Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

DOI

10.2991/ijcis.d.210608.002How to use a DOI?

Keywords

Garment pattern design; Mass customization; Industry 4.0; Knowledge base; Garment patterns associate adaptation; Support vector regression; Ontology; Sensory analysis

Abstract

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.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Zhujun Wang
AU  - Jianping Wang
AU  - Xianyi Zeng
AU  - Xuyuan Tao
AU  - Yingmei Xing
AU  - Pascal Bruniaux
PY  - 2021
DA  - 2021/06/11
TI  - Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling
JO  - International Journal of Computational Intelligence Systems
SP  - 1687
EP  - 1699
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210608.002
DO  - 10.2991/ijcis.d.210608.002
ID  - Wang2021
ER  -