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Title: A new method of ease allowance generation for personalization of garment design
Authors: Chen, Y
Zeng, X
Happiette, M
Bruniaux, P
Ng, R 
Yu, W 
Issue Date: 2008
Source: International journal of clothing science and technology, 2008, v. 20, no. 3, p. 161-173
Abstract: Purpose - The purpose of this paper is to present recent work for optimizing the estimation of ease allowance of a garment using fuzzy logic and sensory evaluation. Design/methodology/approach - The current method first generates a number of fuzzy models each corresponding to one specific key body part and one specific wearer's movement and then aggregates all the values of ease allowance generated from these fuzzy models using the ordered weighted averaging (OWA) operator. The aggregated ease allowance takes into account geometric measures on all representative human bodies, comfort sensations of wearers related to all movements or actions and different styles of trousers (tight, normal and loose). The weights of the OWA operator can be used to adjust the compromise between the style of garments and the comfort sensation of wearers. The related weights of the OWA operator are automatically determined according to designer's linguistic criteria characterizing the relationship between wearer's movements and the features of the garment to be designed. Findings - Based on the optimized values of ease allowance generated from fuzzy models related to different key body positions and different wearer's movements, the authors obtain a personalized ease allowance, permitting to further improve the wearer's fitting perception of a garment. The effectiveness of the method has been validated in the design of trousers of jean type. It can also be applied for designing other types of garment. Originality/value - Integration of wearer's body shapes and human comfort in the design of personalized garments.
Keywords: Clothing
Fuzzy logic
Sensory perception
Publisher: Emerald Group Publishing Limited
Journal: International journal of clothing science and technology 
ISSN: 0955-6222
EISSN: 1758-5953
DOI: 10.1108/09556220810865210
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