour in nonlinear fluid dynamics. In the same way that a two-dimensional wave forms in water and propagates along the surface, a portion of a fabric is deformed (bent, buckled, sheared or stretched) and this deformation travels yarn by yarn along the fabric evolving into a three-dimensional drape configuration. In the 1920s, Klein and Gordon derived a relativistic equation for a charged particle in an electromagnetic field, using the recently discovered ideas of quantum theory. Their Klein-Gordon equation is a mathematical generalisation of our fabric buckling nonlinear differential equation (1a). The Klein-Gordon family of equations is invariant under the Lorentz transformation, i.e. once one solution is known, an infinite number of other solutions can be found by coordinate transformations. One particular member of the Klein-Gordon family of equations, the so-called “sine-Gordon” equation reduces to (2a) Equations in the Klein-Gordon family of equations (including equation (1a)) can be obtained from this equation by coordinate transformations. The sine-Gordon equation has also been applied to mechanical models, magnetic-flux propagation in a large Josephson junction for superconductors, Bloch-wall motion in domain wall dynamics in magnetic crystals, propagation of ultra-short optical pulses in fibre optics and a unitary theory of elementary particles. The sine-Gordon equation was not considered solvable until the development of mathematical Bäcklund transformations in solitary wave
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(soliton) theory. The sine-Gordon equation is now known to be an integrable nonlinear differential equation, meaning that true analytical solutions can be found which do not become chaotic for any range of the variables x and t. These solutions are called soliton or solitary wave solutions which have the unique property of being described analytically and possessing an infinite number of conservation laws. Also, from one solution, an infinite number of other solutions can be obtained by using Bäcklund transformations (a purely algebraic procedure). Bäcklund transformations are a generalisation of surfaces of constant negative curvature. (Background on this area of mathematics can be found in[2].) They transform one pseudospherical surface into another surface of the same total curvature. On these two surfaces, the lines of curvature correspond, the line of corresponding points is tangent at these points to the surface and has a constant length, and the tangent planes at corresponding points meet at a constant angle. Let ω be the supplementary angle between intersecting geodesic lines so that our co-ordinates are polar geodesic as shown in Figure 4. The parameters α and β represent the asymptotic lines. When the transforms of a given pseudospherical surface are known, the Gauss equation becomes (2b) which is also now known as the sine-Gordon equation in “characteristic” coordinates where α = 1⁄2 (x – ct ) and β = 1⁄2 (x + ct ). If ω = φ (α,β) is a solution of equation (2b), then ω1 = φ (αm, β/m) is also a solution, where m is any constant. Therefore, from one pseudospherical surface, an infinity of other pseudospherical surfaces can be obtained by solving this equation. Lamb[3] reintroduced a classical Bäcklund transformation of the sineGordon equation (2b) that leaves the equation invariant and used this transformation to construct multi-soliton solutions. Figure 5 shows the analytical solution, known as the stationary breather which is expressed mathematically as (3)
O’’’
Figure 4. Polar geodesic parameters used in differential geometry for Bäcklund transformations
O’’
M
O
ω
O’
The dynamics of fabric drape
where
representing another solution of the sine-Gordon equation. This true analytical solution corresponds to our computed numerical solution for fabric buckling where the fabric can either buckle upwards against gravity or drape downwards with gravity as in Figure 5. As an example of the many other solutions which can be obtained using Bäcklund transformations, the two-wave or two-soliton solution:
311
(4)
for the sine-Gordon equation (2) is graphed in Figure 6 where the two curves (called kinks) undergo a nonlinear collision with phase shift µ. In summary, we can now model three-dimensional fabric drape dynamically by an integrable system of solitary wave or soliton equations from which true analytical solutions can be obtained giving all the forces and moments in the material, and the geometry of the draped fabric. Conclusion A universally applicable mathematical model is presented in this paper for twodimensional fabric buckling, folding and wrinkle deformation for which a range of numerically computed solutions have been analysed. We have described in
5 2.5 0
4
–2.5
2
–5
0 –4
–2
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Figure 5. A mathematical stationary breather, an analytical solution (equation (3)) for the sine-Gordon equation (2) corresponding to our numerical solution for fabric buckling or folding in Figures 2 and 3.
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1
0 0.5
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Figure 6. The two-solution (equation 4) of the sineGordon equation (2)
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mathematical detail the exact profiles of a buckled or folded fabric as well as the simple and higher order complex modes of fabric deformation which occur for different values of the fabric mechanical parameters under a given set of boundary conditions. Fabric properties considered in the mathematical analysis include the fabric bending rigidity and the bending length or bending rigidity per unit weight of the fabric. We have shown how this fundamental mathematical approach should be capable of predicting the dynamic fabric behaviour as well as the dynamic processing or tailoring behaviour of textile materials simply by solving particular examples of mathematical differential equations referred to as the Klein-Gordon family of equations. An additional quite exciting feature of this family of equations is the fact that they have been mathematically proven to have analytical solutions (called solitary wave or soliton solutions) which are applicable under all conditions and are not subject to computational difficulties associated with finding numerical solutions for highly nonlinear problems. The use of this analytical approach to fabric mechanics and dynamics provides us with a very powerful tool to formulate and solve many long-standing problems in fabric and clothing technology. References 1. Peirce, F.T., “The handle of cloth as a measurable quantity”, J. Textile. Inst., Vol. 21, 1930, p. T377. 2. Eisenhart, L.P., A Treatise on the Differential Geometry of Curves and Surfaces, Ginn., Boston, MA, 1936. 3. Lamb, G.L. Jr, “Bäcklund transformations for certain nonlinear evolution equations”, J. Math. Physics, Vol. 15, 1967, pp. 2157-65.
IJCST 10,5
324 Received October 1997 Revised July 1998 Accepted July 1998
An investigation of the structure of sizing systems A comparison of three multidimensional optimized sizing systems generated from anthropometric data with the ASTM standard D5585-94 Susan P. Ashdown Department of Textiles and Apparel, Cornell University, Ithaca, New York, USA
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 324-341, © MCB University Press, 0955-6222
Introduction The sizing systems used to create a range of sizes for the ready-to-wear fashions sold to women in the USA are flawed in many ways. Women must try on multiple garments when shopping, and often feel that they cannot find an appropriate size[1,2]. These problems are a result of many factors including the use of a sizing system created from outdated anthropometric data, the lack of standardized size labeling, the lack of body measurements on hang tags, and the lack of sizes appropriate for the full range of variation in body type that exists in the population[3,4]. The sizing systems used for ready-to-wear clothing worldwide are created using a variety of methods ranging from trial and error to the use of elaborate statistical methods[5,6]. None of these methods addresses the problems of fitting a population in which there is a large amount of variability in many dimensions. It is possible to design sizing systems that will accommodate this variability today, given the power and sophistication of computer based calculations. A method of creating optimized sizing systems directly from the anthropometric database is used to create a series of different sizing systems. These sizing systems are designed to optimize the fit using as many variables (body dimensions) as are needed to account for the variability in the population. Therefore the resulting sizing systems will potentially fit the population better than sizing systems based on one or two dimensions only. Another advantage of the optimized sizing systems is that they can be structured in different ways by putting constraints on the system in order to simplify product development and/or distribution of the garments. However, the cost of constraining the system is that the performance of the sizing systems is compromised. It is necessary to balance the needs of providing appropriate sizes and a workable structure. In order to consider the various choices between the best fitting sizing system for the population and the best structure of the system, three optimized
sizing systems with various structures will be compared to one another and to an existing sizing system. The structure of sizing systems Sizing systems used in the design and distribution of ready-to-wear clothing are generally based on a selection of dimensions from an anthropometric study of the population for which the sizing system is designed. Key body dimensions are chosen to divide the population into size groups. The goal of any sizing system is to choose these size groups in such a way that a limited number of sizes will provide clothing that fits most individuals in the population. Although sizing systems developed by different countries vary in the body dimensions chosen to divide the population, the basic structure of most sizing systems is very similar. To create a sizing system, the population is first divided into different body types based on dimensions such as height or ratios between body measurements. A set of size categories is developed, each containing a range of sizes from small to large. The size ranges within a size category are based on one key body dimension. The sizes are generally evenly distributed from the smallest size to the largest[7]. Once sizes are identified, the remaining body dimensions necessary to design the pattern for the garment must be determined. Dimensions proportional to the key body dimension are chosen so that the garment patterns will be proportional to one another. Tables I and II show two sizing systems, a system from the USA in which size categories are based on proportional differences with no progression
Size categories
Definition of category
Sizes in category
Size breaks
Junior
Youthful body type, high bust, small waist 5'4'' to 5'6'' in height Proportions same as Misses, height 5' to 5'4''
Odd numbers, 3-15
One break, at size 9
Even numbers, 2-14
Two breaks, at sizes 10 and 18 Two breaks, at sizes 10 and 18 One break, at size 18
Petite
Misses
“Average” body type, longer waist than Junior 5'5'' to 5'8'' in height Women’s Larger proportions, fuller figure than Misses, 5'5'' to 5'9'' in height Plus sizes Larger figure types, corresponding to Misses 16 and larger Note: all body dimensions are in feet and inches Source: plus sizes[8] ; all other sizes[9]
Even numbers, 4-18
Even numbers, 14-24
Even numbers, 12-32
One break, at size 16
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Table I. Sizing system from the USA showing size categories and size breaks
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between categories, and a two-dimensional Korean system in which categories are based on height. History of sizing systems in the USA The structure of sizing systems used for ready-to-wear today has its origins in the proportional drafting systems developed by tailors in the latter part of the eighteenth century[10]. Clothing at this time was custom-fitted. Tailors drafted patterns from an individual’s body measurements to create the custom-fitted garments. As tailors drafted multiple patterns, they recognized certain relationships that seemed to exist between body dimensions of different individuals. Gradually pattern drafting systems were developed that made use of these relationships in order to provide a stock of ready-made clothing that could be sold to any number of similarly sized individuals. A gradual transition occurred from systems that provided custom-made clothing to the mass production of ready-to-wear clothing. By the 1920s most clothing produced nationwide was mass produced using methods which have not changed in any essential way up to the present era. Sizing standards in the USA Individual manufacturers in the USA developed their own sizing systems until the first sizing standard was published in 1958. In this year the United States Department of Commerce published Commercial Standard CS 215-58 as a voluntary sizing standard for the apparel industry[11]. The sizes were based on measurements from a 1941 anthropometric study of 10,042 women[12]. Women were divided into different size categories including Misses, Juniors, Women, and Half-sizes. Sizes in the range were based on bust measurements. In 1970 a new standard, PS 42-70 was published that incorporated additional anthropometric data from an army study[13]. Both the CS 215-58 and the PS 42-70 sizing standards are based on an assumption of proportional body measurements. Once the population is divided into the various sub-groups (i.e. Misses or Juniors) the sizes are identified by bust circumference. All other body dimensions for each size are then generated so that they remain proportional with the bust circumference. This results in a sizing system with a linear relationship between sizes.
