THE PREDICTION OF YARN TENSILE PROPERTIES BY USING ARTIFICIAL NEURAL NETWORKS

This paper presents an artificial-neural-net model for predicting yarn tensile properties. A single hidden-layer neural network trained by using the back-propagation algorithm performs a functional mapping between material properties, process variables, and the resulting yam tensile properties. The material and process variables, namely, yarn count, blend, and front- and back-nozzle pressures on an air-jet spinning machine, are correlated with the experimentally determined yam properties: breaking load and breaking elongation. The neural net was trained and then used to predict the tensile properties of yarns. The errors of prediction were low despite the availability of only a relatively small data set for training, and in each case the prediction error was less than the standard deviation of experimentation. Use of the cross-validation technique ensured that the neural net obtained a generalized mapping of the inputs and outputs.