Research On Individual Garment Design Driven By Coupling Multi Factors

With the booming of fashion industry and the trending of individuation, people’s requirements for clothing have been changed from comfort and beauty to individuation and temperament, and the individuation of garment involves pattern, fit and style. Traditional garment CAD design workflow is from underlying factors to apparel products and each function module of garment CAD is mutually independent, separating the correlative relationship of each design stage. This kind of workflow cannot reflect the combination laws of garment element, so that users cannot grasp the fashion design status.In order to reflect individual features of garment, the clothing factors and constituting form must be chosen accurately. This paper presents the design philosophy of driven elements coupling based on the analysis of the components of clothing. First, we acquire user preference through the elements of dressing space formed by coupling the human parameters and intervals, and realize personalized garment fit design by the spatial distribution of intervals driven by user preference. Second, we make up modeling style elements by coupling the styling features and style characteristics, and drive individual garment evolutionary design based on user interaction. Third, we deal with interference through analyzing the garment coupling status, and then propose a series design for personalized clothing combining two kinds of similarity calculations based on attribute similarity and user evaluation similarity. The main contents are as follows.Initially, we present individual garment fit design based on dressing space. The feature sizes are extracted by factor analysis and garment knowledge. The body detailed sizes are rebuilt using the feature sizes. By acquiring the data from the3D body scanner, the spatial ease allowance is defined. The coupling relationship between body sizes and ease allowance is built based on data mining. The spatial ease allowance can be mapped into2D ease allowance to modify the pattern design. Therefore, once the user fit preference and fuzzy fitting semantic evaluation are transformed into spatial ease allowance weights, the pattern can be adjusted by the spatial ease allowance which contains preference and evaluation. Subsequently, we propose individual garment evolutionary design based on styling features. The construct of garment is made clear through the detailed design content which is divided into style, ornament, color and pattern. Using Kansei engineering, the whole style features can be acquired, and the detailed style feature can be obtained by kernel principal component analysis (KPCA). Then, the relationship between two features can be established based on support vector machine. In order to ensure the stable feature of the evolutionary consequences, the style feature estimation is taken as a judgment in the genetic algorithm. In order to reflect the user preference, the interactive evaluation is adopted in the genetic algorithm as well. These direct the evolutionary design, improving the accuracy and efficiency of the garment genetic algorithm.At last, we raise individual garment series design based on multi-factors coupling. Through the analysis of the garment factors coupling status, the method deals with factor interference is given base on collaborative optimization. Aiming at the conflict between series garments, the attribute similarity method is proposed to deal with the conflict, and the series design for case characteristic can be achieved. The collaborative filtering algorithm is introduced in series design. By clustering the similar review, the similar garment can be searched, and then the similar garment set can be collected based on similar attributes and reviews. This set is classified into several themes according to the features extracted from the set.