Table II. Sizing system from Korea[7]
Size categories defined by height in cm
Sizes in category
Size breaks
Height < 150cm
42, 43, 44, 45
None
150cm < height < 155cm
53, 54, 55, 56, 57
None
155cm < height < 160cm
64, 65, 66, 67
None
160cm < height < 165cm
75, 76, 77, 78
None
165cm < height < 170cm
85, 86, 87, 88, 89
None
The development of ASTM D5585-94 By the 1990s the standard sizes from PS42-70 were no longer appropriate for the population of women in the USA. This standard was based on anthropometric data from a 1941 study and the current population is very different from that of the 1940s. There has also been a shift in the relationship between body measurements and size designations. Size designations, the number that identifies each size, are generally not related to body measurements in the sizing systems for women in the USA. For example, the Misses size designations are even numbers from 2 to 20. Originally these numbers probably referred to chronological age, but this connection was lost long ago. Because the size designations do not refer to any actual body measurement, the sizes can easily change. Currently different manufacturers use the same size designations for clothing that fits different body measurements. Because of the confusion about size designations, women must try on multiple garments to discover which ones will fit their particular body size and proportions[14]. Because of the lack of current standards and confusion about size designations a committee of the American Society of Testing and Materials (ASTM), committee D 13.55 has been formed to develop new voluntary standards for the industry. This committee has published an updated standard for Misses sizes, designation D5585-94. The standard is not derived from new anthropometric data, but is compiled from designer experience and market observations to reflect the sizing most commonly used by manufacturers and retail organizations in the USA today. The results were also cross-checked with anthropometric databases from the US Army and the US Navy[15]. The standard consists of ten sizes, each size identified by 39 body measurements. The sizes range from a size 2 with a bust measurement of 32'' (18.28 cm) to a size 20 with a bust measurement of 44.5'' (113.03 cm). Stature measurements range from 63.5'' (161.29 cm) to 68'' (172.72 cm). ASTM D5585-94 was chosen to compare to the optimized sizing systems as it is the most recently developed sizing system for women in the USA that corresponds to some degree to an available anthropometric database. The optimized sizing systems As the goal is to design optimized sizing systems that can be directly compared to ASTM D5585-94, it is necessary to make choices of comparable anthropometric data between the ASTM standard and the anthropometric data to be used to create the optimized systems(1). It is also necessary to limit the sample to a selection of individuals that can reasonably be regarded as the Misses subpopulation since the Misses sizing standard is not intended to fit the whole population. The choices made to calculate the optimized systems are described below. 1988 anthropometric survey of US army personnel There is no current, professionally conducted anthropometric study of the US civilian population with data that are appropriate for apparel sizing. However,
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an anthropometric study of US army personnel was conducted by Gordon et al. (1988) in which 1,774 men and 2,208 women were measured. This study, known as the ANSUR study, was conducted to collect data for the design and sizing of clothing and equipment[16]. Selection of body dimensions Many of the body measurements for the ANSUR study were taken with an anthropometer or with calipers, resulting in the shortest distance between the two landmarks measured. Measurements from the apparel standard ASTM D5585-94 were always made with a tape measure across the surface of the body. An analysis of all the body measurements from ASTM D5585-94 with the measurements from the ANSUR database was made to select those that were comparable. More of the lower body measurements corresponded than upper body measurements; therefore the decision was made to develop sizing systems that would be appropriate for women’s slacks or jeans. An advantage of the optimization formulation used to create the sizing systems is that it is possible to create a truly multivariate sizing system. It is not necessary to select one or two dimensions and to calculate all others so they are proportional to the key dimensions. Instead, any number of dimensions can be used, so that the particular combination of dimensions for each size is optimized for the individuals in the sample that will fit that size. Four dimensions were chosen to create the optimized systems based on a judgement of which variables would provide the best fit for the garment style. The chosen dimensions are: (1) hip circumference; (2) waist circumference; (3) crotch height; and (4) crotch length. Crotch height corresponds to the inseam measurement of the pants. Crotch length is the measurement from the center front at the waist, through the legs, to the center back at the waist. Defining the Misses sub-sample The database was sorted in order to identify the portion of the sample that would be expected to fit the sizes in ASTM D5585-94. None of the apparel sizing standards actually defines a method for identifying the Misses population. The dimensions commonly used by the apparel industry to identify this population are height and the bust to waist or waist to hip proportion. Therefore the sample was sorted using stature and waist to hip proportions derived from ASTM D5585-94. The stature limitations were calculated by subtracting a half grade interval from the smallest stature value and adding a half grade interval to the largest stature value in ASTM D5585-94, resulting in heights between 63''
(160.02cm) and 68.75'' (174.63cm). Limitations on the waist to hip relationship were set using the following formula: 0.95 * (H – 10.5'') ≤ W ≤ 1.05 * (H – 10.5'') where W = waist circumference in inches and H = hip circumference in inches. All hip sizes in ASTM D5585-94 are exactly 10.5" larger than the corresponding waist size. This formula allows 10 per cent variability around the relationship postulated by the ASTM standard. Sorting the sample of 2,208 women for stature and waist to hip proportion resulted in a sub-sample of 752 women. Optimization methodology The sizing systems were created using the optimization methodology described in Paal [17] and McCulloch et al.[18]. The core of this methodology is a mathematical description of the goodness of fit that an individual experiences when wearing a garment of a particular size. The concept of garment fit is captured by a distance measure, which is calculated from the discrepancies between the body measurements of an individual in the sample and the prototype design values of a size. A larger discrepancy results in a larger distance measure and a worse fit of the final garment, which is designed to fit the prototype body for that size perfectly(2). An advantage of the optimization procedure used is its ability to identify the individuals who will be accommodated simultaneously with selection of the prototype body sizes. Another advantage is that the distance measure, which is the basis of the optimization routine, automatically assigns individuals to their proper sizes. By contrast, heuristic methods of choosing prototypes leave the problem of size assignment unresolved. McCulloch et al.[18] gives full details on how nonlinear optimization techniques can be applied to calculate optimized sizing systems. Aggregate loss as a measure of goodness of fit When all individuals’ distances from their assigned size are averaged over the whole population, an aggregate measure is created that represents how well the sizing system performs in fitting the population. Using this measure, which we call aggregate loss, various existing sizing systems can be compared. An optimal sizing system with a given number of sizes is defined as one that minimizes the value of aggregate loss. In such a sizing system the average distance of individuals from their size is as low as possible. Therefore, on average, the system provides the best fit. The prototype design values for each size in the set of optimized sizes are based on the measurements of the individuals in the sample. Any number of body measurements can be used to create the sizing system, and various formulations of the distance measure are possible. For the purpose of this illustration, the quadratic average of log differences was chosen as the distance measure. The advantage of this simple measure is that the value of the distance can be interpreted as the average (over the four body dimensions considered) of
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percentage differences between an individual’s body measurements and the prototype design values of her assigned size. This use of the quadratic average implies that both positive and negative differences increase the distance. Therefore the fit is worse both when the garment is too big and when the garment is too small along a particular dimension. More complete measures and considerations regarding the choice of a proper distance measure are described in Paal[17]. Imposing structural constraints on the sizing systems A further advantage of the above methodology is that it allows a means of incorporating additional requirements that constrain the sizing system to exhibit desired relationships between sizes. The sizes are still chosen so that they represent the optimal placement among those sizing systems that satisfy the structural constraint. Three different structures were considered: a linear system, a two tiered system, and an unconstrained system. Ten sizes were created for each sizing system so that a direct comparison could be made with the ten sizes of ASTM D5585-94. These sizing systems were based on one half of the sub-sample of 752 women in the Misses size range as defined above. The remaining half of the sub-sample was reserved for testing the systems. The linear system was created by finding the ten optimized sizes in such a way that the increments between each of the sizes in each of the four dimensions remained proportionally constant. This can be thought of as a linear system in which the grade increments between the sizes are calculated as percentages of the measurement (for each of the four measurements). The two tiered system with ten sizes can be thought of as the union of two linear systems with five sizes each. The grade increments are proportionally constant within each tier, but not necessarily the same between the two tiers. Geometrically this results in two lines that are not necessarily parallel. The unconstrained system was created by searching for the optimized ten sizes with no constraints. In this case the system created is not linear in any of the four dimensions and the grade between the sizes is different for each size and for each dimension. Results Tables III-VI show the four dimensions for each of the ten sizes from the three optimized systems and from ASTM 5585-94. The grade rules for each size and the percentage of increase between sizes are also shown. Figures 1-4 show each of the sizing systems plotted in three dimensions, along with the data cloud of the sub-sample of 376 individuals who were reserved for testing the sizing systems. (The fourth dimension, waist circumference, is highly correlated with the hip circumference due to the method used to select the sub-sample, so a plot of waist, crotch height, and crotch length would be much the same.) The cubes plotted on these graphs represent size centers and not the extent of each size. They are placed so that the center of each cube is at the prototype design value for that size. They are
ASTM Hip circumference D5585-94 (inches) size Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
Crotch length (inches) Value Grade %gr.
2 4 6 8 10 12 14 16 18 20
24.00 25.00 26.00 27.00 28.00 29.50 31.00 32.50 34.50 36.50
25.00 25.75 26.50 27.25 28.00 28.75 29.50 30.25 31.00 31.75
34.50 35.50 36.50 37.50 38.50 40.00 41.50 43.00 45.00 47.00
1.00 1.00 1.00 1.00 1.50 1.50 1.50 2.00 2.00
2.9% 2.8% 2.7% 2.7% 3.9% 3.8% 3.6% 4.7% 4.4%
1.00 1.00 1.00 1.00 1.50 1.50 1.50 2.00 2.00
4.2% 4.0% 3.8% 3.7% 5.4% 5.1% 4.8% 6.2% 5.8%
29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
3.0% 2.9% 2.8% 2.8% 2.7% 2.6% 2.5% 2.5% 2.4%
Notes: “Grade” is the amount that this dimension increases to the next size “% gr.” is per cent grade, or the percentage increase to the next size
Linear size
Hip circumference (inches) Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
Crotch length (inches) Value Grade %gr.
A B C D E F G H I J
35.05 35.97 36.91 37.88 38.86 39.88 40.92 41.99 43.09 44.22
24.63 25.55 26.49 27.48 28.50 29.55 30.65 31.78 32.96 34.18
26.91 27.73 28.56 29.42 30.31 31.22 32.17 33.14 34.14 35.17
0.92 0.94 0.97 0.98 1.02 1.04 1.07 1.10 1.13
2.6% 2.6% 2.6% 2.6% 2.6% 2.6% 2.6% 2.6% 2.6%
0.92 0.94 0.99 1.02 1.05 1.10 1.13 1.18 1.22
3.7% 3.7% 3.7% 3.7% 3.7% 3.7% 3.7% 3.7% 3.7%
30.81 30.84 30.87 30.90 30.93 30.96 31.00 31.03 31.06 31.09
0.03 0.03 0.03 0.03 0.03 0.04 0.03 0.03 0.03
Notes: “Grade” is the amount that this dimension increases to the next size “% gr.” is per cent grade, or the percentage increase to the next size
0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
0.82 0.83 0.86 0.89 0.91 0.95 0.97 1.00 1.03
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Table III. Body dimensions, grades, and proportional grades of ASTM D5585-94. All dimensions are in inches
3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% Table IV. 3.0% Body dimensions, grades, and proportional grades of the linear optimized sizing system. All dimensions are in inches
calculated the same way for each sizing system, and are provided for ease of visualization of the three dimensions. The three dimensional plots show how the sizes in each system relate to the data cloud visually, and give some idea of the relationship between systems. However, as the plot can only show three dimensions and the optimized systems are based on four dimensions, these examples cannot show all visual
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Hip circumference (inches) Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
A1 35.86 0.93 2.6% 25.43 0.94 3.7% 30.88 0.20 0.6% B1 36.79 0.95 2.6% 26.37 0.97 3.7% 31.08 0.20 0.6% 332 C1 37.74 0.97 2.6% 27.34 1.00 3.7% 31.28 0.20 0.6% D1 38.71 1.00 2.6% 28.34 1.04 3.7% 31.48 0.21 0.7% E1 39.71 29.38 31.69 A2 37.13 1.17 3.2% 26.59 1.26 4.7% 29.96 0.30 1.0% B2 38.30 1.19 3.1% 27.85 1.31 4.7% 30.26 0.31 1.0% C2 39.49 1.24 3.1% 29.16 1.37 4.7% 30.57 0.31 1.0% Table V. Body dimensions, grades, D2 40.73 1.27 3.1% 30.53 1.43 4.7% 30.88 0.31 1.0% and proportional grades E2 42.00 31.96 31.19 of two tiered optimized Notes: sizing system. “Grade” is the amount that this dimension increases to the next size All dimensions are in “% gr.” is per cent grade, or the percentage increase to the next size inches
Uncon- Hip circumference strained (inches) size Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
A 35.86 1.09 3.0% 25.52 1.08 4.2% 30.57 1.14 3.7% B 36.95 0.74 2.0% 26.60 0.79 3.0% 31.71 –2.30 –7.3% C 37.69 0.47 1.2% 27.39 0.00 0.0% 29.41 0.95 3.2% D 38.16 0.98 2.6% 27.39 1.63 6.0% 30.36 0.62 2.0% E 39.14 –0.37 –0.9% 29.02 –0.69 –2.4% 30.98 1.88 6.1% F 38.77 0.59 1.5% 28.33 0.48 1.7% 32.86 –1.74 –5.3% G 39.36 0.18 0.5% 28.81 0.39 1.4% 31.12 –0.97 –3.1% H 39.54 1.10 2.8% 29.20 1.39 4.8% 30.15 0.65 2.2% Table VI. Body dimensions, grades, I 40.64 0.81 2.0% 30.59 0.76 2.5% 30.80 0.38 1.2% and proportional grades J 41.45 31.35 31.18 of the unconstrained optimized sizing system. Notes: “Grade” is the amount that this dimension increases to the next size All dimensions are in “% gr.” is per cent grade, or the percentage increase to the next size inches
Crotch length (inches) Value Grade %gr. 27.57 28.19 28.83 29.48 30.14 30.93 31.23 31.53 31.83 32.13
0.62 0.64 0.65 0.66
2.2% 2.3% 2.3% 2.2%
0.30 0.30 0.30 0.30
1.0% 1.0% 1.0% 0.9%
Crotch length (inches) Value Grade %gr. 27.44 29.25 28.94 31.30 28.60 29.91 30.54 32.47 30.71 33.08
1.81 6.6% –0.31 –1.1% 2.36 8.2% –2.70 –8.6% 1.31 4.6% 0.63 2.1% 1.93 6.3% –1.76 –5.4% 2.37 7.7%
information. This is true to an even greater extent for sizing systems based on more than four dimensions. A mathematical calculation of the aggregate loss of each of these systems to the reserved sample of 376 individuals was made to provide a basis of comparison of the systems. The total loss as described earlier is an aggregate measure of the discrepancy between each body measurement of each individual
Investigation of the structure of sizing 35
25
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Figure 1. Three-dimensional plot of ASTM D5585-94 by hip circumference, crotch length, and crotch height. All dimensions are in inches
45
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40 ircu 35 mf ere
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Figure 2. Three-dimensional plot of the linear optimized system by hip circumference, crotch length, and crotch height. All dimensions are in inches
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Figure 3. Three-dimensional plot of the two tiered optimized system by hip circumference, crotch length, and crotch height. All dimensions are in inches
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Figure 4. Three-dimensional plot of the unconstrained optimized system by hip circumference, crotch length, and crotch height. All dimensions are in inches
30
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and the corresponding measurement of their closest size. This aggregate loss can be interpreted as the average proportional difference between subjects and sizes. The aggregate loss of each system is reported in Table VII. The validity of this measure must be discovered by conducting an actual fit test of an optimized sizing system on a sample of individuals from the population; however, in the absence of validation this measure still provides a means of ranking the systems. According to the aggregate loss, all of the optimized systems are more successful than ASTM D5585-94. If the individuals in our control sample had to choose their best fitting size from the ASTM system, they would find on average that their measurements differed by a total of 4.8 per cent from the measurements that these garments were intended for. On the other hand, if they had to choose their best fitting size from any of the optimized systems then on average their measurements would only differ from the intended measurements by a total of 2.7 per cent to 2.9 per cent. It is clear that all of the optimized systems can outperform the ASTM system. From the 3D plot it is also clear that the ASTM system does not line up with the densest portion of the population distribution. This could be because ASTM D5585-94 is not designed for a military population. However it is not likely that the Misses civilian population is so different from the army sample. A more likely explanation is that ASTM D5585-94 is not based on anthropometric data describing the population, but rather on untested industry assumptions about the population. The linear optimized system is designed to have the same basic structure as ASTM 5585-94, but the sizes in this system are selected directly from the four relevant body dimensions. Within the constraint that keeps the system structure linear, the sizes are placed in the most efficient manner to accommodate the individuals in the sample. This is the least effective of the optimized systems based on aggregate loss but outperforms the ASTM system. The two tiered optimized system shows further improvement when measured by aggregate loss. The constraints used to create this system could provide a new way of identifying size categories, as it divides the sample into the groups that will form the most optimal linear systems within the sample as a whole. The best system based on the measure of aggregate loss is the unconstrained optimized system. This is understandable, as this system has no structural Sizing system
Measure of aggregate loss
ASTM D5585-94
0.04806
Linear optimized system
0.02908
Two tiered optimized system
0.02784
Optimized system with no constraints
0.02719
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Table VII. Comparison of the sizing systems by a measure of the aggregate loss of goodness-of-fit of the 376 individuals in the reserved sub-sample
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constraints on the process of locating sizes so that they provide the best fit possible for all of the individuals in the population. The comparison of the optimized systems reveals an additional explanation of why a system like the ASTM system can be outperformed: the structural rigidity imposed by a linear system. Each of the optimized systems is potentially a solution to a “nested” problem. The sizing system that fulfills the linear constraint could also fulfill the two tiered system constraint. If the smallest size in one tier of the two tiered system had turned out to be exactly one grade interval above the largest size of the other tier then the two tiers would form a linear system with a size break in the middle. By extension it can be seen that all sizing systems that fulfill any of the constraint specifications are also candidates for being the unconstrained optimum. The unconstrained optimum could have turned out to be a linear system with no size breaks. In fact, if the population exhibited very small proportional variability, then the optimal unconstrained system would be approximately linear. Therefore the shape of the population dictates the shape of the optimum sizing system. However, there are other issues that must be addressed in an unconstrained system, including issues related to production and distribution of garments. Pattern grading Garment patterns within a sizing system are developed from a single base pattern. The pattern from this base size is graded up and down to create the other sizes in the range. Grading is accomplished by moving each point on the perimeter of the pattern the amount needed to increase or decrease the pattern the desired amount. The movement of each point can be captured with two numbers from an x,y coordinate system. These numbers are the grade rules, which can be recorded and used to grade any pattern of a similar shape[9]. In the optimized sizing systems presented here the pattern of grade rules is not so simple. The grade increments for the optimized systems are percentage changes, and therefore the absolute differences between sizes are not constant. The grade rule in this case would consist of 27 numbers for each pattern point. Comparisons of different grade rules can be seen in Table VIII. Once a pattern is graded it is common practice to stack all of the patterns together into a graded nest in order to check the relationships between sizes. In a simple linear system, it is possible to see any errors in grading easily by checking this nest, as all of the pattern perimeters will be equidistant from one another. This will not be true of a graded nest of patterns from the optimized systems. For the constrained optimized systems the spaces between the pattern perimeters will increase for each size. For the unconstrained system there will not be a relationship between any of the pattern perimeters, and these lines may even cross one another as the proportions between sizes change. Figure 5 illustrates the differences between the various graded nests. If it were still the case that most pattern grading was done by hand, this complexity of grading would be extremely labor intensive, and therefore not worth any gains in the fit of the sizes generated. However, pattern grading in
Grade rule, inches no size breaks 0 –0.25
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Grade rule, inches multiple size breaks 4
0.13
–0.25
Number of breaks 0.13
8 –0.38
Size no., 1st break 0.13
10 –0.19 12
(a)
X, Y movement Size no., 3rd break
0.13 14
–0.31
337
X, Y movement Size no., 2nd break
0
–0.19
X, Y movement
X, Y movement Size no., 4th break
0.13
X, Y movement
(b)
Table VIII. Grade rules for the upper right corner of the patterns shown in Figure 5
Figure 5. Graded nest of a jacket front pattern: (a) shows an even grade with no breaks: (b) shows a grade with a break at each size
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the apparel industry is currently done almost exclusively on the computer, using computer-aided design systems that automatically generate the patterns from the grade rules. Therefore the creation of graded patterns for any of these sizing systems is technically possible. Once the grade rules are calculated they can be stored in the computer and applied to any pattern of a similar style.
