Objective Hand Evaluation of Stretch Fabrics Using Artificial Neural Network and Computational Model

Stretch woven textiles are widely employed because of their excellent elongation and recovery properties. The stretch fabrics studied in this research can be used as power stretch and action stretch sportswear fabrics. The purpose of this paper is to investigate an approach to predict the total hand value by translating the senses into numbers. Computational and artificial neural network models were developed. Five primary hand attributes softness, smoothness, fullness, stiffness, and stretchability were shortlisted that influence the fabric handle. Computational methods were used to generate primary and total hand equations based on basic mechanical parameters. To forecast primary hand values, stretch percent was used with low stress mechanical properties. The association between subjective, computational, and artificial neural network total hand values was investigated using a statistical technique. The subjective and computational hand values have a high correlation of 0.84. The subjective and artificial neural network hand values were shown to have a 0.82 correlation. The accuracy of both models' prediction of fabric hand was found to be very high. The study finds that both models can forecast the total hand value of stretch materials with a tolerable level of accuracy.