Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties
In this study artificial neural network (ANN) models have been designed to predict the ring cotton yarn properties from the fiber properties measured on HVI (high volume instrument) system and the performance of ANN models have been compared with our previous statistical models based on regression analysis. Yarn count, twist and roving properties were selected as input variables as they give significant influence on yarn properties. In experimental part, a total of 180 cotton ring spun yarns were produced using 15 different blends. The four yarn counts and three twist multipliers were chosen within the range of Ne 20–35 and αe 3.8–4.6 respectively. After measuring yarn tenacity and breaking elongation, evaluations of data were performed by using ANN. Afterwards, sensitivity analysis results and coefficient of multiple determination (R2) values of ANN and regression models were compared. Our results show that ANN is more powerful tool than the regression models.
KeywordsArtificial neural network Linear regression Ring spun yarn Yarn tenacity Breaking elongation
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- 2.M. D. Ethridge and R. Zhu, “Proceedings of the Beltwide Cotton Conference”, Vol. 2, p.1314, 1996.Google Scholar
- 3.L. Hunter, Melliand Textilber, 69(E), 123 (1988).Google Scholar
- 6.L. Hunter, “Proceedings of the 27th International Cotton Conference”, Bremen, March 24–27, 2004.Google Scholar
- 7.R. Chattopadhyay and A. Guha, “Artificial Neural Networks: Applications to Textile”, pp.17–22, The Textile Institute, Manchester, 2004.Google Scholar
- 9.F. Pynckels, P. Kiekens, S. Sette, L. Van Langenhove, and K. Impe, J. Text. Inst., 88, 440 (1997).Google Scholar
- 10.S. Sette, L. Boullart, L. Van Langenhove, and P. Kiekens, Text. Res. J., 67, 84 (1989).Google Scholar
- 11.A. Majumdar, P. K. Majumdar, and B. Sarkar, Indian J. Fibre Text. Res., 29, 157 (2004).Google Scholar
- 13.A. Majumdar, P. K. Majumdar, and B. Sarkar, Indian J. Fibre Text. Res., 30, 19 (2005).Google Scholar
- 14.V. B. Rao, “C++ Neural Networks and Fuzzy Logic”, pp.14–22, IDG Books Worldwide Inc., 1995.Google Scholar