Indexing surface smoothness and fiber softness by sound frequency analysis for textile clustering and classification

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.

Development of a fabric classification system using drapability and tactile characteristics

“…This suggests that the weight and bending properties of warp and weft (M WARP , M WEFT ) variables are strongly correlated with the B1 cluster, which has medium drapability and a very soft surface. This finding aligns with previous studies, which consistently asserted a significant correlation between the bending properties and weight of the fabric with its softness and drapability (Bacci et al, 2012;Kim et al, 2021;Zhang et al, 2018). Based on the derived neural network, we can conclude that the cluster with highest prediction accuracy is B1.…”

“…The verified classification system was used to train an ANN using nnet, developed by Ripley et al (2016). Its aim was to predict which drapability and softness clusters the samples belong to using the mechanical properties (Kim et al, 2020(Kim et al, , 2021.…”

Review: The softness of hygiene tissue

“…As noted by Kim et al (2020), "the TSA converts the vibrations caused by friction of the fabric surface into acoustic spectrums and measures (the) acoustic frequency and sound pressure with indexing smoothness and softness." The lamellae spin on the surface of the tissue with a constant applied force.…”

Dokuma Kumaşların Deformasyon ve Geri Dönüş Özelliklerinin Yeni Bir Cihaz Kullanılarak Belirlenmesi

“…The device measures micro-surface variations, macro-surface variations and stiffness of any kind of tissue paper [22][23][24]. Kim et al [25] proposed a study in which macro and micro-surface variations (TS750 and TS7) of a large set of fabrics were measured by Tissue Softness Analyzer and the data was used for clustering and classification of textiles.…”