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Size designation and selection An advantage of a standard linear sizing system is that size labeling and the choice of the appropriate size is a simple process, especially if hang tags list body measurements as specified in the international standard[19]. If a consumer tries on a garment and finds it is too large or small, she knows to try the size that is one up or one down from this size. However, in the unconstrained system the proportions change with every size instead of overall changes of largeness or smallness. The result is that the consumer would need to try on many more sizes, and would have a more complex problem choosing which sizes to try. The solution to this problem lies in the computer program that creates the optimized sizing systems. This same program could be modified for size selection. This is an ideal system for assigning sizes in mail-order operations as the sizes could be assigned at the warehouse directly from the customer’s relevant body measurements. New technologies in retail operations could also help with size assignment. Computers could be provided where the clothes are purchased that calculate the recommended size (the closest size according to the distance measures relevant for the particular garment) and any variations on this size (the second, third, etc. closest sizes). Distribution A further issue in retail operations is the selection of the appropriate numbers of each size to be sold at each store location. Currently orders for an appropriate set of garments, known as stock keeping units (SKUs), are based on what has sold in the past. Usually fewer garments are ordered in the smallest and the largest sizes. An advantage of the optimized sizes is that the number of individuals who fit in each size is more evenly distributed across the range of sizes. Table IX shows the number of individuals from the reserved sub-sample in each of the ten sizes for each of the sizing systems. Another advantage of the optimized systems is that if each retailer has an accurate anthropometric database of their customers it would be possible to calculate the number of the different sizes necessary to accommodate the population in their area. As the sizes are directly based on data from the anthropometric database, such predictions should be very accurate. Discussion and conclusions A set of three sizing systems were created based on anthropometric data using an optimization method for comparison to one another and to an existing system ASTM D5585-94. In order to make the comparison to the existing system as reasonable as possible and for clarity of illustration, some simplifications and concessions were made in creating these systems. Different
ASTM Linear Size Frequency Size
Two tier Frequency Size
Unconstrained Frequency Size Frequency
2
1
A
11
A1
28
A
45
4
10
B
27
B1
55
B
59
6
23
C
65
C1
60
C
43
8
64
D
82
D1
54
D
26
10
110
E
82
E1
36
E
33
12
89
F
50
A2
17
F
37
14
43
G
28
B2
35
G
40
16
25
H
18
C2
28
H
23
18
10
I
9
D2
33
I
30
20
1
J
4
E2
30
J
40
376
Total
376
Total
376
Total
376
Total
choices would be made to design sizing systems for use in the industry. However, from this example it is clear that these systems can be designed using different variables and different constraints to create sizing systems with different structures. The sizing systems demonstrated in this paper were created for comparison, and are not based on enough data to be reliable. Anthropometric studies of the population are needed to form the basis of this type of sizing system with enough data to calculate robust sizing systems. Such studies will be much easier than traditional anthropometry using new 3D scanning technologies being developed[20]. Only body measurements are discussed in this paper. The issues of ease and design of patterns must be addressed to complete the process and generate appropriate garment measurements. Other issues beyond the scope of this paper relate to individual preferences of fit and the impact on size selection and distribution of garments. Questions of grading methods, size designation and selection, and distribution will ultimately be important in selecting the structure that is most successful. If the development, distribution, and selection of a garment are not effective in terms of time or money, the improved fit may not provide enough of a benefit to justify the expense. However, in a situation where close fit is desired and there is a lot of variation in the population the optimized systems with no constraints provide a clear advantage in their ability to provide sizes that fit a range of body proportions. In this case, with the aid of computerized systems for grading, size selection, and distribution, the best choice may well be the least structured system. The most useful outcome of the method for creating optimized sizes is the creation and comparison of these different sizing structures as a means to
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Table IX. Frequencies of individuals in each of the sizing systems
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investigate the endogenous structures existing within the population. An example of this is the discovery of appropriate tiers within the population as seen in the simplified two tier optimized system. If the sample of 2,208 women representing the population as a whole were used, and a five tiered sizing system was created with a linear structure within each tier, the optimized equivalent of the Junior, Petite, Misses, Women’s and Plus Size sizing system shown in Table I could be created. In this case the size categories would be set based on the true variation in the sample, instead of the ad hoc proportional observations that form the basis of the current sizing. The optimized system would be based entirely on body measurements; however the system described in Table I also provides divisions that accommodate style variations. For example, a junior size is generally thought of as a size for young women with less full figures. Styles designed for this category are therefore generally less classic and trendier, as this type of style is more acceptable for the younger woman. Dividing the population on the basis of age, and then designing systems for each subset would provide a basis of comparison of different segments of the population. Given enough anthropometric data, the potential for designing an optimized sizing system for each situation is endless. Notes 1. Beatrix Paal, currently a doctoral candidate in the Department of Economics at Cornell University, performed the calculations and prepared the graphs and tables for this paper. The optimization method was developed by Beatrix Paal as a thesis project for her graduate work in the Department of Textiles and Apparel, “Creating efficient apparel sizing systems: an optimization approach”, Cornell University, 1997. 2. More precisely, let the distance function between the body measurements of the nth individual, xn , and sth prototype body size, ys be denoted by d(xn, ys ). For individual n, the only distance that is relevant to fit is the distance to the best (i.e. smallest distance) size in the sizing system, which is given by min d(x n, y s ). Our sizing systems only attempt s to accommodate those individuals who can be reasonably fitted by some size. Let cα be the cut off for the distance measure, beyond which an individual is judged not to be accommodated and define the loss function for individual n in a sizing system with sizes y1, . . . ys as
For a given number of sizes, S, and a given loss cutoff, cα , the optimal system is found by selecting the prototypes y 1, y 2, ..., y S so as to minimize the aggregate loss across all individuals, i.e.
Once the prototypes are derived, the individuals who cannot be accommodated can be found by calculating each individual’s distance to the closest prototype. Those closer to a prototype than cα are accommodated and those farther away are not. By varying cα we can achieve the desired accommodation rate.
References 1. LaBat, K.L. and Delong, M.R., “Body cathexis and satisfaction with fit of apparel”, Clothing and Textile Research Journal, 1990, Vol. 8 No. 2 (Winter), pp. 42-8. 2. Goldsberry, E., Shim, S. and Reich, N., “Women 55 years and older: overall satisfaction and dissatisfaction with the fit of ready-to-wear, part II”, Clothing and Textile Research Journal, Vol. 14 No. 2, 1996, pp. 121-31. 3. Workman, J.E., “Body measurement specifications for fit models as a factor in clothing size variation”, Clothing and Textile Research Journal, Vol. 10 No. 1, 1991, pp. 31-6. 4. Tamburrino, N., “Apparel sizing issues, part 1”, Bobbin, April 1992, pp. 44-7; “Apparel sizing issues, part 2”, Bobbin, May 1992, pp. 52-60; “Sized to sell”, Bobbin, June 1992, pp. 68-74. 5. Salusso-Deonier, C.J., DeLong, M.R., Martin, F.B. and Krohn, K.R., “A multivariate method of classifying body form variation for sizing women’s apparel”, Clothing and Textile Research Journal, Vol. 4 No. 1, pp. 38-45. 6. Gordon, C.C. and Friedl, K.E., “Anthropometry in the US armed forces”, in Ulijaszek, S.J. and Mascie-Taylor, C.G.N. (Eds), Anthropometry: The Individual and the Population, Cambridge University Press, Cambridge, 1990. 7. Jongsuk, C.Y. and Jasper, C.R., “Garment-sizing: an international comparison”, International Journal of Clothing Science and Technology, Vol. 5 No. 5, 1993, pp. 28-37. 8. Zangrillo, F.L., Fashion Design for the Plus-Size, Fairchild Publications, New York, 1990, pp. 173-4. 9. Price, J. and Zamkoff, B., Grading Techniques for Modern Design, 2nd edition, Fairchild Publications, New York, 1996. 10. Kidwell, C.B. and Christman, M.C., Suiting Everyone: The Democratization of America, Publication No. 5176. Smithsonian Institution Press, Washington, DC. 11. United States Department of Commerce, Body Measurements for the Sizing of Women’s Patterns and Apparel, A Recorded Voluntary Standard of the Trade CS 215-58, US Department of Commerce, Washington DC, 1958. 12. O’Brien, R. and Sheldon, W.C., Women’s Measurements for Garment and Pattern Construction, Miscellaneous Publication No. 454, US Department of Agriculture, Washington DC, 1941. 13. United States Department of Commerce, Body Measurements for the Sizing of Women’s Patterns and Apparel, Voluntary Product Standard PS 42-70, US Department of Commerce, Washington DC, 1970. 14. Roach, M., “The numbers game”, Vogue, August 1996, pp. 94-6. 15. American Society for Testing and Materials, Standard Table of Body Measurements for Adult Female Misses Figure Type, Sizes 2-20. D5585-94, ASTM, Philadelphia, PA, 1994. 16. Gordon, C.C., Bradtmiller, B., Clausner, C.E., McConville, J.T., Tebetts, I. and Walker, R.A., 1988 Anthropometric Survey of US Army Personnel, Technical Report NATICK/TR89/044, US Army Natick Research, Development, and Engineering Center, Natick, MA, 1989. 17. Paal, B., “Creating efficient apparel sizing systems: an optimization approach”, unpublished Master’s thesis, Cornell University, 1997. 18. McCulloch, C.E., Paal, B. and Ashdown, S.A., “An optimization approach to apparel sizing”, Journal of the Operational Research Society, Vol. 49, 1998, pp. 492-9. 19. Palaganas, D., “Uni-sizing Europe”, Apparel Industry Magazine, September 1991, pp. 82-4. 20. Parroty Interactive, “Cyberscope: scanners”, Newsweek, June 9, 1997, p. 12.
