Predicting of Spun Yarns Properties in Worsted Spinning by Using Hybrid Supervised and Unsupervised Neural Network

The uniformity of yarn is one of the major quality parameters which significantly influences on yarn characteristics, warping, weaving, and ultimately fabric production. This parameter depends on fiber properties and spinning process directly. In this study, yarn non-uniformity in a worsted spinning system was predicted by using a hybrid technique involving Kohonen's self-organized and perceptron neural network. A series of 2490 experiments involving parameters of raw materials and production quality performed from a worsted spinning factory was collected and analyzed. In step one, Kohonen neural network was used to cluster data. Each cluster was fed into the perceptron network, and the predicting error of the hybrid model was calculated for a new series of data. As the second step, the whole data were fed into the perceptron network and the error percentage was computed. Comparing the results obtained from the hybrid technique and the perceptron network showed that the rate of predicting error can be reduced by 3.34% when the hybrid approach of Kohonen and perceptron neural network was used.