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Personalized Pattern Recommendation System of Men’s Shirts

In: Artificial Intelligence for Smart Manufacturing

Author

Listed:
  • Guillaume Tartare

    (University of Lille, ENSAIT, GEMTEX-Laboratoire de Génie et Matériaux Textiles)

  • Cheng Chi

    (University of Lille, ENSAIT, GEMTEX-Laboratoire de Génie et Matériaux Textiles)

  • Pascal Bruniaux

    (University of Lille, ENSAIT, GEMTEX-Laboratoire de Génie et Matériaux Textiles)

Abstract

Commercial garment recommendation systems have been generally used in the apparel industry. However, existing research on digital garment design has focused on the technical development of the virtual design process, with little knowledge of traditional designers. The fit of a garment has a significant role in whether a customer purchases that garment. In order to develop a well-fitting garment, designers and pattern makers should adjust the garment pattern several times until the customer is satisfied. Currently, there are three main drawbacks of traditional pattern-making: (1) it is very time-consuming and inefficient, (2) it relies too much on experienced designers, and (3) the relationship between the human body shape and the garment is not fully explored. In practice, the designer plays a key role in a successful design process. There is a need to integrate the designer’s knowledge and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modeling, and garment fit assessment should be integrated into the apparel CAD system to improve the efficiency of the design process. Based on the above issues, a fit-oriented garment pattern intelligent recommendation system is possible for supporting the design of personalized garment products. The system works in combination with a newly developed design process, i.e. body shape identification—design solution recommendation—3D virtual presentation and evaluation—design parameter adjustment. This process can be repeated until the user is satisfied. The proposed recommendation system has been validated by some successful practical design cases.

Suggested Citation

  • Guillaume Tartare & Cheng Chi & Pascal Bruniaux, 2023. "Personalized Pattern Recommendation System of Men’s Shirts," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Artificial Intelligence for Smart Manufacturing, pages 119-143, Springer.
  • Handle: RePEc:spr:ssrchp:978-3-031-30510-8_7
    DOI: 10.1007/978-3-031-30510-8_7
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