Seam slippage often occurs with some garments during the process of wearing or washing, which not only affects the appearance of the garment but also influences garment quality. The purpose of this study is to find out the factors that affect the seam slippage of garments. In order to make the test results closer to those of the garment itself, this study first proposed to make the fabric into a Japanese woman's prototype sample, in order to simulate the garments produced by a garment enterprise. Then, according to standards GB/T 13772.2-2008/ISO 13936-2: 2004 and GB/T 21294-2014, samples were made and seam slippage at the armhole and side seam was tested. Experimental results reveal that the factors that cause the seam slippage of garments are the fabric, seam type and sewing thread. According to the regression analysis, the seam type has a significant effect on the seam slippage of the armhole and side seam, with Pearson correlation coefficients of −0.715 and −0.650, respectively. Thickness, weight, weft density and weave type of the fabric are also important in terms of seam slippage. The weight of the fabric is more significant than other factors; the significant values at the armhole and side seam are 0.009 and 0.002, respectively. In the linear equation, it is shown that weft breaking strength of the fabric only impacts the seam slippage at the side seam. Sewing thread is another important factor for the seam slippage of garments, and its influence on the armhole is obvious; the larger the sewing thread linear density of polyester material is, the smaller the seam slippage is.

Purpose The purpose of this paper is to study the style design methods of professional female vests that meet the emotional needs of consumers. Design/methodology/approach Using the theory of kansei engineering as a guide to screen representative samples of female professional vests and relevant emotional vocabularies of styles, through morphological analysis, style design elements of female professional vests are extracted, the fifth-order semantic difference questionnaire was used to establish the perceptual assessment matrix for design elements, the correlation analysis method and multiple linear regression analysis were used to analyze the results of the perceptual evaluation of the sample, find out the relationship between the perceptual vocabulary and design elements of professional female vest styles, and establish a regression model, finally, it is verified by random samples of the market, so as to guide the development of new products. Findings The seven design elements extracted from professional female vest styles have an impact on consumer perception, by using a linear analysis method, the correspondence between perceptual perception of consumers and style design elements can be quantified and a model can be established to accurately predict consumers’ perceptual intentions. Originality/value The application of perceptual engineering in the style design of professional female vests provides a new idea for the design of clothing styles. It helps garment companies and designers to determine the development direction of professional woman’s vest styles, while the research results provide design reference for other products.

In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

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