Artificial Neural Network Models for Predicting Breaking Strength and Abrasion Resistance Properties of Woven Fabrics With Different Chenille Yarns

Abstract

This study was carried out with aim of predicting some performance properties of fabrics with changing chenille yarn parameters. In this study, different chenille yarns were produced with parameters of pile length, yarn count and yarn type. Three different yarn types were used: polyester, acrylic and viscose. Four different yarn counts were used for each yarn type and four different pile lengths were used for each yarn count. Thus, 48 woven fabrics were obtained from 48 different yarns. The estimated properties included breaking strength in weft-warp direction and abrasion resistance, and these properties formed output data. As input data, yarn properties such as pile length, yarn count and yarn type; fabric properties such as fabric density, fabric thickness and fabric weight were used. Neural network toolbox in MATLAB was used to develop Artificial Neural Network (ANN) models. Different network structures were used to estimate three performance features, thus aiming to obtain more accurate results. Additionally, predictions were made with linear and nonlinear multiple regression models, and compared with ANN models. The R2 values obtained from ANN models for breaking strength in the warp-weft direction and abrasion resistance were found to be 0.95, 0.74, 0.87, respectively, while they were found to be 0.32, 0.40, 0.41 for linear multiple regression models and 0.65, 0.27, 0.60 for nonlinear multiple regression models. The obtained ANN models were successful by a clear margin compared to statistical models.