Digital prediction modeling of fabric softness based on thickness classification

Abstract

Textile trade requires a uniform digital softness evaluation index in the context of well-developed online trading as opposed to the sensory evaluation provided by actual touch. The fabric touch tester is used to assess the softness scores of 150 fabrics and 18 mechanical indexes in response to the existing issue that the fabric softness evaluation model does not have a wide application value. A subjective assessment experiment and objective score clustering are used to compare the validity of the objective scores. The samples are divided into three thickness categories based on one-way ANOVA and cluster analysis: light, medium, and thick. Correlation analyses are done between mechanical indexes and fabric softness ratings in each category, and stepwise regression is used to establish four prediction models for fabric softness. The findings indicate that thickness and weaving method considerably impact the fabric's softness among the fundamental variables. The bending, thermal conductivity, compression, and surface properties—in descending order— among the mechanical parameters—have an effect on the fabric's softness. The R² of the established prediction models for the softness of light, medium, and thick fabric, respectively, reached 83.2%, 88.7%, and 84.1%. It can help customers and businesses come to an understanding about the softness of fabrics, increase the number of transactions, and improve the effectiveness of online purchasing.