Investigation of the structure of sizing 341
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342 Received March 1998 Revised August 1998 Accepted August 1998
No-interval coherently phased product development model for apparel Traci May-Plumlee Department of Textile Products Design and Marketing, University of North Carolina at Greensboro, Greensboro, North Carolina, USA, and
Trevor J. Little Department of Textile and Apparel Management, College of Textiles, North Carolina State University, Raleigh, North Carolina, USA
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 342-364, © MCB University Press, 0955-6222
Introduction Apparel manufacturing firms have focused much effort in recent years on improved market responsiveness in a demand activated, customer driven and retail competitive business environment. One major thrust has been on quick response initiatives and the accompanying technologies needed to support the QR business strategy. As barriers to the adoption of quick response have been overcome, and the use of a QR business strategy has become more widespread, attention has turned to the need for improvement in other aspects of the apparel manufacturing business cycle. The literature clearly establishes that development of new products is a critical activity in a successful manufacturing firm (Bruce and Biemans, 1995; Craig and Hart, 1992). It is not surprising, therefore, that one of the business functions currently under scrutiny is the product development process for apparel. Emphasis on improving the product development process in apparel manufacturing firms is concentrating in two areas; improving the cost effectiveness of the process by streamlining and shortening the product development cycle, and improving the market receptiveness of new products through the addition of custom fit products and developing needed products close to the selling season. However, these efforts have been undertaken without benefit of in-depth, comprehensive documentation of the development process. This lack of documentation presents challenges in focusing improvement efforts and gauging their effectiveness. Examples of simple, generic models of the product development process used in the apparel industry can be found in the academic literature (Gaskill, 1992; Regan et al., 1998), trade literature (Fashion Apparel Manufacturing, 1982; Garfield, 1985; Marketing Committee, 1989; Sadd, 1996) and reference books (Burns and Bryant, 1997). Although these models are useful in achieving a fundamental understanding of the process, they provide an inadequate foundation for research efforts to redesign an improved process. This paper
presents a comprehensive model of the process of developing a line of apparel products based on the current practices found in the US apparel industry. Product development models Product development is defined as the design and engineering of products which are serviceable for the target consumer, marketable, manufacturable and profitable (Kunz, 1993). Clearly, integrated efforts of multiple functional units within a business firm must be employed to develop such a product and achieve these outcomes. Review of existing models for the new product development process illustrates the diversity of processes used in manufacturing industries, and provides a framework for understanding the apparel product development process. The product development literature identifies several forms that new products may take. Simply stated, new products may be: • “new to the world” inventions which create a new market; • modifications of existing products; and • existing products introduced to new markets. The new product development process models reviewed in this paper focus primarily on development of products which are new innovations or modifications of existing products. Strategies for introducing existing products to new markets focus on marketing innovation not product innovation and use a truncated model rather than an idea to implementation approach. Many authors have developed normative models of the new product development process. Traditionally, the process was represented by models that were sequential in nature, while more recent ones tend to reflect concurrent product development processes and integrate multiple business units. Sequential models for product development Figure 1 provides examples of sequential models of the new product development process. As is clear from the diagrams, this type of model suggests that a product moves sequentially through a series of defined stages. The processes occurring at each stage, often conceived as taking place in a single department, must be complete before passing the developing product forward to the next stage. Though these models may accurately depict the traditional product development process, they have several limitations in that they omit critical parts of the model needed to address a continuously changing marketplace. First, the product development literature acknowledges various initiating factors for new product development. Gruenwald found that customers, marketing, and research and development were the three most common sources of new product ideas (Gruenwald, 1992). Other authors emphasize the need for input from the consumer and for correlating that input with design objectives in the product development process (Erhorn and Stark, 1994; Himmelfarb, 1992).
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OPPORTUNITY IDENTIFICATION Market Definition Idea Generation No
344
Go DESIGN Consumer Measurement Perceptual Mapping Product Positioning Forecasting Sales Potential Product Engineering and Marketing Mix No Go TESTING Advertising and Product Testing Pretest Market Forecasting Test Marketing No
Figure 1. Sequential models by Urban and Hauser (1980), Gruenwald (1992) and Himmelfarb (1992)
Phase 1: Search for Opportunity Evaluate Go
No Go
Concept Idea Re-evaluate in future
No Go
Re-evaluate in future
Evaluate Go
Re-evaluate in future
Phase 4: Research & Development
Go INTRODUCTION Launch Planning Tracking the Launch
Evaluate Go
No Go PROFIT MANAGEMENT Decision Support System Market Response Analysis Innovation at Maturity Product Portfolio Management
Evaluate Go
No Go
Phase 6: Major Introduction
No Go
Evaluate Go
No Go
Manufacturing Re-evaluate in future
Evaluate Go
No Go
Pilot Production
Phase 5: Marketing Plan No Go
Evaluate Go
Engineering Design & Prototyping
Phase 3: Modelling (Prototypes) No Go
No Go
R&D determines feasibility
Phase 2: Conception Evaluate Go
Evaluate Go
Re-evaluate in future
Evaluate Go
No Go
Commercial Production
Harvest
However, in sequential models, input from marketing is only incorporated in later stages of the process as a means of testing new product concepts and developing marketing plans. Second, sequential models provide no means for efficient communication and movement backward as well as forward in the design and development process. In each model, gates between stages must be cleared to move forward in the process. If a gate is not cleared, the product is moved into an undefined state where it remains pending re-evaluation and possible recycling. This may result in dropping potentially successful products, in long development delays, or in a poor yield of successful new products. Third, the models describe a segmented, and consequently a slow approach, to product development minimizing interaction between departments. Because critical departments have no input until later stages of development, important considerations may be omitted from the process resulting in missed market opportunities, an extended development cycle, and difficult to manufacture products that are too costly and require major revision late in the development process. By the time a developing product has been reviewed by all departments, and perhaps recycled several times, the market opportunity identified at the beginning of the process may no longer exist. An alternative approach to modeling a sequential product development process is to describe it as simply ten to 13 activities in an ordered list. Cooper and Kleinschmidt (1986) used a model, shown in Figure 2, of 13 sequential activities. As shown in Figure 3, the authors found that only 1.9 per cent of the
Product development model
New Product Process Activities 1. Initial screening 2. Preliminary market assessment 3. Preliminary technical assessment 4. Detailed market study/ market research 5. Business/financial analysis 6. Product development 7. In-house product testing 8. Customer tests of product 9. Test market/trial sell 10. Trial production 11. Precommercialization business analysis
Number of Activities
345
Figure 2. Cooper and Kleinschmidt’s new product process activities (1986)
Proportion of Projects That Featured...
Only 1 activity
0%
2 activities
0.6%
3 activities
1.9%
4 activities
2.5%
5 activities
6.3%
6 activities
6.9%
7 activities
14.5%
8 activities
18.2%
9 activities
20.8%
10 activities
13.8%
11 activities
7.5%
12 activities
5.0%
All 13 activities
1.9%
Figure 3. Number of activities used in new product process (Cooper and Kleinschmidt, 1986) 0
5
10
15
20
25
manufacturing firms studied used all 13 activities in the product development process, and most used just eight or nine activities. Rochford and Rudelius (1992) found similar results using a 12 activity model. Mahajan and Wind (1992) found that even a model limited to ten activities did not accurately reflect the process followed in industry. Although these sequential activity models have
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proved useful in some instances, they are too restrictive to accurately depict traditional industry practices. The more general sequential models that focus on broadly defined process stages are more realistic representations. Parallel and concurrent product development models Due in part to the cited limitations, many authors view sequential processes as obsolete and see industrial product development shifting toward a parallel or concurrent product development process model (Himmelfarb, 1992; Nijssen et al., 1995; Zahra and Ellor, 1993). This process is often represented as sequential steps, but with each step occurring simultaneously in a number of departments. Operating under a concurrent model, communication is enhanced and the expertise of all departments is employed throughout the process. A shorter product development cycle, better products and improved communication between departments are identified as advantages of this approach. Erhorn and Stark (1994) modeled an integrated approach, shown in Figure 4, where product development occurs simultaneously in multiple departments and product improvements are accomplished without hindering the process. According to the authors, use of this model facilitates product innovation, cost management, meeting quality requirements and a shortened product development cycle. Barclay et al. (1995) also emphasized the importance of an integrated approach in discussing their wedge shaped concurrent product development model. It incorporates multiple new product options which are narrowed into a single new product concept through a series of decision points. Concurrent models offer advantages over sequential models of product development. First, the integrated approach supports a faster and more efficient product development cycle. Rosenthal (1992) illustrated this difference clearly in the diagram shown in Figure 5. Concurrent approaches allow for continuous exchange of information and responsibility among many departments involved
Marketing
Design Customer spec agreed
Industrial Engn.
Figure 4. Erhorn and Stark’s (1994) integrated process model
Production
Product defined
Design completed
Routine manufacture achieved
Target Market Date
Product development model
Conceptual design
Product design
Product engineering
347
Process engineering
Pilot development and testing
Volume production: Development and ramp-up
Time = Sequential (with some recycling)
= Simultaneous
in development. Second, marketing and manufacturing play a larger role in the entire product development cycle helping to assure that a wide variety of external and internal design requirements are considered in the process. It is notable, however, that these models do not provide for firms which outsource production, thus replacing the manufacturing function with sourcing as in private label apparel development. Multiple convergent product development models Hart and Baker (1994) attempted to limit the functional divisions suggested by “parallel” processes with a multiple convergent processing model. Like Barclay et al. (1995) Hart and Baker recognized that all tasks ideally converge ultimately into a single product launch. In their model, however, multiple convergences occur during the development phases that follow concept generation, development and screening. This is in contrast to Bruce and Biemans (1995) multiple convergent model diagramming the early stages of development. Bruce and Biemans (1995) model, shown in Figure 6, formally recognizes the numerous inputs to the product development process from varying sources. The role those same sources play in the continuing development of the product is also documented. This multiple convergent approach bears great similarity to the product development process used in the apparel industry. Other product development models and methods Saren’s (1994) model also builds on the concept of multiple evaluations, but also formally recognizes with shaded blocks those product development activities that may occur outside of the firm which will market the product. Saren’s “blocks” model, shown in Figure 7, provides for multidirectional movement through the process, allows for simultaneous activities and emphasizes the early stages of the new product development process.
Figure 5. Sequential versus simultaneous process (Rosenthal, 1992)
Changes to product lines
Specification of potentially required changes etc.
Development work on changes/new products required
Development of altered parts etc., if required
R & D projects (ongoing)
Feasibility studies Time projection(s) Initial specifications
Early design(s) Concept developed technically Cost of concepts
Physical product development
Figure 6. Bruce and Biemans’ multiple convergent processing model (1995) Suppliers
Preparation of marketing and launch plan
Convergent point: full business analysis
Convergent point: Concept evaluation and choice
Fuller market assessment Concept(s) introduced to market for evaluation Positioning of concept(s) Price indications
Convergent point: idea(s) evaluation
Estimations of market potential Comparison with competitors Initial financial assessments
Functional performance of product, collaboration on the development
Collaboration on concepts may be both technical and commercial
Modifications to ideas Preference inputs
Modifications to production process in light of development
Evaluation of the implications of alternative concepts in terms of resources and costs
Study of required alterations Study of resource implications
Process improvement projects
Specific demands Potential improvements
Competitor analysis Market trend forecasts etc.
Convergent point: idea generation
Manufacturing
Customers
Marketing
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R&D
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Stimulus Initial research
Search
1
349
Concept development 1 Technical/ commercial assessment
Proposal Business planning
Product testing 1
Market research 2
Product development 1
Manufacturing development Promotional/ advertising development
Distribution development
Figure 7. Saren’s (1994) blocks model of the product development process
2
New product
Cooper (1994) discussed limitations of stage-gate models that break the product development process into discrete stages each culminating in a gate where a decision to continue, abandon, hold or recycle the product idea is made. Products are prevented from moving into the next stage of development until the current stage is completed. Cooper’s solution is the Third Generation Process modeled in Figure 8. This process model represents a flexible process with overlapping stages and fuzzy gates that allow a product to advance to the next stage of development conditional on future completion of the activities in the current stage. Implementation of this model could shorten the development cycle by facilitating interaction between stages and eliminating delays at decision gates. Although the need to interface with marketing is emphasized, one of the limitations of the previous authors’ models is in recognizing the role of marketing and marketing research throughout the process. The multiple convergent model developed by Bruce and Biemans (1995) is a notable
Stage 1
Stage 2
Stage 3
Stage 4
etc.
Idea Prelim Investigation Gate 1
Gate 2
Business Case Gate 3
Development Gate 4
Test & Validate Gate 5
Figure 8. Cooper’s (1994) product development process model
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exception to this generalization. Quality Function Deployment (QFD), shown in Figure 9, is a method implemented to aid in developing marketable products with desirable attributes (Erhorn and Stark, 1994; Himmelfarb, 1992). QFD has been used by Japanese manufacturers of a variety of consumer goods including apparel. During QFD, customer expectations are identified and then translated into production process parameters through a series of matrices and tables. Companies credit the QFD process with reducing startup costs, pre-production costs and product changes late in the development cycle (Kinni, 1993). Griffin identified less concrete benefits, but acknowledged that the product development teams she studied had used QFD for a relatively short time, so long term benefits were unclear (Griffin, 1992). Craig and Hart (1992) concluded that study of the new product development process lacks the depth needed for critical evaluation and improvement. This general observation is equally true for the product development process used in the apparel industry as evidenced by the simplicity of examples found in the literature (Sadd, 1996) and reference books (Burns and Bryant, 1997). Yet, Sadd (1996) provided examples of how in-depth study of the product development process served to identify opportunities for increasing margins and sales volume, reducing lead and cycle time, and reducing product development costs. Comprehensive study of the apparel product development process has the potential for fostering competitive improvements in managing the process. Such improvements may result in greater return on product development investments, more consumer responsive products and a quicker development cycle.
