A Machine Learning-Enhanced 3D Reverse Design Approach to Personalized Garments in Pursuit of Sustainability

Zhujun Wang (), Xuyuan Tao, Xianyi Zeng, Yingmei Xing, Zhenzhen Xu and Pascal Bruniaux
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Zhujun Wang: School of Textile and Garment, Anhui Polytechnic University, Wuhu 241000, China
Xuyuan Tao: GEMTEX Laboratory, Ecole Nationale Superieure des Arts et Industries Textiles, 59056 Roubaix, France
Xianyi Zeng: GEMTEX Laboratory, Ecole Nationale Superieure des Arts et Industries Textiles, 59056 Roubaix, France
Yingmei Xing: School of Textile and Garment, Anhui Polytechnic University, Wuhu 241000, China
Zhenzhen Xu: School of Textile and Garment, Anhui Polytechnic University, Wuhu 241000, China
Pascal Bruniaux: GEMTEX Laboratory, Ecole Nationale Superieure des Arts et Industries Textiles, 59056 Roubaix, France

Sustainability, 2023, vol. 15, issue 7, 1-21

Abstract: The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth a new 3D reverse garment design approach embedded with a garment fit prediction and structure self-adaptive adjustment mechanism, using machine learning (ML) techniques. Initially, the 3D basic garment was drawn directly on the scanned mannequin of a specific consumer. Next, a probabilistic neural network (PNN) was employed to predict the garment’s fit. Afterwards, genetic algorithms (GA) and support vector regression (SVR) were utilized to estimate and control the garment structural parameters following the feedback of fit evaluation and the consumer’s personalized needs. Meanwhile, a comprehensive evaluation was constructed to characterize the quantitative relationships between the consumer profile and the designed garment profile (garment fit and styles). Ultimately, the desired garment which met the consumer’s needs was obtained by performing the routine of “design–fit evaluation–pattern adjustment–comprehensive evaluation”, iteratively. The experimental results show that the proposed approach provides a new solution to develop quality personalized fashion products (garments) more accurately, economically, and in an environmentally friendly way. It is feasible to facilitate the sustainable development of fashion companies by simultaneously reducing costs and negative impacts on the environment.

Keywords: interactive 3D garment design; reverse engineering; machine learning; probabilistic neural network; genetic algorithms; support vector regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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