A study of instrumental method for suiting fabric hand evaluation and classification

In the textile and apparel industry, fabric end-use preference and selection criteria are largely based on fabric hand because it relates to both the mechanical properties and aesthetic appearance of fabrics. This paper examines a method to grade fabric hand based on Kawabata’s measurements and neural network modeling. The proposed method is verified by comparing the hand graded by the neural network model to Kawabata’s total hand value. Ninety-five commercial fabrics from different manufacturers were tested using Kawabata evaluation system (KES-FB). Cluster analysis using SAS classified the suiting fabric samples into four groups in this study. The test results of fabric mechanical properties show similarities and dissimilarities between woven and knitted suiting fabrics. In comparison, woven suiting fabrics are less subject to shear and bending deformation. Knitted fabrics have a higher total hand value than woven fabrics with a smoother surface. Cluster analysis well divided the suiting fabric samples into four groups describing different fabric performance. The training dataset in the neural network model was selected based on information from the clustering results. The training model was proved to be accurate with a low MSE of 4 × 10-8. The model successfully graded the test samples with values ranged from 0 to 1. Additionally, the validity for grading fabric hand using the neural network technique was examined by analyzing the correlation between the hand graded by neural network model and Kawabata’s equations. The regression analysis shows a relatively strong correlation (p<0.0001, R2= 0.6363) between neural network grades and Kawabata’s grades.