Pressure therapy is generally accepted as an effective means of preventing and controlling hypertrophic scarring after burn injury. Pressure treatment based principally on the use…
Abstract
Pressure therapy is generally accepted as an effective means of preventing and controlling hypertrophic scarring after burn injury. Pressure treatment based principally on the use of pressure garments is widely used in Hong Kong and many other countries. These garments are tailor‐made to the individual patient's measurement to provide a uniform and firm support to body contours, and they are designed individually for the area of injury. Attempts to review the existing practice of the various kinds of pressure garments on patients, and to provide a better understanding of the present use of fabric and production methods employed in the manufacturing of the garments. Includes a brief account of the problems encountered by both the patients and the medical staff.
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C.L. Hui Patrick, S.F. Ng Frency and C.C. Chan Keith
In the process of fabric spreading, the variance of fabric yardage between fabric rolls may lead to a difference in fabric loss during spreading. As there are numerous…
Abstract
In the process of fabric spreading, the variance of fabric yardage between fabric rolls may lead to a difference in fabric loss during spreading. As there are numerous combinations the arrangement of the fabric roll sequences for each cutting lay, it is difficult to construct a roll planning to minimise the fabric wastage during spreading in apparel manufacturing. Recent advances in computing technology, especially in the area of computational intelligence, can be used to handle this problem. Among the different computational intelligence techniques, genetic algorithms (GA) are particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. This paper presents the details of GA and explains how the problem of roll planning can be formulated for GA to solve. The result of the study shows that an optimal roll planning can be worked out by using GA approach. It is possible to save a considerable amount of fabric when the best roll planning is used for the production.
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Keith C.C. Chan, Patrick C.L. Hui, K.W. Yeung and Frency S.F. Ng
Assembly line balancing problems that occur in real world situations are dynamic and are fraught with various sources of uncertainties such as the performance of workers and the…
Abstract
Assembly line balancing problems that occur in real world situations are dynamic and are fraught with various sources of uncertainties such as the performance of workers and the breakdown of machinery. This is especially true in the clothing industry. The problem cannot normally be solved deterministically using existing techniques. Recent advances in computing technology, especially in the area of computational intelligence, however, can be used to alleviate this problem. For example, some techniques in this area can be used to restrict the search space in a combinatorial problem, thus opening up the possibility of obtaining better results. Among the different computational intelligence techniques, genetic algorithms (GA) is particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. In this paper, we present the details of a GA and discuss the main characteristics of an assembly line balancing problem that is typical in the clothing industry. We explain how such problems can be formulated for genetic algorithms to solve. To evaluate the appropriateness of the technique, we have carried out some experiments. Our results show that the GA approach performs much better than the use of a greedy algorithm, which is used by many factory supervisors to tackle the assembly line balancing problem.
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Patrick C.L. Hui, Keith C.C. Chan, K.W. Yeung and Frency S.F. Ng
This paper aims to investigate the use of artificial neural networks (ANN) to predict the sewing performance of fabrics. The purpose of this study is to verify the ANN techniques…
Abstract
Purpose
This paper aims to investigate the use of artificial neural networks (ANN) to predict the sewing performance of fabrics. The purpose of this study is to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics.
Design/methodology/approach
In order to verify the ANN techniques that could be emulated as human decision in the prediction of sewing performance of fabrics, 109 data sets of fabrics were tested by using fabric assurance by simple testing system and the sewing performance of each fabric's specimen was assessed by the domain experts. Of these 109 input‐output data pairs, 94 were used to train the proposed backpropagation (BP) neural network for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed BP neural network.
Findings
After 10,000 iterations of training of BP neural network, the neural network converged to the minimum error level. The experimental results reveal the great potential of the proposed approach in predicting the sewing performance of fabrics for apparel production.
Originality/value
Generally, the fabric's performance in the manufacturing process is judged subjectively by the operators and/or their supervisors. Current methodologies of acquiring fabric property information and predicting fabric sewing performance are still incapable of providing a means for efficient planning and control for the sewing operation. Further, development of techniques to predict the sewing performance of fabric is essential for the current apparel production environment. In this paper, the use of ANN to predict the sewing performance of fabrics in garment manufacturing is investigated.
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Sinem Gunesoglu and Binnaz Meric
The aim of this paper is to study the operator activities in garment industry and the percentages of distribution of operations and to analyze the personal and delay allowances by…
Abstract
Purpose
The aim of this paper is to study the operator activities in garment industry and the percentages of distribution of operations and to analyze the personal and delay allowances by observing the operations and deriving the ratios within a manufacturing period.
