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Fibers and Polymers

, 12:657 | Cite as

Prediction system of open-end rotor spinning process based on LM-neural network for bamboo charcoal fibers

  • Te-Li Su
  • Chung-Feng Jeffrey Kuo
  • Hsin-Jung Wei
Article

Abstract

In line with the environmental protection trends of the 21st century, bamboo charcoal fiber is invented to meet the requirements of the fields of science and technology. Its special functionalities, namely, antistatic, moisture adsorptive, perspiring, antibacterial, deodorizing, anti-radiation, and far infrared properties make it extremely suitable for applications in medicine, sports, and recreational fields, as well as an important breakthrough for environmentally-friendly textile materials. To achieve rapid manufacturing, this study processes bamboo charcoal fibers by open-end (OE) rotor spinning. The Taguchi orthogonal array is applied to the design of this experiment, and the significant factors of fibers quality are obtained through ANOVA in order to facilitate the follow-up processes of quality control. The process prediction system is built based on the test data, and is combined with the back-propagation neural network and the Levenberg-Marquardt (LM) algorithm in order to establish an OE rotor spinning process prediction system. The rotor speed, feed speed, and winding speed are set as the network input parameters, while the yarn strength, the hairiness, the unevenness, and the imperfections indicator (I.P.I.) are the output parameters. Through network learning and training, this system reports a prediction error below 5 %, proving that this prediction system has excellent predictability.

Keywords

Bamboo charcoal Open-end rotor spinning Taguchi method Neural network 

References

  1. 1.
    C. I. Su and X. R. Lin, Fiber. Polym., 10, 822 (2010).CrossRefGoogle Scholar
  2. 2.
    O. Demiryürek and E. Koç, Fiber. Polym., 10, 694 (2009).CrossRefGoogle Scholar
  3. 3.
    C. F. J. Kuo and T. L. Su, Fiber. Polym., 7, 404 (2006).CrossRefGoogle Scholar
  4. 4.
    C. F. J. Kuo, T. L. Su, and C. P. Tsai, Fiber. Polym., 8, 654 (2010).CrossRefGoogle Scholar
  5. 5.
    C. F. J. Kuo, T. L. Su, C. H. Chiu, and C. P. Tsai, Fiber. Polym., 8, 66 (2007).CrossRefGoogle Scholar
  6. 6.
    M. D. Jean, C. D. Liu, and J. T. Wang, Applied Surface Science, 245, 290 (2005).CrossRefGoogle Scholar
  7. 7.
    J. Fan and J. Pan, Appl. Math. Comput., 207, 351 (2009).CrossRefGoogle Scholar
  8. 8.
    C. Ma, Appl. Math. Comput., 206, 133 (2009).CrossRefGoogle Scholar
  9. 9.
    J. Zhao and F. Wang, Journal of Materials Processing Technology, 166, 387 (2005).CrossRefGoogle Scholar
  10. 10.
    V. Singh, I. Gupta, and H. O. Gupta, Engineering Applications of Artificial Intelligence, 20, 249 (2007).CrossRefGoogle Scholar
  11. 11.
    B. G. Kermani, S. S. Schiffman, and H. T. Nagle, Sensors and Actuators B: Chemical, 110, 13 (2005).CrossRefGoogle Scholar
  12. 12.
    C. F. J. Kuo, T. L. Su, and Y. J. Huang, Fiber. Polym., 8, 529 (2007).CrossRefGoogle Scholar
  13. 13.
    C. F. J. Kuo, T. L. Su, C. D. Chang, and C. H. Lee, Fiber. Polym., 9, 768 (2008).CrossRefGoogle Scholar
  14. 14.
    C. F. J. Kuo, C. D. Chang, T. L. Su, and C. H. Lee, Fiber. Polym., 10, 394 (2009).CrossRefGoogle Scholar

Copyright information

© The Korean Fiber Society and Springer Netherlands 2011

Authors and Affiliations

  • Te-Li Su
    • 1
  • Chung-Feng Jeffrey Kuo
    • 2
  • Hsin-Jung Wei
    • 2
  1. 1.Department of Cosmetic Application and Management, St. Mary’s MedicineNursing and Management CollegeYilanTaiwan
  2. 2.Department of Materials Science and EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan

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