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A knowledge-supported approach for garment pattern design using fuzzy logic and artificial neural networks

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Abstract

In the clothing industry, garment pattern design serves as a significant middle link between fashion design and manufacturing. With the advent of advanced multimedia techniques, like virtual reality, 3D modeling, and interactive design, this work has become more intuitive. However, it is still a tremendous knowledge-based work that relied on the experienced patternmakers’ know-how. For enterprises, it will take a long time to cultivate a patternmaker from an abecedarian to an expert. Moreover, while facing fierce competition in the market, enterprises still have to endure the pressures and risks led by the turnover of experienced patternmakers. In this context, we put forward a knowledge-supported garment pattern design approach by learning the experienced patternmakers’ knowledge based on fuzzy logic and artificial neural networks. Based on this approach, we created a knowledge-supported pattern design model, consisting of several sub-models following the garment styles, considering the properties of fabrics and fitting degree. The inputs of the model are the feature body dimensions, while the outputs, namely the pattern parameters, can be generated following the fabric and fitting degree selected. Through performance comparison with other models and the actual fitting test, the results revealed that the present approach was applicable. Our proposed approach can not only support the non-expert patternmakers or abecedarians to make decisions when developing the patterns by reducing the difficulties of patternmaking but help the enterprises to reduce the dependencies on the experts, hence promoting the efficiency and reducing risks.

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Acknowledgements

The authors wish to acknowledge the financial support of the Key Research Project of Humanities and Social Sciences in Anhui Province College (No. SK2016A0116 and SK2017A0119), the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University (No. CUSF-DH-D-2020091), the Open Project Program of Key Laboratory of Silk Culture Heritage and Products Design Digital Technology of Ministry of Culture and Tourism of China (No. 2020WLB07), the European H2020 Research Program (Project: FBD_BModel, No. 761122), the Special Excellent Ph.D. International Visit Program of DHU, the Open Project Program of Anhui Province College Key Laboratory of Textile Fabrics, Anhui Engineering and Technology Research Center of Textile (No. 2018AKLTF15), the Social Science Planning Project in Anhui (No. AHSKQ2019D085), and the National Key Research and Development Program of China (No. 2019YFF0302100).

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Authors and Affiliations

  1. College of Textiles and Garments, Anhui Polytechnic University, Wuhu, 241000, Anhui, China

    Zhujun Wang & Yingmei Xing

  2. College of Fashion and Design, Donghua University, Shanghai, 200051, China

    Zhujun Wang, Jianping Wang, Yalan Yang & Shuo Xu

  3. Key Laboratory of Silk Culture Heritage and Products Design Digital Technology, Ministry of Culture and Tourism, Zhejiang, 310018, Hangzhou, China

    Zhujun Wang

  4. GEMTEX Laboratory, Ecole Nationale Superieure des Arts et Industries Textiles, 59056, Roubaix, France

    Xianyi Zeng

  5. Key Laboratory of Clothing Design & Technology, Donghua University, Ministry of Education, Shanghai, 200051, China

    Zhujun Wang & Jianping Wang

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  1. Zhujun Wang
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Correspondence to Jianping Wang.

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Wang, Z., Xing, Y., Wang, J. et al. A knowledge-supported approach for garment pattern design using fuzzy logic and artificial neural networks. Multimed Tools Appl 81, 19013–19033 (2022). https://doi.org/10.1007/s11042-020-10090-6

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