Predicting the intermingled yarn number of nips and nips stability with neural network models

Abstract

This study aims at predicting the effects of selected process parameters on nips stability and number of nips by using different artificial intelligence methods. Partially oriented polyester yarn with 283 dtex linear density and different numbers of filaments are intermingled with different speed and pressure levels. The feed forward neural network with multi-hidden layers (ML-FFNN) and general regression neural networks (GRNN) have been selected as artificial intelligence methods. The number of filaments, intermingling speed and pressure values are used as input variables on the artificial neural networks. The effects of number of hidden layers on the ML-FFNN and number of nodes in the hidden layer are investigated. Based on comparative results, the ML-FFNN is found to give better performance (at most 6%) than by GRNN in terms of prediction accuracy on train and test data sets. It can be concluded from this study that the neural networks has great ability to predict intermingling process parameters.