Construction of Garment Pattern Design ...
Document type :
Article dans une revue scientifique: Article original
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Title :
Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling
Author(s) :
Wang, Zhujun [Auteur]
Donghua University [Shanghai]
Anhui Polytechnic University
Wang, Jianping [Auteur]
Donghua University [Shanghai]
Zeng, Xianyi [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Tao, Xuyuan [Auteur]
Génie et Matériaux Textiles [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Xing, Yingmei [Auteur]
Anhui Polytechnic University
Bruniaux, Pascal [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Donghua University [Shanghai]
Anhui Polytechnic University
Wang, Jianping [Auteur]
Donghua University [Shanghai]
Zeng, Xianyi [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Tao, Xuyuan [Auteur]
Génie et Matériaux Textiles [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Xing, Yingmei [Auteur]
Anhui Polytechnic University
Bruniaux, Pascal [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Journal title :
International Journal of Computational Intelligence Systems
Abbreviated title :
Int. J. Comput. Intell. Syst.
Volume number :
14
Pages :
1687 - 1699
Publication date :
2021-05-31
ISSN :
1875-6891
English keyword(s) :
Sensory analysis
Ontology
Support vector regression
Garment patterns associate adaptation
Knowledge base
Industry 4.0
Mass customization
Garment pattern design
Ontology
Support vector regression
Garment patterns associate adaptation
Knowledge base
Industry 4.0
Mass customization
Garment pattern design
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design ...
Show more >Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design.Show less >
Show more >Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
ENSAIT
Junia HEI
ENSAIT
Junia HEI
Collections :
Submission date :
2023-06-20T11:51:47Z
2024-03-19T08:02:31Z
2024-03-19T08:02:31Z
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