Indexing surface smoothness and fiber softness by sound frequency analysis for textile clustering and classification. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Kim, Hye Jin; Youn, Seonyoung; Choi, Jeein; Kim, Hyeonji; Shim, Myounghee; Yun, Changsang
• Is Part Of: Textile research journal. Volume 91:Issue 1/2(2021)
• Page Start: 200
• Page End: 218
• Publication Date: 2021-01
• Subjects: fabric texture -- smoothness -- softness -- cluster -- drape -- bending; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517520935211
• Languages: English
• ISSNs: 0040-5175

Abstract

Cutting-edge technology is being used in the fashion industry for three-dimensional (3D) virtual fitting programs to meet the demand for clothing manufacturing as well as textile simulating. For expanding the textile choices of the program users, this research looks at the indexation of tactile sensations, the texture of fabrics, which has been subjectively evaluated by the human hand. Firstly, this study objectively measured and indexed the surface smoothness and fiber softness of 749 fabrics through a tissue softness analyzer that mimics human hands. Secondly, after statistical analyses of the drape coefficient, each bending distance and Young's modulus for the initial tensile strength in the warp–weft directions, the thickness, and the weight of the fabrics, it was found that drape (Pearson coefficient = 0.532) and bending properties are the key factors in the fabric surface smoothness (TS750), while the fiber softness (TS7) showed a weak correlation with thickness (Pearson coefficient = 0.364), followed by the log value of the Young's modulus in the weft direction. Thirdly, we classified nine clusters for TS750 based on the 11 regression variables with significant Pearson coefficients, and characterized each cluster in order of surface smoothness (TS750) after Duncan post-hoc tests and analyses of variance (all statistically significant, p < 0.01) with microscopic surface images of one sample for each cluster. For precise TS750 classification, we finally trained the 267 samples with the same 11 variables, resulting in 93.3% prediction through an artificial neural network with multiple hidden layers. This prediction with Fisher discriminants for the clusters will enable the 3D virtual program users to predict further clustering of newly added fabrics.