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Design and Research of Automatic Garment-Pattern-Generation System Based on Parameterized Design

Author

Listed:
  • Peng Jin

    (College of Fashion and Design, Donghua University, Changning District, Shanghai 200051, China)

  • Jintu Fan

    (Shanghai International Fashion Innovation Centre, Donghua University, Changning District, Shanghai 200051, China
    Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China)

  • Rong Zheng

    (Shanghai International Fashion Innovation Centre, Donghua University, Changning District, Shanghai 200051, China)

  • Qing Chen

    (Shanghai International Fashion Innovation Centre, Donghua University, Changning District, Shanghai 200051, China)

  • Le Liu

    (School of Design, Jiangnan University, Wuxi 214122, China)

  • Runtian Jiang

    (College of Fashion and Design, Donghua University, Changning District, Shanghai 200051, China)

  • Hui Zhang

    (College of Fashion and Design, Donghua University, Changning District, Shanghai 200051, China)

Abstract

Personalization in the apparel industry shows importance and the potential for demand, but the existing personalization has unreasonable time cost, labor cost, and resource waste. To solve the problems of the waste of resources as well as both time and labor cost caused by manual pattern making in clothing personalization, a method of automatic garment pattern generation based on a parametric formula and the Python language was proposed. Based on the classification of common curves in patterns, three curve fitting algorithms based on different parameters were derived and combined with the Python language to achieve personalized generation of different patterns by classifying the parameters in the system into key parameters, secondary parameters, and variable parameters. Three different methods for verifying the accuracy of the garment patterns were proposed based on curve fitting similarity and three-dimensional virtual modeling, and the accuracy of the proposed system was verified. The results show that the accuracy and comfort of the patterns generated via the system were high. Meanwhile, the Python-language-based system fits well with the production system of enterprises, which can improve the rapid response capability of garment personalization, greatly save the time cost and labor cost of enterprises, reduce resource loss, and contribute to the sustainable development of the garment industry.

Suggested Citation

  • Peng Jin & Jintu Fan & Rong Zheng & Qing Chen & Le Liu & Runtian Jiang & Hui Zhang, 2023. "Design and Research of Automatic Garment-Pattern-Generation System Based on Parameterized Design," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1268-:d:1030288
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    References listed on IDEAS

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    1. Danny Chi Kuen Ho & Eve Man Hin Chan & Tsz Leung Yip & Chi-Wing Tsang, 2020. "The United States’ Clothing Imports from Asian Countries along the Belt and Road: An Extended Gravity Trade Model with Application of Artificial Neural Network," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
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    Cited by:

    1. Zhujun Wang & Xuyuan Tao & Xianyi Zeng & Yingmei Xing & Zhenzhen Xu & Pascal Bruniaux, 2023. "A Machine Learning-Enhanced 3D Reverse Design Approach to Personalized Garments in Pursuit of Sustainability," Sustainability, MDPI, vol. 15(7), pages 1-21, April.

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