Fabric Softness Classification Using Linear and Nonlinear Models

Abstract

In this study, the authors use linear and nonlinear models and yarn parameters such as CV%, hairiness, and surface softness to classify the softness of knitted fabrics (T-shirts) for comparison to human subjective evaluations. All classification rates are verified with a leave-one-out cross-validation technique. The results show 20% misclassification when using a linear model to sort samples into two classes (low and high). When sorting into three classes, the misclassification is 30%. When sorting T-shirt softness into three classes using a tree modeling technique and the surface response average (SRA) and maximum peak-to-valley height (Ry), it is possible to match the human data at a 65% rate. When using surface response parameters and measured yam properties to sort T-shirt softness into three classes, with tree modeling it is possible to classify 91% of the samples accurately based on the human data.