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Influence of Woven Fabric Width and Human Body Types on the Fabric Efficiencies in the Apparel Manufacturing

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the apparel manufacturing, the fabric is the single largest element in the cost of the garment. Therefore, effectual fabric consumption causes a reduction in cost and exertions. The purpose of this research is to study the effects of fabric width on the efficiency of marker (cutting) plans. Fabric consumption is in four types for human body shapes, that is, triangle, oval, square, and circle, in both genders to control the fabric utilization. Two clothing styles, fitted trousers and fitted shirts, are manufactured in an apparel manufacturing industry. The marker plans produced through Garment Gerber Technology software are accomplished in 36 different fabric widths (independent variables). The evaluation of dependent variables, that is, marker efficiency, marker loss, and fabric consumption efficiency relevant to four body shapes in variable fabric widths is analyzed for both women and men. The statistical analysis indicates that there is a linear relationship between marker efficiency and fabric width (sig <0.05). The regression analysis (p-value) between dependent variables and predictor variables (body types and fabric width) is also statistically significant. Also, the result implies that markers are more productive with larger fabric widths in all styles in both genders.
Rocznik
Strony
484--496
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
  • College of Textiles, Donghua University, Shanghai 201620, China
  • Department of Textile Engineering & Technology, University of the Punjab, Lahore 54590, Pakistan
autor
  • College of Textiles, Donghua University, Shanghai 201620, China
  • Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai 201620, China
autor
  • College of Textiles, Donghua University, Shanghai 201620, China
  • Key Laboratory of Eco-textiles, Jiangnan University, Wuxi 214122, China
autor
  • College of Textiles, Donghua University, Shanghai 201620, China
autor
  • College of Textiles, Donghua University, Shanghai 201620, China
autor
  • College of Textiles, Donghua University, Shanghai 201620, China
  • College of Textiles, Donghua University, Shanghai 201620, China
Bibliografia
  • [1] Glock, R. E., Kunz, G. I. (2005). Apparel manufacturing: Sewn product analysis, 4th ed., (Prentice Hall, Upper Saddle River, NJ), 173.
  • [2] Wong, W. K., Leung, S. Y. S., Au, K. F. (2005). Real-time GA-based rescheduling approach for the pre-sewing stage of an apparel manufacturing process. The International Journal of Advanced Manufacturing Technology, 25(1-2), 180-188.
  • [3] Bilgic, H., Baykal, P. D. (2016). The Effects of Width of the Fabric, Fabric and Model Type on the Efficiency of marker Plan in Terms of Apparel. TEKSTIL ve KONFEKSIYON, 26(3).
  • [4] Rose, D. M., Shier, D. R. (2007). Cut scheduling in the apparel industry. Computer Operation Research, 34, 3209-3228.
  • [5] Li, Z., Milenkovic, V. (1995). Compaction and separation algorithms for non-convex polygons and their applications. European Journal of Operation Research, 84, 539-561.
  • [6] Li, S. W., Guo, L. W., Li, C. H. (2009). Proceedings, Eighth International Conference on Machine Learning and Cybernetics, (Baoding, China), July 2009, p. 2560-2563.
  • [7] Burke, E. K., et al. (2007). Complete and robust no-fit polygon generation for the irregular stock cutting problem. European Journal of Operational Research, 179(1), 27-49.
  • [8] Chung, M. -J., Lin, H. -F., Wang, M. -J. J. (2007). The development of sizing systems for Taiwanese elementary-and high-school students. International Journal of Industrial Ergonomics, 37(8), 707-716.
  • [9] Hsu, C. -H. (2009). Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry. Expert Systems with Applications, 36(3), 4185-4191.
  • [10] Wong, W. K., Leung, S. Y. S. (2008). Genetic optimization of fabric utilization in apparel manufacturing, International Journal of Production Economy, , 114: p. 376-387.
