Title :

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

Author(s) :

Wang, Zhujun [Auteur]
Xing, Yingmei [Auteur]
Wang, Jianping [Auteur]
Zeng, Xianyi [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Yang, Yalan [Auteur]
Xu, Shuo [Auteur]

Journal title :

Multimedia Tools and Applications

Abbreviated title :

Multimed. Tools Appl.

Volume number :

81

Pages :

19013–19033

Publisher :

Springer Verlag

Publication date :

2020-10-29

ISSN :

1380-7501

English keyword(s) :

Garment pattern design
Knowledge modeling
Fuzzy logic
Artificial neural networks
Anthropometric measurements

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T11:38:34Z
2024-03-15T14:55:36Z