Research On The Method Of Customized Size Formulation For Young Women’s Clothing Based On The Loss Coefficient Of Fit

With the improvement of material living standards,consumers’ demand for customized clothing is gradually increasing,and their requirements for clothing fit are constantly increasing.Traditional large-scale clothing production is difficult to meet consumers’ requirements for clothing fit.As important participants in fashion consumption,young women have a particularly strong demand for customized clothing.Compared with traditional clothing scale production orders,the number of customer groups for clothing customization orders is relatively small,and the size based on large-scale data is difficult to reflect the differences in individual body shape characteristics.Therefore,it is necessary to study clothing customization size for young women.Based on this,in order to develop high fit clothing customization size for young women,this article proposes a clothing customization size development method based on the loss coefficient of fit.Through organizing and analyzing mainstream literature on the research direction of this article,it is found that clothing size research mainly focuses on the establishment of size shapes,supplemented by research directions such as subdivision of human body types.At the same time,there is a trend of transitioning from a single method to a combination method,especially to a combination method containing cluster analysis.Based on literature review,in order to meet the higher demand of consumers for clothing fit and the need for enterprises to improve the production efficiency of clothing customization,a method for developing customized sizes for young women’s clothing considering the loss coefficient of fit is proposed.This article uses the improved Martin measurement method and three-dimensional human scanning technology to collect 8 human body data from 230 unmarried pregnant young women aged 18 to 25,including height,chest circumference,waist circumference,hip circumference,back length,shoulder width,leg length,and thigh circumference.Based on the data of 230 testers,the sparrow search algorithm was used to optimize the initial weights and thresholds of the BP neural network,and to fill in the missing values of the sample data.The experiment proved that the optimized BP neural network predicted values using the sparrow search algorithm were more accurate.Randomly select 100 individuals from 230 testers to form a clothing custom size sample set for size development.This article uses principal component analysis and correlation analysis methods to determine the clustering basis variables by calculating the correlation index.At the same time,the fitting loss coefficient method is used to determine the number of clusters,and K-means clustering and linear regression analysis methods are used to obtain the clothing customized size table.To demonstrate the superiority of the fitting loss coefficient method,based on the relevant literature citations of existing methods for determining cluster numbers and their application in clothing size development,mixed F-statistics and the final cluster center distance were selected for comparative experiments.The experimental results indicate that the clothing size design method considering the loss coefficient of fit is superior to the other two methods in terms of overall consideration of fit,coverage,and production efficiency.This paper puts forward a new idea to formulate the size from the angle of fitting loss of finished garments,provides a certain reference for the subsequent research on the size of garment customization,and provides a method to formulate the size of high-fit garments for garment customization enterprises.