Predicting the Tensile Strength of Polyester/Viscose Blended Open-End Rotor Spun Yarns Using the Artificial Neural Network and Statistical Models

Abstract

Predicting the tensile strength of polyester/viscose blended open-end rotor spun yarns using the artificial neural network and statistical models In this study, an Artificial Neural Network (ANN) and a statistical model were developed to predict the tensile strength of polyester/viscose blended open-end rotor spun yarns. Seven different blend ratios of polyester/viscose slivers were produced and these slivers are manufactured with four different rotor speed and four different yarn counts in the rotor spinning machine. A Back Propagation Multi Layer Perceptron (MLP) network and a mixture process crossed regression model with two mixture components (polyester and viscose blend ratios) and two process variables (yarn count and rotor speed) were developed to predict the tensile properties of polyester/viscose blended open-end rotor spun yarns. In conclusion, both ANN, and the statistical model have given satisfactory predictions; however, the predictions of ANN gave relatively more reliable results than those of the statistical models. Since the prediction capacity of statistical models is also obtained as satisfactory, it can also be used for the strength prediction of yarns, because of its simplicity and non-complex structure.