Design Attributes Customer Needs Customer Perceptions “Engineering” Measures
Features Design Attributes
Operating Matrix Process Steps
House of Quality Measures
Control Matrix
Features
Design Matrix
Operational Conditions Measures Process Steps
Figure 9. QFD’s interaction matrices (Griffin, 1992)
Measures
Apparel development models Published product development models for apparel are of a sequential type; some defining the process in general stages and others using lists of activities. Models by Burns and Bryant (1997), Regan et al. (1998), and Sadd (1996) document apparel development as a series of stages in a linear progression, following the form of the traditional sequential model. In some cases, however, the authors state the process is not as sequential as it appears. Gaskill (1992) incorporates some concurrent activity in a few model stages, but provides no indication of whether that activity occurs in one or many functional units. Other authors represent the apparel development process as a more extensive list of activities completed sequentially (Garfield, 1985; Marketing Committee, 1989). Sometimes a timeline is added to the list of activities to create a merchandising calendar (Fashion Apparel Manufacturing, 1982). In addition to the previously identified limitations of sequential models – limited involvement of critical functional units, no capacity for backward movement or “line optimization” in the process, and inefficiency – these models lack the depth needed for critical analysis of the apparel development process. Current models do not permit process activities or phases to occur concurrently as in the apparel product development process. Cooper’s model allows for such activity, but is not specific to apparel. A model is needed that provides detail in terms of the activities undertaken to complete the development process, provides for concurrent activity and for the involvement of a variety of functional units in each stage of the process. The apparel product development process In addition to the above concerns, the apparel development process varies substantially from the processes used for developing other products and therefore requires a unique model to guide in-depth study. First, in the apparel industry products are developed in seasonal lines rather than as individual products. An apparel line may consist of many groups of products which must be managed simultaneously through the process. The normative models presented earlier in this paper depict the development process for individual rather than groups of related products. This observation suggests that the process for developing an apparel line could be represented by combining multiple smaller processes, each representing a single product. On examination, the process proves more complex; some decisions made during the product development process have implications for all products in the line, while others apply only to a limited number. For example, a line plan establishes parameters for all products to be included in a season’s line, but a color standard may apply to only a few. Recognizing that the outcome of apparel development is multiple related products rather than a single product emphasizes the complexity of the development process. In any given stage of the process, some products in the line may move forward in the process, others may recycle through previous phases and still others may be simply dropped from the line and archived for future reference.
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Second, several lines of new product must be produced annually. Consequently, while one line of product is being developed, the previous line may be in production and a third line may be selling at retail sales (Burns and Bryant, 1997, p. 132). If a company produces more lines each year, stages of the development process may overlap. This fact not only has implications for modeling the process, it highlights the importance of efforts to strategically manage and optimize it. In practice, the merchandising calendar is used to schedule the process. Currently, sales data for the previous season’s line is incomplete, so decisions must be made on new product without benefit of data regarding consumer acceptance of the previous products. In-depth study provides the opportunity to improve, streamline and thus better manage the process. Third, the strategy for developing any one product in the apparel line may differ from the strategy used in developing other products. A sequential model does not differentiate between strategies which may include original design, modification of existing products, knock-offs or take-offs, joint product development and combinations of these methods (Glock and Kunz, 1995). Of these original design, knock-offs or take-offs, and modification of existing products each follow a slightly different path through the product development process. As the last strategy suggests, development of a single line of apparel products may incorporate all of these strategies, different products following different paths. To further complicate matters, a firm may optimize a line which is currently being marketed by adding additional colors or fabrics. A product may be added to a line requiring accelerated development while the remainder of the line is far advanced in the process. Joint product development, a description not so much of how the development process is completed, but rather of who is completing particular phases, can follow any of the above identified paths. Current models are limited by a sequential nature that, though it may provide some sense of order, does not clarify the concurrent nature of some development activities nor the involvement of functional areas of the manufacturing firm. An in depth model of the apparel product development process must accomplish these things and be adaptable for: • developing both product lines and individual products; • development of seasonal lines and multiple seasons annually; • developing new products, take-offs, and modifying existing products. No-interval coherently phased product development model for apparel Typically, the development of new products is viewed as a design and development task. However, the responsibility for new products in apparel firms is coordinated and shared by four functional areas: (1) marketing; (2) merchandising;
(3) design and development; and (4) production. By defining the process in terms of functional divisions rather than departments, internal as well as joint product development strategies are provided for in the model. Figure 10 provides a comprehensive overview of the six phase apparel product development process model indicating the involvement of each of the four functional areas in each phase. The model integrates information from the literature, professional presentations, documentary videos and discussions with industry professionals (Burns and Bryant, 1997; Glock and Kunz, 1995; Kunz, 1993; Littrell, 1997; Magg, 1997). Like Cooper’s, this model incorporates overlapping stages and fuzzy gates which allow for various items within an apparel line to be selectively advanced or recycled through previous development phases. These coherently phased divisions are represented by broken lines between each phase of the process. The location of each gate represents the functional area having possession of the product line at that convergent point. System constraints may vary among product lines and firms, but include limitations such as vendor reliability, raw material availability and target retail price point. The product development process is also subject to constraints imposed by the consumer such as personal consumption expenditures, consumer wants, and consumer feedback which is influenced by a firm’s proximity to the consumer based on the channel of distribution. Figures 12-17 provide an in-depth examination of each phase of the development process. As is detailed in the following paragraphs, the model incorporates parallel processing and multiple convergent points identified as strengths of previous models. Figure 11 provides a legend to aid in model interpretation. Each shape and shading pattern used conveys something about the process depicted. Although each of the six phases is discussed in isolation, the discussion must be interpreted in the context of the complete model shown in Figure 10. The first phase, line planning and research, is shown in detail in Figure 12. Though the impetus for initiating development of apparel lines tends to be a seasonal merchandising calendar, many sources are used in arriving at the concept that will guide the process. Thus, this first phase incorporates the research and parameter establishment that will guide the development process. Marketing, merchandising and design all contribute to this research phase. Marketing provides the most general parameters for the process based upon a variety of research strategies. The marketing plan and sales forecasts developed at this stage provide financial and sales goals, and the financial information needed for planning the product line. Target customer research through focus groups and customer feedback via returns are connected to other areas of the model with dotted lines; an indication that not all firms conduct this research. Merchandising primarily uses this Phase 1 research in development of the line plan. Product development may provide input to the line plan, but also
Product development model 353
Figure 10. Apparel industry product development process overview
Phase I.
Line Plan
Preliminary Line
Often 1 year prior to consumer purchase
Research
Phase 2: Design/ Concept Development Line Adoption
Time
Phase 4: Marketing the Line
System Constraints
Phase 3: Design Development and Style Selection Modified Line
Phase 5: PreProduction
Final Line Phase 6: Line Optimization
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Production, Planning and Control
Merchandising
Planning Product Development Line and
Marketing
Production
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Product development model
Merchandising
Marketing
355
Production Planning and Control
Multiple Functional Units
Information Source
Decision Point
Documents/Products
Alternative Processes
Fuzzy Gate
Figure 11. Model legend
utilizes the information gathered in Phase 1 in formulating the creative direction of the line. So, for the current apparel product development process, research conducted by product development in Phase 1 initiates Phase 2, the design/concept development phase. Phase 1 converges into the line plan which establishes the specific parameters for the line under development, and may include some or all of the parameters identified in the model. Phase 2, shown in Figure 13, is the process of initiating development of specific products. The general line concept identified by the line plan is translated to specific color stories and concepts for the multiple product groupings which will compose the line. Often, product development initiates development of more product groupings than will be included in the final line. This allows for selective paring down of the line throughout the process. Following the color and concept meeting to review initial plans, some firms
Figure 12. Phase 1 Marketing
Marketing Plan
Feedback from Customers
Focus Groups Target Consumer Research
Sales Data From Previous Lines
Trade Associations
Sales Forecasts
Trade Literature
Market Research
Merchandising
Line Plan * Dollar Investment * Line Concept -Color palette -Materials -Styling * Weeks of Sale * Price Points * No. of Classifications * Size Ranges * Quality Level -Size Standards -Fit Standards * Model Stocks * Unit Plans
Merchandising and Product Development
Raw Material Vendors
Fabric & Trim Research
Print Forecasting Services
Color, Fabric and Style Direction
Color Forecasting Services
Color Research
Trend Research Retail Market Direction– Competitors Direction
Fashion Forecasting Services
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Market Research Firms
Phase 1: Line Planning and Research
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take their concept selections to the consumer for review. The mall intercept interview is a common strategy for conducting these concept tests. Approved concepts are then translated into design specifications and sketches. At the conclusion of Phase 2, a preliminary line represented by sketches and specifications is completed. Phase 3 translates the line from sketches and specifications to actual samples of the product line, as seen in Figure 14. Materials are evaluated and ordered to
Color Stories for Product Groupings
Concept Boards for Presentation
Color Boards for Presentation
Replace
Concept Development
Concept/Theme for Product Groupings Product Development
Creative Inspiration
Research from Phase 1
Merchandising
Line Plan * Dollar Investment * Line Concept -Color palette -Materials -Styling * Weeks of Sale * Price Points * No. of Classifications * Size Ranges * Quality Level -Size Standards -Fit Standards * Model Stocks * Unit Plans
Back to Phase 1
Not Approved
Color and Concept Meeting
Approved
Concept Tests
Marketing
Positive Outcome
Marketing, Merchandising and Product Development
Negative Outcome: Back to Concept Dev.
Color/ Concept Dropped
Don’t Replace
Trim Selection
Fabric Selection
Color Standard Selection Design Development
Design Sketches
Phase 2: Design/Concept Development
Archive for potential recycle into future product line
Product Development
* Design Specifications For Each Style
* Styles Selected by Design Team
Preliminary Line
Design Specifications
Approved
Design Review
Not Approved
Product Development
Product development model 357
construct the prototype of each design to be included in the line. Patterns are developed and fit standards finalized. Constructed prototypes are evaluated for fit using a fit model and in some cases provided to a consumer panel for wear testing. The prototypes are then reviewed by merchandising, marketing and product development culminating in final adoption of the line. Following Phase 3, the line is marketed to retail channels through markets and calls by sales representatives during Phase 4. This process, as shown in Figure 13. Phase 2
Materials Tests
Not Approved
Raw Materials Development
Not Approved
Develop Internally Produced Fabrics
Co-Develop Fabrics with Vendors
Back to design specifications
* Design Specifications For Each Style
* Styles Selected by Design Team
Preliminary Line
Product Development
Replace
Don’t Replace
Product Development
Approved
Wear testing
Sample Yardage, Trim and Findings Order
Prototypes (First Samples)
Fit and Style Evaluation
First Patterns
Fit Standards
Garment Development
Drop Style
Not Approved
Approved
Preliminary Costing
Revised Prototype
Not Approved
Approved
Sample Specifications
Merchandising
Revise Sizes/ Colors/Styles
Add Sizes/ Colors/Styles
Product Development, Merchandising and Marketing
Final Adoption * Sizes/styles/colors assigned to line plan * Assortment diversity/ volume/allocation determined * Prices established * Gross margin established
Approved
Replace
Not Approved Sales/ Merchandising Meeting
Drop Styles/ Colors/ Sizes
Don’t Replace
Phase 3: Design Development and Style Selection
358
Archive for future reference
Back to previous phases
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Figure 14. Phase 3
Figure 15, requires duplicating the prototype garments to provide samples for the sales representatives and detail costing to refine preliminary cost estimates. Based on response of buyers and retail accounts, the line may be modified. Phase 5, pre-production, involves translating the prototypes and first patterns in sample sizes into the complete size range required for sales to the consumer. As noted in Figure 16, this is accomplished through the grading process. Additionally, quality, production and process standards must be
Product Development
Order Duplicates
Order Fabrics for Sales Samples (Duplicates) Duplicates
Detail Costing
Product Development and/or Production, Planning and Control
Merchandising
Final Adoption * Sizes/styles/colors assigned to line plan * Assortment diversity/ volume/allocation determined * Prices established * Gross margin established
Back to previous phases
Archive for future reference Drop Styles/ Colors Sizes
Adequate
Sales Forecasts
Add Styles/ Colors/ Sizes
Back to previous phases
Merchandising and Marketing
Production Specification
Production, Planning and Control
Merchandising and Marketing
Promotional Materials for Sales Representatives
Merchandising
Fixturing for Market Space
Market Dispays
Sales Reps call on Sales Reps show Retail Accounts Line at Market(s)
Marketing
Review Retail Orders
Inadequate
Replace
Don’t Replace
Ahead to Phase 5
Merchandising
* Adopted Line Minus Dropped Pieces Plus Added Styles/Colors/Sizes
Modified Line
Phase 4: Marketing the Line
Product development model 359
finalized in preparation for manufacture. Sourcing and scheduling production according to sales forecasts generated by sales to retail is also completed. Figure 17 shows Phase 6, line optimization. In this phase, improvements are made to the line as orders continue and sales forecasts are modified. Modifications may be made to the line to enhance sales or to better balance a line which is having erratic sales. Phase 6 may be cycled through indefinitely as
Figure 15. Phase 4
Product Development
Production Patterns
Production Specifications from Phase 4
Figure 16. Phase 5 Final Garment Specifications
Merchandising
Size Specification Sheets Graded Patterns
Final Engineering Specification Verify Grading & Fit
Production Marker
Source Production
External Production Contract
Production, Planning and Control
Internal Production Schedule
Production Fabric, Trim and Findings Orders
Final Line * Quality Specifications * Material Specifications * Engineering/Production Specifications Merchandising and Production, Planning and Control
Ahead to Production
Ahead to Production
360
* Adopted Line Minus Dropped Pieces Plus Added Styles/Colors/Sizes
Modified Line
Phase 5: Pre-Production
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production continues, although the ideal is to have as little change as possible at that stage of development.
