Fabric hand, an important characteristic to the textile industry, is influenced by such fiber parameters as flexural rigidity and friction and by yarn parameters such as count, twist, CV%, hairiness, stiffness, and softness. This study deals primarily with predicting the softness of knitted T-shirts from yarn quality parameters. The work consists of a short literature review on the existing yam parameters as well as fabric hand evaluation and prediction techniques. The latest developments in measuring the roughness of textile material surfaces are, also covered. Using the surface profile as tested by the mechanical stylus surface analyzer (MSSA), developed at North Carolina State Uni versity, a novel yarn surface analysis parameter called the "surface response average" (SRA) is developed, along with a model for the fiber/stylus tip interaction. Ten T-shirts produced from ten different yarn samples are ranked based on their softness by a panel of judges. The yams used to make these T-shirts are tested by Uster III and MSSA, and standard roughness parameters are calculated. The results show no significant corre lation between standard roughness parameters and fabric softness. The correlation be tween hand and SRA is about -0.6, which suggests that a higher SRA corresponds to a softer fabric.

The influence of winding on yam hairiness is examined, and increased hairiness during winding is verified using a Zweiglc hairiness tester. Spccifically, this study concentrates on the increase of wild hairs on the yam surface after winding, and the relationship between winding tension and yarn hairiness. Fiber transfer is proposed to explain the increased wild hairs; experimental results verify that fiber transfer occurs. A theory is proposed to explain the mechanism of fiber transfer during winding, and experimental results are given to support this mechanism.

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.

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