  • [11] Kayar, M., Ozel, Y. (2008). Using neural network method to solve marker making ‘‘calculation of fabric lays quantities’’efficiency for optimum result in the apparel industry. In: 8th WSEAS international conference on simulation, modeling and optimization (SMO ‘08) Santander, Cantabria, Spain, 23–25 September 2008, p. 219–223.
  • [12] Ng, S., et al. (2001). Fabric Loss during Spreading: A Comparative Study of the Actual Loss in Manufacturing Men’s Shirts. Journal of the Textile Institute, 92(3), 269-279.
  • [13] Pamuk, O., Yildiz, E. Z. (2016). A study about parameters affecting the marker plan efficiency. TEKST KONFEKSIYON, 26, 430-435.
  • [14] Wong, W. K., et al. (2013). 7 - Optimizing fabric spreading and cutting schedules in apparel production using genetic algorithms and fuzzy set theory, in Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI), W.K. Wong, Z.X. Guo, and S.Y.S. Leung, Editors. 2013, Woodhead Publishing. p. 132-152.
  • [15] Milenkovic, V. J. (1998). Rotational polygon overlap minimization and compaction. Computational Geometry, 10(4), 305-318.
  • [16] Mok, P. Y., Kwong, C. K., Wong, W. K. (2007). Optimisation of fault-tolerant fabric-cutting schedules using genetic algorithms and fuzzy set theory. European Journal of Operational Research, 177(3), 1876-1893.
  • [17] Bennell, J. A., Oliveira, J. F. (2008). The geometry of nestingproblems: A tutorial. European Journal of Operational Research, 184(2), 397-415.
  • [18] Hsu, C. H. (2009). Developing accurate industrial standards to facilitate production in apparel manufacturing based on anthropometric data. Human Factors and Ergonomics in Manufacturing & Service Industries, 19(3), 199-211.
  • [19] Ondogan, Z., Erdogan, M. (2006). The Comparison of the Manual and CAD Systems for Pattern Making, Grading and Marker Making Processes. Fibres And textiles in Eastern Europe, 14(1), 62.
  • [20] Czarnecki, C. (1995). Integrating the cutting and sewing room of garment manufacture using mechatronic techniques. Mechatronics, 5(2), 295-308.
  • [21] Naveed, T., Hussain, A., Zhong, Y. (2017). Reducing fabric wastage through image projected virtual marker (IPVM). Textile Research Journal, 88(14), 1571-1580.
  • [22] Guo, Z. X., et al. (2006). Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry. Computers & Industrial Engineering, 50(3), 202-219.
  • [23] Irenee R. (2015). The science of personal dress complete study: Body shapes. Amazaon.com.
  • [24] Hutton, W. C., Malko, J. A., Fajman, W. A. (2003). Lumbar disc volume measured by MRI: effects of bed rest, horizontal exercise, and vertical loading. Aviation, Space, and Environmental Medicine, 74(1), 73-78.
  • [25] Goel, S., Tashakkori, R. (2015). Correlation Between Body Measurements of Different Genders and Races, in Collaborative Mathematics and Statistics Research. Springer, p. 7-17.
  • [26] AGBO, D. D. A. (2013). Determination of Body Size and Shape Using Wasit, Bust, and Hip Measurements for Effective Garment Designing for Adult Females in Benue State. Volume 4 April, p. 1.
  • [27] Awoh, D. K. (2017). Body Size and Shape Categorization of Some Ethnic Groups in Benue State Using Waist, Bust and Hip Measurements. International Journal of Scientific Research in Information Systems and Engineering (IJSRISE), 1(1).
  • [28] Bye, E., et al. (2008). Optimized pattern grading. International Journal of Clothing Science and Technology, 20(2), 79-92.
  • [29] Naveed. T., et al. A comparative study on fabric efficiencies for different human body shapes in the apparel industry. Autex Research Journal. DOI: 10.1515/aut-2018-0027.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-22b72eef-1184-4be3-977c-27bda6911861
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