Neural Model of the Spinning Process Dedicated to Predicting Properties of Cotton-Polyester Blended Yarns on the Basis of the Characteristics of Feeding Streams
Research and development
Authors:
- Jackowska-Strumułło Lidia
Institute of Automatic Control, Faculty of Electrical, Electronic, Computer and Control Engineering, Technical University of Lodz, Łódź, Poland - Cyniak Danuta
Department of Spinning Technology and Yarn Structure, Technical University of Łódź, Łódź, Poland - Czekalski Jerzy (j/w)
- Jackowski Tadeusz (j/w)
 Full text | Abstract: The subject of our research were yarns manufactured from cotton-polyester blended slivers, as well as pure cotton and polyester slivers with the use of a BD 200S rotor spinning frame. Partial models of the spinning process were developed for selected, essential yarn quality parameters, such as tenacity, irregularity of yarn’s mass, hairiness, number of yarn faults (number of thin and thick places and number of neps). In order to model the variability of these parameters, the following methods were used: linear and linearised regression, non-linear multiple regression as well as ADALINE and two-layer perceptron (MLP) artificial neural networks. The best results of approximation we obtained using the MLP network. Selection of the optimum network structure was carried out for each of the parameters. A hybrid model was used to model the variability of the CV coefficient. |
Tags: yarn blends, cotton, polyester-fibres, rotor spinning, yarn quality parameters, linear regression, non-linear regression, artificial neural networks.