Conclusion Most previous work in conceptualizing and modeling the new product development process has been of a generic nature contributing much to the
Production
Back to Phase 5 Size Specifications
361
Add Styles/ Colors/ Sizes Adequate Orders
Drop Styles/ Colors/ Sizes Inadequate Orders Review Final Line Against Retail Orders
Merchandising
Don’t Replace
Replace
Add Style Archive for future reference
Merchandising and Production, Planning and Control
* Quality Specifications * Material Specifications * Engineering/Production Specifications
Final Line
Back to Phase 2 Design Development
Add Color
Add Size
Back to Phase 5 Production Fabric, Trim and Findings Orders
Back to Phase 2 Color Standard Selection
Quality and Volume Monitoring
Back to Phase 6 Line Optimization
Phase 6: Line Optimization
Product development model
Figure 17. Phase 6
structured understanding of the process, but little depth. These published models have been beneficial in conceptualizing major phases in the process, but do not provide the depth needed for optimizing the process used in a particular industry. This limitation also exists in works of those authors who have developed theoretical frameworks for the apparel design process applying existing design theory (Regan et al., 1998). The no-interval coherently phased product development (NICPPD) model for apparel integrates with previous research. It has been shown that the current practices and process of apparel product development map to the parallel
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processing, multiple convergent points and fuzzy gates identified as strengths of previous models. The main advantage of having a model of the apparel development process is to understand the critical convergent points, concurrent processes and porous phase boundaries. The NICPPD model provides an effective tool for intra-company to inter-business analysis of the apparel product development process. The NICPPD model allows the researcher to: • visualize the impact of changing business environment, processes, suppliers and customer requirements; • provide a context for integrating research projects; • identify opportunities and establish priorities for research; • clarify information flow providing a basis for establishing effective information technology. The NICPPD model allows the practitioner to: • benchmark and modify apparel development processes, such as those documented by Hardaker and Fozzard (1997) and Koh et al. (1997); • build the organizational structure required to effectively execute the apparel development process; • develop effective strategies for the rapid product development required for line optimization and market responsiveness; • strategically plan organizational and procedural changes to facilitate apparel development. As strategies such as automation and QR become standard means of doing business in the apparel industry, new opportunities must be sought to increase the competitiveness of firms in the industry. Optimization of the little studied and extremely important apparel product development process presents such an opportunity. Critical evaluation and improvement of the process can only be undertaken after a thorough understanding of current practices has been attained. This model provides the framework for such understanding, and a solid foundation for research to improve the process. References Barclay, I., Holroyd P. and Poolton, J. (1995), “The new product development process: a sphenomorphic management model”, International Journal of Vehicle Design: The Journal of the International Association for Vehicle Design, Vol. 16 No. 4/5, pp. 365-74. Bruce, M. and Biemans, W. (1995), Product Development: Meeting the Challenge of the DesignMarketing Interface, John Wiley & Sons Ltd, Chichester. Burns, L. and Bryant, N. (1997), The Business of Fashion: Designing, Manufacturing and Marketing, Fairchild Publications, New York, NY. Cooper, R. (1994), “Perspective: third generation new product processes”, Journal of Product Innovation Management, Vol. 11 No. 1, pp. 3-14. Cooper, R. and Kleinschmidt, E. (1986), “An investigation into the new product process: steps, deficiencies, and impact”, Journal of Product Innovation Management, Vol. 3 No. 2, pp. 71-85.
Craig, A. and Hart, S. (1992), “Where to now in new product development research?”, European Journal of Marketing, Vol. 26 No. 11, pp. 1-49. Erhorn, C. and Stark, J. (1994), Competing by Design: Creating Value and Market Advantage in New Product Development, Omneo, Essex Junction, VT. Fashion Apparel Manufacturing (1982), Report of the Technical Advisory Committee, American Apparel Manufacturers Association, Arlington, VA. Garfield, M. (1985, January), “What product? When? How many? Compressing cycle times”, Bobbin, Vol. 27 No. 5, pp. 99-103. Gaskill, L. (1992), “Toward a model of retail product development: a case study analysis”, Clothing and Textile Research Journal, Vol. 10 No. 4, pp. 17-24. Glock, R. and Kunz, G. (1995), Apparel Manufacturing: Sewn Product Analysis, Prentice-Hall, Englewood Cliffs, NJ. Griffin, A. (1992), “Evaluating QFD’s use in US firms as a process for developing products”, Journal of Product Innovation Management, Vol. 9 No. 3, pp. 171-87. Gruenwald, G. (1992), New Product Development, (2nd ed.), NTC Business Books, Lincolnwood, IL. Hardaker, C. and Fozzard, G. (1997), “The bra design process – a study of professional practice”, International Journal of Clothing Science and Technology, Vol. 9 No. 4, pp. 311-25. Hart, S. and Baker, M. (1994), “The multiple convergent processing model of new product development”, International Marketing Review, Vol. 11 No. 1, pp. 77-92. Himmelfarb, P. (1992), Survival of the Fittest: New Product Development in the ‘90s, Prentice-Hall, Englewood Cliffs, NJ. Kinni, T. (1993), “What’s QFD? Quality function deployment quietly celebrates its first decade in the US”, Industry Week, Vol. 242 No. 21, pp. 31-2. Koh, T., Lee, E. and Lee, Y. (1997), “An object-oriented model of apparel pattern making”, International Journal of Clothing Science and Technology, Vol. 9 No. 7, pp. 367-79. Kunz, G. (1993), Phases of Product Development: Preadoption and Postadoption[Video], Iowa State University Media Resources Center, Ames, IA. Littrell, J. (1997), “Design from merchandising to manufacturing”, Paper presented at the 47th Annual Conference of the American Society for Quality Control Textile and Needle Trades Division, Spring. Magg, T. (1997), Product Development Manager, Cross Creek Apparel, Inc., Mt. Airy, North Carolina, Personal interview conducted on site at Cross Creek Apparel, July. Mahajan, V. and Wind, J. (1992), “New product models: practice, shortcomings and desired improvements”, Journal of Product Innovation Management, Vol. 9 No. 2, pp. 128-39. Marketing Committee of the American Apparel Manufacturers’ Association (1989), “Merchandising calendar”, Apparel Manufacturer, Vol. 1 No. 2, p. 26. Nijssen, E., Arbouw, A. and Commandeur, H. (1995), “Accelerating new product development: a preliminary empirical test of a hierarchy of implementation”, The Journal of Product Innovation Management, Vol. 12 No. 2, pp. 99-109. Regan, C., Kincade, D. and Sheldon, G. (1998), “Applicability of the engineering design process theory in the apparel design process”, Clothing and Textiles Research Journal, Vol. 16 No. 1, pp. 36-46. Rochford, L. and Rudelius, W. (1992), “How involving more functional areas within a firm affects the new product process”, Journal of Product Innovation Management, Vol. 9 No. 4, pp. 287-99. Rosenthal, S. (1992), Effective Product Design and Development: How to Cut Lead Time and Increase Customer Satisfaction, Business One Irwin, Homewood, IL.
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Sadd, D. (1996), “Structuring product development for higher profits”, Bobbin, Vol. 38 No. 2, pp. 68-73. Saren, M. (1994). “Reframing the process of new product development: from a ‘stages’ model to a ‘blocks’ framework”, Journal of Marketing Management, Vol. 10 No. 7, pp. 633-43. Urban, G. and Hauser, J. (1980), Design and Marketing of New Products, Prentice-Hall, Inc., Englewood Cliffs, NJ. Zahra, S. and Ellor, D. (1993), “Accelerating new product development and successful market introduction”, S.A.M. Advanced Management Journal, Vol. 58 No. 1, pp. 9-15.
Locating defects on shirt collars using image processing Mustafa Al-Eidarous Department of Electronic Engineering, University of Hull, Hull, UK 1. Faults and inspection system 1.1 Shirt collar description The primary purpose of inspection of shirt collars is to avoid collars with faults, which might affect the saleability of the product. One of the advantages of using a modern Charge Coupled Device (CCD) image sensing device is coverage of a large area (Bradshaw, 1995), using certain edge detection or edge enhancement match filter techniques (Hill et al., 1983; Parui and Hashim, 1986). However most of these techniques cannot detect small defects and are affected by noise. Inspection is usually carried out on the cut pieces of cloth, before stitching, to avoid unnecessary assembly of faulty collars, and because of difficulty of relocating defects in rolls following inspection due to fabric stretch. Faults in shirt collars may be due to faults in the original cloth, or might arise during manufacturing. The shirt collar pieces are cut to the shape shown in Figure 1(a). Each fabric panel is divided lengthwise into two areas, the collar band region and collar region, along the folding line. First panel collar regions (seen during wearing of the garment): (1) Collar Point Left (CPL). (2) Collar Point Right (CPR). (3) Collar Back Centre (CBC). Second panel collar band regions (most exposed in presentation windowing): (1) Right Collar Band (RCB). (2) Left Collar Band (LCB). (3) Central Collar Band (CCB). The two fabric panels (plies) are sewn together on three sides as shown in Figure 1(b). The collar plies are sewn such that their inner sides are situated on the outside. Inverting or “turning” the collar right side out is done by folding the stitch line inside the collar pocket as shown in Figure 1(c) (Norton-Wayne, 1995; Paul et al., 1990; Taylor et al., 1990). The shirt collar is formed by sandwiching lining and stiffening layers between the top and bottom cloths and folding the final product along the folding line.
Locating defects on shirt collars
365 Received October 1997 Revised February 1998 Accepted March 1998
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 365-378, © MCB University Press, 0955-6222
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CBC
CPR
Collar Regions
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RCB
Collar Band Regions CCB
LCB
(a) Shirt collar panel Stitch line
Collar point
(b) Top view Collar pocket
Figure 1. Shirt collar panels (c) Oblique view
The two panel areas subdivide into regions of quality importance. The collar areas CPL, CPR and CBC are most exposed in presentation or windowing, whereas the areas RCB, LCB, and CCB are seen during the wearing of the garment. Faults in these areas must be avoided, and collar panels with faults in these areas are not suitable for use as the top ply. A high quality visual appearance is of importance in two stages: (1) the presentation of the product in the merchandising pack (known as windowing ); and (2) during the consumer use of the product. 1.2 Faults and their sizes The faults studied in this work are those found in a group of 22 rejected shirt collar panels of various mono-colours and from various production lines of one manufacturer. The fabrics of the panels were all of the same textural weave, i.e. the yarns woven as a rectangular interlaced grid. Since almost 75 per cent of the manufacturer’s total production is white and other mono-colours, the investigation was based on plain, mono-colour fabrics. The general classification of faults follows that of Taylor et al. (1988), which can be taken as typical of generally occurring faults for mono-coloured materials.
The fault types are: Locating defects on shirt collars • Thick weave: an extra piece of loose yarn trapped in the weave. • Slub flaw: a short thick place in the yarn where the fibres are not spun properly. • Knot: appears like a prominent knot or spot on the surface of the fabric. 367 • Mis-weave: caused by incorrect interlacing between weavers and wrappers, which leaves loose threads over the surface of the cloth. • Ladder: faults due to threads missing from the weft or warp. • Holes: gaps in the cloth larger than the usual spacing between the yarns. • Colour flaw: a short thick piece of a yarn of dissimilar colour trapped in the wrapper or weaver. • Foreign fibre: a foreign fibre, i.e. a strand or piece of fibre of dissimilar colour (usually black), dragged into the yarn during the spinning process. • Black mark: parts of the yarn, either wrapper or weaver. • Stains: marks caused by heavy contamination of fabric by oil or grease or other substances. Typical fault types were used in this research and two (mis-weave and ladder) were not available. The details of the fault types investigated are indicated in Table I, together with the sample coding used for the database information, and programming purposes. 1.3 Image capturing environment The CCD camera and back-light were used in this research. The captured image was digitised to 256 × 256 pixels with 256 grey level (zero representing black and 255 maximum brightness or white). Fault type
Width
Length
Number of samples
Sample coding
Thick weave
0.4mm
6mm+
9
ca1 . . . ca9
Slub flaw
0.40mm
5-50mm
2
cd2, cd3
Knot
1-2mm
any
1
cb1
Mis-weave
0.40mm
2-25mm
none
-
Ladder
several cm
several cm
none
-
Hole
any
any
1
ck2
Coloured flaw
any
0.40mm+
1
ce1
Foreign fibre
0.5mm
2
ch2, ch3
Black mark
any
any
6
cf1 . . . cf6
Stain
any
any
1
cj1
Table I. Fault types, size, number of samples, and sample coding
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Figure 2. Original image (ca) with low resolution
Figure 3. Original image (ca) with higher resolution
The camera focus was set at 5mm and aperture set at 5.6 for an optimum depth of field and suitable exposure. The area of the image was chosen to be 2.3in. by 3.7in. (58.42mm ( 93.98mm ) The field of view captured could be varied by altering the height of the camera. The height of the camera (lens to object distance) was 10in., giving an effective pixel area of (height × width) = 0.11mm × 0.18mm. Illumination is a key parameter affecting the image which directly affects the quality of the output data. It is necessary to customise the illumination for each application. The methods for industrial application can be back lighting , front lighting, structure lighting and strobe lighting. The specific source of lighting energy affects the amount of processing and the result achieved. In this application, back light was used where by the shirt collar was located between the light source and the camera. A frosted glass is normally placed over the light to produce a diffuse area emitter (Louis and Galbiati, 1990). Back lighting has the advantage that it produces high contrast images of the defect on the shirt collar. The high contrast minimises the image processing task and reduces sensitivity of the system to variations in the illumination source. An example of the original image with no defect, shown in Figure 2, is labelled ca. This is used as a reference image for other defects. Every defect type has a subgroup of actual defect images. The labels of the images within each subgroup are also shown in Table I. As an example, Figure 3 shows a thick
weave defect (ca1). Figure 4 shows the area of the image 1.0in. (0.5in. with Locating defects higher resolution to a level where the thread and the thick weave are very on shirt collars obvious. Figure 5 shows an example of an image with three lines, used in the signature counting method, section 2.3.2. 1.4 Inspection system A description of an integrated system for on-line quality inspection of a shirt collar is shown in Figure 6. In order to detect shirt collar defects, the system acquires static images of a moving object on a carrier using CCD cameras. Movements of the carrier may cause vibrations that will degrade the captured image which will affect the result. Several images of different portions of the object would enable parallel fault detection of the parts of the object which would speed the inspection, but several cameras connected to the system would be needed. For the time being only one camera is implemented. The PC system controls the image processing and finds the defect. When a fault is detected, the host computer interrupts the motor driver. The defective
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Figure 4. Thick-weave (ca1) with higher resolution
Figure 5. Image with three lines fault
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370 CCD Camera DC-Mc
moving object carrier
Figure 6. Structure of automated shirt collar inspection system
Rejected shirt collar
shirt collar is then removed from the carrier. This could be done with a robot or any pick and place device. 2. Detection methods 2.1 Statistical techniques Koshimizu and Aoi (1978) developed a statistical method for automatic fabric inspection, which attempts to reduce the amount of computation and data handling associated with statistical techniques by using grey level signatures for horizontal and vertical rows. Horizontal and vertical signatures work because most defects tend to be elongated longitudinally and laterally. The signature for a vertical column or horizontal row of an image is simply the sum of the grey levels in the pixels of the column or the row. Thus, the horizontal signature of row i is: (1) where i = 1, 2, ..., N, vertical signature of column j; (2) where j = 1, 2, ..., N.
