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Journal Article

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3D virtual garment simulation, Artificial neural network, Body dimension and fitting perception, Psychological segmentation



3D virtual simulation prototyping software combined with computer-aided manufacturing systems are widely used and are becoming essential in the fashion industry in the earlier stages of the product development process for apparel design. These technologies streamline the garment product fitting procedures, as well as improve the supply chain environmentally, socially, and economically by eliminating large volumes of redundant samples. Issues of non-standardized selection on garment sizing, ease allowance, and size of 3D avatar for creating 3D garments have been addressed by many researchers. Understanding the relationship between body dimensions, ease allowance, and apparel sizes before adopting virtual garment simulation is fundamental for satisfying high customer demands in the apparel industry. However, designers find difficulties providing the appropriate garment fit for customers without fully understanding the motivation and emotions of customers’ fitting preferences in a virtual world. The main purpose of this study is to investigate apparel sizes for virtual fitting, particularly looking at garment ease with consideration of body dimensions and the psychographic characteristics of subjects. In order to develop a virtual garment fitting prediction model, an artificial neural network (ANN) was applied. We recruited 50 subjects between the ages of 18 and 35 years old to conduct 3D body scans and a questionnaire survey for physical and psychological segmentation, as well as fitting preferences evaluation through co-design operations on virtual garment simulation using a commercial software called Optitex. The results from the study demonstrate that ANN is effective in modeling the non-linear relationship between pattern measurements, psychological characteristics, and body measurements. This new approach and the proposed method of virtual garment fitting model prediction on garment sizes using an Artificial Neural Network (ANN) is significant in prediction accuracy. The project also achieves the concept of mass customization and customer orientation and generates new size-fitting data that can bring a new level of end-user satisfaction.

Source Publication

Human Factors for Apparel and Textile Engineering

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