| Author: | Zhang, Xin |
| Title: | Development of an intelligent personalized patternmaking system based on D2C model |
| Advisors: | Mok, Tracy (SFT) Fan, Jintu (SFT) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Department: | School of Fashion and Textiles |
| Pages: | xx, 208 pages : color illustrations |
| Language: | English |
| Abstract: | In today’s fashion market, consumers, particularly the young generation, often prefer personalized products, whereas for manufacturers, producing products at lower costs and a faster speed are at the top of the agenda, contributing to the increase in competitiveness and hence the growth in profits. To meet the needs of both parties, mass customization appears to be a crucial strategy. Nevertheless, most clothing products that customers currently purchase are still ready-to-wear (RTW) produced by mass production model. RTW garments are typically produced based on garment size charts, which are derived from size tables of the brands, where each brand has its own target customer segment and thus its own size table, representing a certain body shape, named as ‘average figure’ of the brand. In RTW production, grading is an inseparable part of producing multiple-sized garments, but the current grading methods show inevitable limitations. Specifically, the use of proportional grading in the mass production, in which uniform deviations are assumed in the grading process, failing to reflect the characteristics of different bodies and resulting in ill-fitting garments. Individuals, whose body shapes deviated from the average figure, are forced to choose which body area they want their clothing to fit. To address these issues, an intelligent personalised patternmaking system is proposed and developed in this study to create better fitted garments more easily and efficiently. In the proposed system, the auto-grading technique, as a crucial function, is designed to address the limitations of the traditional proportional grading method commonly utilized in the RTW production, aiming to provide customized clothing patterns for individuals with diverse body sizes and shapes. Meanwhile, a novel method for size recommendation is also developed to offer size suggestions for customers to cater to the environment of current RTW mass production. This study involves a total of three developments, building upon a comprehensive review of related theories, practice and research. The first development in an integrated size table that covers very broad body size ranges as well as a large variety of body shapes, which can serve as a valuable size database to provide key sizes and range of measurements of individuals. In the second development, automatic size recommendation method first introduces a quantitative fit evaluation scheme, helping customers identify appropriate size of RTW clothing. This quantitative fit evaluation method also provides the theoretical rationale to formulate the grade distributions to be used in the auto-grading function of the third development of an automatic personalised pattern generation system. Both the automatic size recommendation method and automatic personalised pattern generation methods were validated by carefully designed experiments. This study explores the relationship between garment fit, clothing sizes, and apparel customization. Given the rapid expansion of international fashion e-commerce in recent years, the proposed intelligent personalised patternmaking system can be applied in direct-to-consumer (D2C) model to offers on one hand effective size recommendation for ready-to-wear products based on standard size charts; and on the other hand, not only provides optimal fit for customers of different body shapes but also gives manufacturers an easy way to customize clothing patterns for individual customers. |
| Rights: | All rights reserved |
| Access: | open access |
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