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.

中文翻译：

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通过声频分析索引表面光滑度和纤维柔软度，用于纺织品聚类和分类

时尚行业正在使用尖端技术进行三维（3D）虚拟试衣程序，以满足服装制造和纺织品模拟的需求。为了扩大程序用户的纺织品选择，本研究着眼于触觉指数、织物质地，这些指数已由人手主观评估。首先，本研究通过模拟人手的组织柔软度分析仪，对749种织物的表面光滑度和纤维柔软度进行客观测量和指标化。其次，对悬垂系数、各弯曲距离、经纬向初始拉伸强度、织物厚度、重量的杨氏模量进行统计分析，发现悬垂性（皮尔逊系数=0. 532) 和弯曲性能是织物表面光滑度 (TS750) 的关键因素，而纤维柔软度 (TS7) 与厚度呈弱相关性 (Pearson 系数 = 0.364)，其次是纬纱杨氏模量的对数值方向。第三，我们根据具有显着 Pearson 系数的 11 个回归变量为 TS750 分类了 9 个聚类，并在 Duncan post-hoc 检验和方差分析后按表面平滑度 (TS750) 的顺序对每个聚类进行了表征（均具有统计学意义，p < 0.01）每个簇的一个样品的微观表面图像。对于精确的 TS750 分类，我们最终用相同的 11 个变量训练了 267 个样本，通过具有多个隐藏层的人工神经网络产生了 93.3% 的预测。