Personalized Pattern Recommendation System of Men’s Shirts

Guillaume Tartare (), Cheng Chi () and Pascal Bruniaux ()
Additional contact information
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

A chapter in Artificial Intelligence for Smart Manufacturing, 2023, pp 119-143 from Springer

Abstract: 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.

Keywords: Human-centered; 3D body modelling; Designer’s knowledge; Personalized pattern-making; Parametric design; Fit garment; Intelligent recommendation system (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-30510-8_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031305108

DOI: 10.1007/978-3-031-30510-8_7

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().