Name
Abstract | ||
|---|---|---|
The ease allowance is an important criterion in garment design. It is often taken into account in the process of construction of garment patterns. However, the existing pattern generation methods cannot provide a suitable estimation of ease allowance, which is strongly related to wearer's body shapes and movements and used fabrics. They can only produce 2D patterns for fixed standard values of ease allowance. In this paper, we present a new method for optimizing the estimation of ease allowance of a garment using fuzzy logic and sensory evaluation. Based on the optimized values of ease allowance generated from fuzzy models related to different key body positions and different wearer's movements, we obtain an aggregated ease allowance using the OWA operator. This aggregated result can further improve the wearer's fitting perception of a garment and adjust the compromise between the style of garments and the fitting comfort sensation of wearers. The related weights of the OWA operator are determined according to designer's linguistic criteria on comfort and garment style. The effectiveness of our method has been validated in the design of trousers of jean type. It can be also applied for designing other types of garment. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.engappai.2008.05.007 | Eng. Appl. of AI |
Keywords | Field | DocType |
fuzzy logic,related weight,owa operator,ease allowance,different wearer,aggregated ease allowance,aggregated result,garment design,body shape,garment style,garment pattern,sensory evaluation technique,data aggregation,sensory evaluation | Pattern generation,Computer science,Fuzzy logic,Body shape,Garment design,Operator (computer programming),Artificial intelligence,Body positions,Perception,Data aggregator,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 2 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
9 | 0.61 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Chen | 1 | 9 | 0.61 |
| X. Zeng | 2 | 20 | 2.38 |
| M. Happiette | 3 | 9 | 0.61 |
| P. Bruniaux | 4 | 9 | 0.61 |
| R. Ng | 5 | 9 | 0.61 |
| Winnie Yu | 6 | 15 | 1.53 |