The mean and standard deviations of these signatures are found in the usual Locating defects way; on shirt collars (3)
(4) with similar expressions for vertical mean and standard deviation. N is the sample size of the column or row in pixels, which in this application is 256. It is now assumed that the presence of a fault causes a significant deviation in the signatures of the columns or rows in which the fault occurs. 2.2 Control and sensitivity Control limit is very important in the grouping moving averaging method. The appropriate limits depend on the application. The designer must consider the sensitivity; k is the constant of sensitivity. This is usually different in plain fabric than wool fabric. Trial and error is the only way to judge the sensitivity. The constant of sensitivity on this shirt collar application was 2, so the operator used the software with the same control limit. Previous researchers (Bradshaw, 1995; Norton-Wayne, 1995) have suggested that a sensitivity constant of 1-2 is appropriate for similar types of application. The following control limits are defined: Vertical upper control limit VUCL = µv + kσ v Vertical lower control limit VLCL = µv – kσv Horizontal upper control limit HUCL = µh + kσh Horizontal lower control limit HLCL = µh – kσh where k is an adjustable control parameter. The faults are then detected if the signature for a column or row falls outside the respective control limits: v S j ≥ VUCL Light Value Fault in column j. v
S j ≤ VLCL Dark Value Fault in column j. h
S i ≥ HUCL Light Value Fault in row i. h
S i ≥ HUCL Dark Value Fault in row i. Using mean and standard deviation of the column or row signature led to the development and design of two novel approaches: moving group average, and moving divided group average.
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2.2.1 Moving group average A program was designed using mean and standard deviation of the column or row signature to allow the user to capture or load the image, and to select different k values and different group numbers of columns or rows. Calculating the moving average of groups, in each image there are 256 columns (rows). Each signature is the sum of 256 pixels. Grouping every n, n = 1, 2, ..., 256, columns (rows) and taking their average, gives 256/n groups. Each group can be moved by any number of steps m, m = 1, 2, ..., 256, then the next group average calculated, and so on. The result of this grouping and averaging will produce a graph. If the graph lies above or below or touching the two levels, an alarm will sound to void the defective fabric. Figure 7 shows the non-fault image (ca) with a graph not exceeding the upper or lower levels which means that there is no defect and no alarm will sound. Figure 8 shows the defect image (ca1), with a graph exceeding the upper or lower levels which means there is a defect and an alarm will give notice of it. 2.2.2 Moving divided group average The column or row is divided into a number of groups as a power of 2, then the moving average method explained previously is applied. The number of columns or rows times the number of steps should equal 256. The result is shown in Figures 9 and 10. Figure 9 shows the result of the nonfault image (ca), which does not exceed the upper or lower level, meaning there is no defect and no alarm. The graph is more precise than the result in Figure 7, because the graph in Figure 9 is elongated to 512 points since every column is divided into two groups. Figure 10 shows the result of the (ca1) image which exceeds the upper or lower level. This means there is a defect and an alarm will give notice of it. This graph gives a more precise result than Figure 8.
Figure 7. Moving group average of a non-defect (ca)
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Figure 8. Moving group average of a defect image (ca1)
Figure 9. Moving divided group average of a non-defect (ca)
Figure 10. Moving divided group average of a defect image (ca1)
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where N = 256, m is the step, and Xij is the pixel value at column j and row i. Figure 11 shows the software structure used during inspection of shirt collars. There are many methods to separate the defect from the background, e.g. filtering and edge detection, but most of these methods are not suitable for this application with the experiments done on the images. Hence, the following techniques were used: (1) Highlighting method. (2) Variance method. These are explained below. 2.2.3 Highlighting method The object is separated from the background by choosing a suitable threshold (T ) value where T = µ or T = µ + σ or the mode (the maximum or the minimum frequency). The best automated threshold value for this application is calculated from the grey level T hi = µhi + kσ hi (Bradshaw, 1995). The highlighting function is defined as follows: If Test > Threshold → Result = 256 If Sample < Threshold → Result = 1 Applying this method separated only 60 per cent of the defects from the background, so the variance filtering was used (Phillips, 1994). 2.2.4 Variance method This method is done by sliding a 3 × 3 window along whole the image. Any other window size can be used, but a 3 × 3 window is appropriate for this application because it gives the best result. The variance function replaces the Read the image, process and display Enter space and division or spread and step Loop starting position from 1 to 256 spread
Figure 11. Software structure used during inspection
Get mean standard deviation display for horizontal or for vertical
pixel in the centre of a 3 × 3 area, with the sum of squares of the differences Locating defects between the centre pixel and its eight neighbours. on shirt collars (6) where Xij is the pixel value at the ith row and jth column, Xc is the value of the centre pixel of the window. If variance value ≥ R (reference deviation) the pixel is set to white; otherwise, the pixel is set to black. Applying the variance method over the complete image separated 90 per cent of the defects from the background. Then the moving group average and signature counting method were applied (see sections 2.3.1 and 2.3.2).
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2.3 Detection methods with filtering The variance method was used on the defect image (ca1), to separate the defect from the background. Then, the moving group average method was used to show the shape of the defect. The vertical and horizontal signatures were counted to represent the defect number. The moving group average and signature counting methods will be described, then application to shirt collars discussed and the results presented in sections 2.3.1 and 2.3.2. 2.3.1 The moving group average The moving group average is applied on the signature S, mean µ and standard deviation σ. Figure 12 shows these three graphs. The upper graph represents µ + σ, the lower graph represents µ – σ, and the middle graph represents the mean under the image. The three graphs show the vertical signature and at
Figure 12. Filtered defected image (ca1) with moving group average on S, µ and σ
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the right of the image, they show the horizontal signature, thus clearly showing the defect and its shape. 2.3.2 Signature counting The signature of each column or row of the image is calculated, then the lines in the signature graph counted and compared with the reference (non-defect) to give an indication of the defect. To compare these numbers automatically, the following equation is used: (7) where Ns is the number of columns in the signature graph of the sample image, and N r is the number of columns in the signature graph of the reference image (non-defect). The similarity signature ratio value, Isr , must be zero. The mismatch must lie between 0 and 1, where 0 indicates a perfect match and any number between 0 and 1 a complete mismatch. The signature method, where it works, produces a significant false region. The variance filter shows the image of the thick weave fault (ca1), as in Figure 13, while Figure 14 is an example of an image (ca31) with three continuous straight lines which also shows significant false regions. These can be compared with the non-defect image shown in Figure 9. When we run the variance filter algorithm on a non-defect ca, no defect will be counted. Noise and discontinuity effects on the image will affect the result of the signature counting algorithm. Clearly, further processing and linking would be needed for a characterization of faults. It may be concluded that the variance filtering and signature counting method are suitable for this application.
Figure 13. Filtered defected image (ca1) with signature line graph
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Figure 14. Filtered three lines defect image with signature line graph
Table II shows the image type and the number of objects used to calculate the similarity signature ratio value, Isr. Figure 13 shows the graph of the defect shape of thick weave after using the variance filter on (ca1) image in Figure 3. Figure 14 shows the graph of the three line fault, after using the variance filter on (ca31) in Figure 5. After using the variance filter method, the size of the fault can be calculated but the noise must be removed to get the approximate size. 3. Conclusion The main reason for inspecting shirt collars instead of the fabric roll is the difficulty in relocating defects following inspection, due to fabric stretch. Further, machine vision roll inspection machines are very expensive. The results show that the statistical methods (moving group average and moving divided group average) can be applied to an automated inspection system to detect faults in shirt collars or in any mono-coloured woven fabric. The moving group average is beneficial (in maximising defect contrast) because defects rarely extend the full length of a row or column.
Image type
Number of objects
ca
0
ca1
1
ca31
3
Table II. Resulting number of defects on each image
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The novel techniques using variance filtering with the moving group average on S, µ and σ, or with the signature counting graph showed the fault and its shape very clearly. In both methods the operator can see the defect on the graph and an alarm sounds. This makes the system fully automated and the operator is in the position of an observer. Current work is concentrating on aspects of this general method for all defects, to separate the defect from the background. A CCD line scan camera, by which a large area can be captured, can be used. Defects from other manufacturers with different fabrics will be similar, but dimensions and rates of occurrence may vary. Additional types of faults will occur with patterned fabric and multicoloured fabric. It would be necessary to generalise the system and for each application to have its own library. Information about faults might be used for both quality control and process control. References Bradshaw, M. (1995), “The application of machine vision to the automated inspection of knitted fabrics”, Mechatronics, Vol. 5 No. 2/3, pp. 233-43. Hill, W.J., Norton-Wayne, L. and Finkelstein L. (1983), “Signal processing for automatic optical surface inspection of steel strip”, Trans. Inst. Meas. Control, Vol. 5 No. 3, pp. 137-54. Koshimizu, H. and Aoi, S. (1978), “Study of fabric inspection by computer image processing”, in Proc. 9th Conf. Image Eng. Japan, pp. 55-9. Louis, J. and Galbiati, J.R. (1990), Machine Vision and Digital Image Processing Fundamentals, Prentice-Hall, Englewood Cliffs, NJ. Norton-Wayne, L. (1995), “Automated garment inspection using machine vision”, IEEE Int. Conf. on Systems Engineering, Pittsburgh, ch.152, pp. 374-77. Parui, S.K. and Hashim, A.A. (1986), “Automatic defect detection in textile manufacturing”, Int. Conf. on Computer-Aided Production Engineering, Mechanical Engineering Publ. Ltd, Edmunds, UK, pp. 443-8. Paul, F.W., Torgerson, E., Avigdor, S., Cultice, D., Gopalswamy, A. and Subbarao, K. (1990), “A hierarchical system for robot-assisted shirt collar processing”, IEEE International Conf. on Systems Engineering, Pittsburgh, ch. 152, pp 378-82. Phillips, D. (1994), Image Processing in C, R&D Publication, Inc., Lawrence, KS. Taylor, G.E., Taylor, P.M., Zedeh, J.E. and Monkman, G. (1988), “Automated inspection of shirt collars”, Proc. Int. Conf. Robot Vision and Sensory Controls, pp. 281-91. Taylor, P.M., Wilkinson, A.J., Gibson, I., Gunner, M.B. and Palmer, G.S. (1990), “An integrated automated garment assembly system”, IEEE International Conference on Systems Engineering, Pittsburgh, ch. 152, pp. 383-6.
Anisotropy of fabrics and fusible interlinings
Anisotropy of fabrics and interlinings
C. Cassidy De Montfort University, Leicester, UK, and
S.V. Lomov St Petersburg State University of Technology and Design, Russia
379 Received April 1998 Revised July 1998 Accepted July 1998
Introduction The modelling of fabric, particularly woven fabric, as an orthotropic sheet is widely used in the 3D garment drape visualisation systems[1] and as a tool for prediction of fabric properties[2]. The following equations are used to predict the anisotropic linear elastic behaviour of fabric for in plane and bending deformation[3]: 1/Eθ = 1/E1cos4θ + 1/E2 sin4θ + (1/G-2/K) sin2θ cos2θ (1) 2 2 1/Gθ = 1/G cos 2θ + (1/E1 + 1/E2 + 2/K) sin 2θ (2) 1/K = µ 1/E1 = µ 2 /E2 Bθ = B1 cos4θ + B2 sin4θ + 2B* sin2θ θ cos2θ (3) Where E, G, B and µ designate Young’s modulus, shear modulus, bending stiffness and Poisson’s ratio respectively. Subscripts “1”, “2” and “θ ” – warp, weft and bias directions (θ is an angle relative to the warp direction) and; B* = 2τ + θ2 B1/2 where τ is twisting rigidity and σ – Poisson’s ratio for bending. The assumption made in equations (1-3) is that the linearity of tension, shear and bending, depends on the value of θ . However, the fabric behaviour is not linear, nor are the experimental anisotropy curves smooth. In one of the earliest studies of woven fabric anisotropy by B.P. Pozdnyakov[4] an unexplained significant decrease of fabric tensile resistance for θ = 15° and 75° was observed. Fabric properties are usually measured on KES-F or FAST systems using recommended parameters; if these data are to be fed into a garment CAD system, then the applicability of these measurements to the orthotropic description of the anisotropy of mechanical properties should be improved. The possible errors in predicting fabric in a deformed state with the orthotropic shell model were also discussed by Amirbayat and Hearle[5]. It is useful to compare the anisotropical mechanical behaviour of woven fabric with the behaviour of other sheet materials. A suitable choice of such The authors would like to thank Coats (UK), Leicester, and Lever Bros (UK), Port Sunlight for their help with this work.
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 379-390, © MCB University Press, 0955-6222
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comparative material is non-wovens (used in fusible interlinings) and fusible interlinings themselves. This would provide important information for the modelling of garments and an opportunity to study the effect of fabric anisotropy in composites. The mechanical properties of fusible interlinings were studied in several works[6-8]; however, the anisotropy of these properties was not fully investigated. The aim of the present paper is to study the feasibility of application of equations (1-3) to the behaviour of woven fabric and fusible interlinings using KES-F measurements. The accuracy of simple predictive models for the mechanical behaviour of textile composites used in other studies will also be investigated[6,8]. Experimental Samples The woven fabrics used to produce fusible interlinings samples (Table Ia) have two different patterns of anisotropy. Both fabrics are plain weave; however, Fabric A has the same yarn for warp and weft, whereas fabric B has warp and weft threads of different linear density. The fusible interlinings b,c and d (Table Ib) are produced from the same base viscose/polyester non-woven fabric. The interlinings were fused to the fabrics on a Reliant Rolamatic continuous fusing press at a recommended temperature of 140°C for 11 seconds at a pressure of 80lb/sq inch. The angle θ is measured relative to warp direction for woven fabrics and relative to the “machine” direction (primary fibre orientation) for nonwovens.
