Optimization of top roller diameter of ring machine to enhance yarn evenness by using artificial intelligence

Abstract

Attempts have been made to improve cotton and cotton/polyester (35:75) yarns regularity by optimizing front top rollers diameter of the ring machine using a self-organized Kohonen neural network. The diameters of the top rollers have been reduced in stages and in each stage yarns are produced. The unevenness as well as the imperfections of the produced yarns have been measured. The results show that the unevenness of the yarn decreases with the decrease in top rollers diameter up to optimum diameter beyond which the unevenness of yarns increases rapidly as the top rollers diameter decreases. These optimum values are different in the cases of cotton and cotton/polyester yarns. Kohonen neural network has been applied to observe optimum values of top rollers diameter in case of each yarn type. The optimum diameter of top rollers as estimated by neural network is found to be 27.5 mm for most of the cotton and cotton/polyester (35:75) yarns.