A novel model for predicting tenacity and unevenness of ring-spun yarn: a special case in textile engineering

Abstract

This study aimed to model the unevenness and tenacity of ring-spun yarn in a special case in textile engineering using response surface methodology. Yarn number and front roll speed were input variables, while yarn tenacity and unevenness were response/output variables. This study showed that the response surface methodology (RSM) could predict the yarn’s tenacity and unevenness with the yarn coefficient of determination (R2) values of 0.99 and 0.98 and with the error sum of square (SS residual) values 0.00187 and 0.003215, respectively. We also found that an artificial neural network (ANN) could predict the yarn's tenacity and unevenness with the yarn coefficient of determination (R2) values of 0.51 and 0.63 and with the error sum of square (SS residual) values 1.48 and 0.856, respectively. It was concluded that the response surface methodology (RSM) and artificial neural network (ANN) could predict the yarn's tenacity and unevenness. Response surface methodology (RSM) predicts yarn characteristics better than ANN with MIMO (multiple inputs, multiple outputs) modeling. The novelty of this study is that we used RSM and ANN for the first time to obtain the tenacity and unevenness of ring-spun yarn accurately. A simpler approach was employed in this study for predicting tenacity and unevenness using RSM and ANN; however, future research holds the potential for incorporating advanced mathematical models to enhance the prediction. This research suggests that RSM and ANN can be applied to predicting the tenacity and unevenness of ring-spun yarn. The scientific application of this research is that the investigation will benefit practitioners in the textile industry to optimize yarn parameters by ring spinning machines.