Fabric Weave Composition
Weight, g/m2
B
plain
plain
50% polyester 50% viscose
65% viscose 25% polyester 10% linen
207
198
Threads per cm
warp 17.6 weft 16.0
warp 32.0 weft 18.0
Yarn linear density (Tex)
warp 58.2 weft 59.4
warp 27.9 weft 51.6
Yarn linear density x sett, (Tex/cm) Table Ia. Woven fabrics table
A
Yarn twist (turns/m)
warp 1,024 weft 950
warp 893 weft 932
warp 520 weft 523
warp 733 weft 451
Adhesive
Method of applying adhesive
Weight
a
None
N/A
40g/sq m
b
*PA/PE
Scatter
51g/sq m
c
PE
Scatter
55g/sq m
d
PA
Dot
52g/sq m
Fusible interlining
Anisotropy of fabrics and interlinings 381
Note: * PA = Polyamide, PE = Polyethylene
Table Ib. Fusible interlinings
Results and discussion: bending Tables II and III show the results of bending properties measured using KES-F. Anisotropy trends are summarised in Figure 1. Hysteresis HB(θ ) is strongly correlated with B(θ ). The woven fabrics do not show any unexpected trends. Fabric A with its uniform structure shows a small anisotropy of bending stiffness; the anisotropy in fabric B is quite pronounced.
θ
a
Non-wovens b c
d
Fabric
Fabric B +b +c
+d
Fabric
Fabric A +b +c
+d
0 (warp)
0.15
0.083
0.075
0.16
0.094
0.70
0.71
0.77
0.092
0.78 0.88 0.73
30
0.23
0.16
0.14
0.24
0.070
0.78
0.90
0.83
0.087
0.92 1.07 0.91
45
0.30
0.22
0.22
0.30
0.057
0.84
0.90
1.00
0.085
1.19 1.27 1.10
60
0.31
0.25
0.27
0.35
0.046
1.02
1.10
1.15
0.095
1.12 1.35 1.20
90 (weft)
0.35
0.35
0.26
0.32
0.044
1.10
1.11
1.20
0.11
1.20 1.37 1.30
d
Fabric
Fabric B +b +c
+d
Fabric
θ
a
Non-wovens b c
Fabric A +b +c
+d
0 (warp)
0.093 0.051
0.058
0.097 0.057
0.59
0.55
0.64
0.060
0.61 0.62 0.60
30
0.17
0.085
0.10
0.16
0.044
0.58
0.72
0.62
0.063
0.68 0.72 0.71
45
0.27
0.15
0.19
0.29
0.036
0.58
0.62
0.75
0.065
0.73 0.65 0.86
60
0.38
0.19
0.27
0.42
0.029
0.65
90 (weft)
0.47
0.32
0.33
0.63
0.028
0.067 0.073
Table II. Bending rigidity B, gf cm
Table III. Bending hysteresis HB, gf cm
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B,gf cm
Fabric A +b,c,d
1
0.9
382
Fabric B+b,c,d
0.5 2 a,b,c,d 0.1 0.1 3
Fabric A
0.08 4
Fabric B
0.04
Figure 1. Anisotropy trends: bending
0 0
15
30
45
60
75
90 θ , grad
The values of bending rigidity of non-wovens with and without adhesive are shown to be close and there is no apparent increase in rigidity as a result of the addition of adhesive. However, there is a significant difference of bending rigidity between the two directions (B2 /B1≈ 3). Composite bending stiffness tests revealed a difference between the face and back of the composite. Figure 2 shows the bending diagram of “Fabric A + D” for θ = 90 (weft) and 45 degrees. Bending rigidities shown in Tables II and III are generated by KES-F system automatically and represent the average for these two directions. The trend of composite bending stiffness is determined by the non-woven stiffness; its value is close to the estimation computed with the theory of Kanayama and Niwa[8]. The anisotropy equation (3) describes the bending behaviour of fabrics, nonwovens and composites quite well. Let k be the coefficient relating B* and B1,B2: B* = k(B1 + B2 )/2. The anisotropy curves for fabrics are best fitted with k = 0.66; for non-wovens – with k = 1.4. Results and discussion: tension Tables IV and V show the results of the tensile measurements. The typical tensile diagram is shown on Figure 3. Linearity LT does not appear to depend on the direction of tension. The anisotropy trends are shown in Figure 4.
Anisotropy of fabrics and interlinings
M,gf cm/cm 90° 0.2 45°
0.1
383
0
–2
K,1/cm
2
–0.1
–0.2
Figure 2. “Fabric A + D”
Composite vs. components Data shown in Tables IV and V reveal some unexpected trends. First, deformation is higher for non-wovens with adhesive in comparison to the same non-woven without adhesive. If an adhesive bonds fibres within nonwoven fabric (the bonded regions can be considered rigid in comparison with “free” fabric), then only “free” regions will elongate under the applied load, and
a
Non-wovens b c
0.89
0.85
0.88
d 0.88
Fabric 0.75
Fabric B +b +c 0.90
0.93
+d
Fabric
+b
0.91
0.69
0.90
Fabric A +c 0.97
+d 0.94
Note: * Angular difference of LT is less then 0.02 for all samples
θ
a
Non-wovens b c
d
Fabric
Fabric B +b +c
+d
Fabric
Fabric A +b +c
Table IV. Linearity of tension diagram LT*
+d
0 (warp)
1.50
1.56
1.54
1.53
4.55
1.47
1.41
1.70
3.34
1.85 1.42 1.73
30
2.35
2.82
2.96
2.65
4.58
2.31
1.02
2.46
3.96
2.49 2.03 2.32
45
3.49
5.07
4.09
5.68
5.22
2.82
1.54
3.33
4.76
3.35 2.73 3.00
60
2.42
2.79
2.23
2.74
5.44
3.37
1.68
3.41
4.76
3.71 3.34 3.47
90 (weft)
1.97
2.04
2.05
2.12
3.20
3.07
3.00
3.05
4.07
3.68 3.71 3.55
Table V. Deformation EM, % at Fmax = 500 gf
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E gf/cm 30°
0° 400
45°
384
300
200
100
Figure 3. Typical tensile diagram
E 0
1
2
3
0 =4G 45 4
ε,%
average deformation of the sample should be lower than the deformation in a “free” fabric. This is not the case. Second, the addition of a fabric to non-woven does not necessarily lead to the increase of resistance to tension (for a weft direction there is a pronounced decrease of this resistance). Third, the shape of anisotropy curve can change as a result of bonding a nonwoven to a fabric. For non-wovens and fabrics alone these curves have a maximum near 45 degree direction; for fabric A with interlining, the maximum is shifted to the weft direction (note that bending stiffness behaves quite the opposite – see Figure 1). The possible explanation of these effects is the influence of bonding process on the mechanical properties of fibres within the non-woven layer. Anisotropy curves Strictly speaking, the orthotropic equation (1) cannot be applied to the data retrieved with standard KES-F measurements with Fmax = 500 gf/cm because of a non-linearity of the tensile diagrams. Nevertheless we shall check the applicability of equation (1) to our data to find whether it is possible to use the orthotropic model as an approximate description of anisotropy of fabrics for considerably large deformations. The orthotropic equation (1) should be fully justified for the description of initial tensile moduli; unfortunately, it is very difficult to evaluate the initial modulus of tension, especially for bias angles; therefore the application of the orthotropic model for them cannot be evaluated. Let us consider the equation (1) in the form valid for the linear behaviour of the material: (4) EMθ = EM1cos4θ + EM2sin4θ + EM* sin2θ cos2θ
where constant EM* can be computed from EM45 value: (5) EM* = 4EM45 – EM1 – EM2 Computing the value of EM* from equation (5), we can compare equation (4) with the experimental data for θ = 30 and 60 degrees – Figure 4. It can be noted that the theory agrees reasonably well with the experiment in the case of composite fabrics and can be considered as a very approximate evaluation for non-wovens and woven fabrics. This is quite natural: the less freedom of fibre
Anisotropy of fabrics and interlinings 385
EM,% 5 Key b c d
4 a 3 a
a
2
a
a 1
0
15
30
45
60
75
90 θ, grad
EM,% Key FabricB
5
Fabric A
4 B
A
3 2 1
0
15
30
45
60
75
90 θ, grad
EM,% Key 5
Fabric B +...
4
Fabric A +...
A
3
B
2 1
0
15
30
45
60
75
90 θ, grad
Figure 4. Anisotropy trends: tension
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movement in a material, the more close its behaviour is to the behaviour of a solid anisotropic plate. Results and discussion: shear Tables VI and VII show results of shear properties measurements. Anisotropy trends are summarised in Figure 5. There should be a symmetry of shear properties for angles θ and τ/2 – θ , so the data for θ = 30 and 60, 0 and 90 degrees is averaged on this figure. Note that equation (2) can be rewritten in a form (6) 1/Gθ = 1/G – ( 1/G – 1/G*) sin22θ where constant G* = G45. Fabrics There is an apparent difference between the observed shear behaviour of woven fabrics (Figure 5) and the behaviour predicted by Equation 6. There is an
θ
Table VI. Shear modulus G, gf/(cm grad)
Non-wovens b c
d
Fabric
Fabric B +b +c
+d
Fabric
Fabric A +b +c
+d
0 (warp)
7.49
5.75
6.91
8.52
0.68
10.8
11.9
11.7
0.76
12.5
11.6 11.8
30
5.59
5.64
6.25
6.48
3.45
11.5
12.1
11.9
5.56
11.6
11.1 12.5
45
6.52
6.15
6.78
7.44
4.35
10.7
11.1
11.0
3.42
11.3
9.53 11.2
60
6.64
6.19
6.69
6.56
6.56
12.3
12.0
11.3
6.36
11.2
11.6 11.6
90 (weft)
7.70
7.40
7.95
8.36
0.54
12.0
10.9
12.4
0.74
11.9
11.4 12.1
d
Fabric
Fabric B +b +c
+d
θ
Table VII. Shear hysteresis HG/ HG5, gf/cm
a
a
Non-wovens b c
Fabric
Fabric A +b +c
+d
0 (warp)
24.0 17.4
19.7 13.8
15.4 14.9
16.6 16.5
0.68 1.40
28.9 25.2
27.0 25.7
27.0 24.1
1.06 2.91
26.4 28.1
42.7 28.9 35.8 27.3
30
32.8 21.2
24.9 16.2
24.9 17.4
28.5 19.0
7.34 3.66
27.1 23.6
30.2 25.6
22.8 21.2
5.92 4.66
27.3 25.7
39.7 28.4 32.9 25.4
45
29.3 19.2
20.5 15.4
20.2 15.6
21.2 16.5
3.29 3.89
29.1 24.6
30.7 25.1
24.5 21.5
6.83 4.02
32.1 27.0
51.4 34.8 34.3 24.8
60
28.3 19.1
19.7 14.2
14.1 13.9
23.8 16.7
3.71 3.64
25.4 25.5
36.2 30.2
31.6 25.7
5.17 4.67
38.7 30.3
43.1 31.8 32.4 26.8
90 (weft)
28.6 20.4
23.6 16.5
23.1 16.8
24.4 17.8
0.33 1.11
26.6 28.1
30.2 27.3
24.7 26.6
0.96 2.86
25.6 27.7
38.9 27.4 32.6 27.8
Anisotropy of fabrics and interlinings
G, gf cm grad Fabric b,c,d 10
387 8
a
a
c b
a
a
6
a
Key 4
Fabric A Fabric B
2
0
15
30
45
60
75
Figure 5. Anisotropy trends: shear
90 θ, grad
unexpected local maximum of shear resistance for θ = 30 and 60 degrees. The possible explanation for this possibly lies in the bilinear shear behaviour of fabrics. The effective shear force in warp and weft directions is T12 = Tcos2θ, where T is shear force in direction θ . For the directions θ close to 45 degrees T12 is small, so the fabric resists shear in the initial region of the shear diagram, where this resistance is highest. To develop a qualitative description let us consider the “rigid-linear” shear behaviour of a fabric and linear tension behaviour. In this case tension deformation ε and tension force P in the warp and weft directions, shear angle and shear force T will be linked with the following equations : ε1 = P1 /E1 – µ P2 /E2 (7) ε2 = P2 /E2 – µ P1/E1 0, T < HG/ 2 12 Γ (T12 ) γ12 = (T – HG/ 2) / GT > HG/2 = 12 where E is Young modulus and HG is the shear hysteresis.
(8)
IJCST 10,5
388
For shear in the direction θ with a shear angle γ and shear force T the transformation formulae for components of deformation and stress tensors [9] are : P1 = T sin2θ, P 2 = –T sin2θ, T12 = T cos2θ (9) γ = γ12 cos2θ + (ε1 – ε2 )sin2θ Combining equations (7), (8) and (9), the equation for the dependence of γ on T is obtained: γ = Γ (T cos2θ ) cos2θ + T/k sin22θ (10) where k = 1/E1 + 1/E2 + 2/K = G45 For a given G, HG and G45 equation (10) gives a qualitative description of a fabric behaviour in shear in the direction θ . Table VIII shows initial slopes of γ (T) curves computed from equation (10) for Fabric A and Fabric B with the parameters from Tables VI and VII. The ratio of initial slopes for θ = 30 and 45 degrees is about 1.3 for both fabrics and reasonably agrees with the observed values of G30 /G45 –1.64 and 1.15 respectively. For non-woven and composite fabrics this effect is shaded by the larger value of k = G45 in equation (10). Non-wovens and composite fabrics The addition of glue to non-wovens alters the shear rigidity only slightly (see Tables VI, VII, VIII and Figure 5). Anisotropy curves for non-wovens and composite fabrics correspond to equation (6) (Figure 5). The ratio G0 /G45>1 for composite fabrics. However, the angular variation of shear is feeble (less than 20 per cent for non-wovens and less than 10 per cent for composites). Relationship between tensile and shear parameters The last test for the anisotropy model – the compatibility of shear and tensile behaviour predicted by equations (1) and (2) – cannot be conducted directly with the KES-F data because the relationship between tensile and shear moduli suggested by equations (1) and (2) is valid only for their initial values. The initial value of E45 can be evaluated from equations (1) and (2) as; 0 E 45 ≈ 4G (11)
Fabric Table VIII. Estimated shear rigidity for θ = 30°
A B
G30, gf/(cm grad) estimated
G45, gf/(cm grad)
4.54 5.88
3.42 4.35
(with G