Optimization and prediction of the pilling performance of weft knitted fabrics produced from wool/acrylic blended yarns

Abstract

Effects of fibre, yarn and fabric parameters on the pilling performance of weft knitted fabrics produced from wool/acrylic blended yarns have been investigated. In order to optimize the process conditions and estimate the individual effects of each controllable factor on a particular response, Taguchi’s experimental design is used. The controllable factors considered in this study are blend ratio, yarn twist multiple and count, number of feeding yarns, fabric structure and knit density. According to the signal-to-noise ratio analysis, it is observed that the used materials type and the number of feeding yarns have the largest and smallest effect on the pilling performance, respectively. Knit density is the second factor affecting the pilling performance of knitted structures and it is followed by factors knit structure, yarn twist and yarn count. The optimum condition to achieve the least pilling is determined. The prediction of fabric pilling is made using neural network. The maximum and minimum errors of prediction are found to be 4.18% and 0.21% respectively. The average of predicted error of the number of pills for weft knitted fabrics is 1.92%. The results show the good capability and predictive power of artificial neural network algorithm to predict the pilling performance of weft knitted fabric.