Design/methodology/approach
A work sampling technique is used. Relevant reports (1978‐2004) are studied to give the basis and methodology of the technique. In accordiance with work sampling techique, the operations to be observed in a sewing room are defined, the number of observations and observers required for each day and the procedure for making observations are determined and the distributions of work flows are calculated.
Findings
It is found that 72.7 per cent of working time in an general sewing room was spent for productive activities and 23.2 per cent for personal and unavoidable delay allowances.
Practical implications
Work sampling technique gives information about personal and delay allowances in a work flow of any sewing room. When the distributions of activites are determined, it is possible to find which activities are most responsible for low efficiency. For this purpose, standard operations time in a sewing room should be determined by time measurement studies and work flow should be organized.
Originality/value
This paper deals with an actual sewing room and gives general information about the distributions of activites in work flow which should be used for organization of any sewing room.
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Anirban Dutta and Biswapati Chatterjee
The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for…
Abstract
Purpose
The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.
Design/methodology/approach
Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.
Findings
It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.
Research limitations/implications
This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.
Practical implications
The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.
Originality/value
This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.
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Yu-Chung Tsao, Chia-Chen Liu, Pin-Ru Chen and Thuy-Linh Vu
In recent years, the demand for garments has significantly increased, requiring manufacturers to speed up their production to attract customers. Cut order planning (COP) is one of…
Abstract
Purpose
In recent years, the demand for garments has significantly increased, requiring manufacturers to speed up their production to attract customers. Cut order planning (COP) is one of the most important processes in the apparel manufacturing industry. The appropriate stencil arrangement can reduce costs and fabric waste. The COP problem focuses on determining the size combination for a pattern, which is determined by the length of the cutting table, width, demand order, and height of the cutting equipment.
Design/methodology/approach
This study proposes new heuristics: genetic algorithm (GA), symbiotic organism search, and divide-and-search-based Lite heuristic and a One-by-One (ObO) heuristic to address the COP problem. The objective of the COP problem is to determine the optimal combination of stencils to meet demand requirements and minimize the total fabric length.
Findings
A comparison between our proposed heuristics and other simulated annealing and GA-based heuristics, and a hybrid approach (conventional algorithm + GA) was conducted to demonstrate the effectiveness and efficiency of the proposed heuristics. The test results show that the ObO heuristic can significantly improve the solution efficiency and find the near optimal solution for extreme demands.
Originality/value
This paper proposes a new heuristic, the One-by-One (ObO) heuristic, to solve the COP problem. The results show that the proposed approaches overcome the long operation time required to determine the fitting arrangement of stencils. In particular, our proposed ObO heuristic can significantly improve the solution efficiency, i.e. finding the near optimal solution for extreme demands within a very short time.
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The pressure on body scars resulting from pressure garments gradually decreases with time, probably as a consequence of stress relaxation in the fabric material. In order to…
Abstract
The pressure on body scars resulting from pressure garments gradually decreases with time, probably as a consequence of stress relaxation in the fabric material. In order to understand the basic mechanisms contributing to this phenomenon, an understanding of the stress relaxation behaviour of relevant textile structures is vitally important. Makes comparisons of the stress relaxation and shrinkage properties on six selected fabric samples which are all currently used by the hospitals in the UK and/or in Hong Kong. The design of the stress relaxation tests was based on the consideration of the fabric from a performance‐in‐use standpoint. The results of the tests showed differences between the fabrics collected in the UK and Hong Kong. It was also found that the stress relaxation in the wale direction was considerably lower (resulting in better performance) than in the course direction. It may be noted that the shrinkage of the fabrics was closely related to the degree of elasticity loss during washing.
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Sau Fun Frency Ng and Chi Leung Patrick Hui
Pressure garments are mainly made of elastic Lycra fabrics and tailor‐made to individual patients’ measurements to provide an appropriate amount of skin‐garment interface pressure…
Abstract
Pressure garments are mainly made of elastic Lycra fabrics and tailor‐made to individual patients’ measurements to provide an appropriate amount of skin‐garment interface pressure for burn rehabilitation. However, the fabric tension would be different at various locations from the hem edges of pressure garments, and thus the skin‐garment interface pressure cannot be uniformly maintained over the interface surface. Aims to investigate the pattern of interface pressure changes caused by the different types of edge finish used for making pressure garments. The effect of garment sizes on the change of interface pressure was also examined. Experiments were carried out using two selected elastic Lycra fabrics, four types of hem finish and three different garment sizes. The results of the study provide a guideline for designing the edge finish of pressure garments, and a minimum margin from the hem edges of garments to the scar area is also recommended.