• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.372-378
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• 2015

In labor-intensive enterprise, such as garment factory, assembly line is widely used as a manufacturing process for reducing costs and production time. However, for the sake of the various working capacity of worker, idle or delay may happen and influence the rear processes. If these unforeseeable delay happened continuously, it may influence the whole manufacturing process and a model, which is called limited-cycle model with multiple periods (LCMwMP), is assumed to evaluate the influence risk. In order to minimize the risk, the assignment of the workers is focused on. In this paper, we deal with an assembly line as LCMwMP model when two kinds of workers exist, whose efficiency is assumed to two different groups. We consider an optimization problem for finding an assignment of workers to the line that minimizes total expected risk, which is exchanged to expected cost by reset model of LCMwMP. First, reset model as a simple model of LCMwMP is introduced. Then, some hypotheses of the rules of the optimal worker assignment are proposed and some numerical experiments are researched assuming the processing time as Erlang distribution. Finally, the other rules on other certain conditions are discussed.

• Journal of the Korean Society of Clothing and Textiles
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• v.39 no.6
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• pp.877-884
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• 2015

Polytrimethylene Terephthalate (PTT) is an eco-fiber with good elastic properties; however, it requires more detailed studies related to spinnability according to blending of various kinds of fibers. The evolution of spinning technology was focused on improved productivity with good quality; in addition, air vortex spinning was recently invented and applied on the spinning factory as the facility with good productivity and quality. More detail spinning technology according to the blending of various kinds of fibers on the air vortex spinning system is required to obtain good quality yarns for high emotional fabrics. In this paper, the physical properties of air vortex, compact and ring staple yarns using PTT/wool/modal blend fibers were investigated with yarn structure to promote high functional PTT that includes fabrics for high emotional garments. Unevenness of air vortex yarns was higher than those of compact and ring yarns; in addition, imperfections were greater than those of compact and ring yarns, which was attributed to a fascinated vortex yarn structure. Tenacity and breaking strain of air vortex yarns were lower than those of compact and ring yarns, caused by higher unevenness and more imperfections of air vortex yarns compared to compact and ring yarns. Vortex yarns showed the highest initial modulus and ring yarns showed the lowest ones which results in a stiff tactile feeling of air vortex yarns in regards to the initial modulus of yarns. Dry and wet thermal shrinkages of air vortex yarns were lower than ring yarns. Good shape retention of vortex yarns was estimated due to low thermal shrinkage.