Fibers and Polymers

, Volume 9, Issue 1, pp 87–91 | Cite as

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties

Article

Abstract

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.

Keywords

Artificial neural network Linear regression Ring spun yarn Yarn tenacity Breaking elongation 

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References

  1. 1.
    M. E. Üreyen and H. Kadoğlu, Text. Res. J., 76(5), 360 (2006).CrossRefGoogle Scholar
  2. 2.
    M. D. Ethridge and R. Zhu, “Proceedings of the Beltwide Cotton Conference”, Vol. 2, p.1314, 1996.Google Scholar
  3. 3.
    L. Hunter, Melliand Textilber, 69(E), 123 (1988).Google Scholar
  4. 4.
    P. K. Majumdar and A. Majumdar, Text. Res. J., 74, 652 (2004).CrossRefGoogle Scholar
  5. 5.
    Y. El Mogahzy, R. M. Broughton, and W. K. Lynch, Text. Res. J., 60, 495 (1990).CrossRefGoogle Scholar
  6. 6.
    L. Hunter, “Proceedings of the 27th International Cotton Conference”, Bremen, March 24–27, 2004.Google Scholar
  7. 7.
    R. Chattopadhyay and A. Guha, “Artificial Neural Networks: Applications to Textile”, pp.17–22, The Textile Institute, Manchester, 2004.Google Scholar
  8. 8.
    L. Cheng and D. L. Adams, Text. Res. J., 65, 495 (1995).CrossRefGoogle Scholar
  9. 9.
    F. Pynckels, P. Kiekens, S. Sette, L. Van Langenhove, and K. Impe, J. Text. Inst., 88, 440 (1997).Google Scholar
  10. 10.
    S. Sette, L. Boullart, L. Van Langenhove, and P. Kiekens, Text. Res. J., 67, 84 (1989).Google Scholar
  11. 11.
    A. Majumdar, P. K. Majumdar, and B. Sarkar, Indian J. Fibre Text. Res., 29, 157 (2004).Google Scholar
  12. 12.
    A. Majumdar, P. K. Majumdar, and B. Sarkar, J. Text. Inst., 96, 55 (2005).CrossRefGoogle Scholar
  13. 13.
    A. Majumdar, P. K. Majumdar, and B. Sarkar, Indian J. Fibre Text. Res., 30, 19 (2005).Google Scholar
  14. 14.
    V. B. Rao, “C++ Neural Networks and Fuzzy Logic”, pp.14–22, IDG Books Worldwide Inc., 1995.Google Scholar

Copyright information

© The Korean Fiber Society 2008

Authors and Affiliations

  1. 1.School of Industrial ArtsAnadolu UniversityEskisehirTurkey
  2. 2.Textile Engineering DepartmentEge UniversityIzmirTurkey

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