Fibers and Polymers

, Volume 14, Issue 10, pp 1722–1730 | Cite as

A synergetic immune clonal selection algorithm based multi-objective optimization method for carbon fiber drawing process

  • Jiajia Chen
  • Yongsheng Ding
  • Yaochu Jin
  • Kuangrong Hao
Article

Abstract

Carbon fiber production is a large-scale system which comprises a large number of production processes. Among the various complex production conditions, the drawing process is one of the most influential factors that affect the quality of carbon fiber. How to obtain the fittest process parameters of the drawing process is a typical multi-objective optimization problem. To address the drawbacks of mathematical programming techniques available for solving optimization problems, we propose a new synergetic immune clonal selection algorithm (SICSA) to obtain the optimal process parameters, such as the linear density, strength, and breaking elongation ratio. The main operators of the SICSA are synergetic evolution, clonal operation and non-uniform mutation. The synergetic evolution between populations adopts a “division-parallel-recombination” mode, the clonal operation searches for optimal solutions globally, and the non-uniform mutation explores optimal solutions locally and enhances the diversity of the solutions. As a result, optimal solutions which lead to reasonable distribution of the drawing ratio are obtained. We also compare the proposed SICSA with an immune algorithm and a genetic algorithm for optimizing the parameter in the drawing process. Our results show that the SICSA has the best performance in precision and convergence time. These results can serve as references and provide guidance for real production of carbon fiber.

Keywords

Carbon fiber, Drawing process Synergetic immunity clonal selection Multi-objective optimization 

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References

  1. 1.
    J. Yang and Y. Jaluria, Int. J. Heat Mass Transf., 52, 4108 (2009).CrossRefGoogle Scholar
  2. 2.
    S. E. Bechtel, S. Vohra, and K. I. Jacob, Polymer, 42, 2045 (2001).CrossRefGoogle Scholar
  3. 3.
    Z. Gou and A. J. McHugh, J. Non-Newton. Fluid Mech., 118, 121 (2004).CrossRefGoogle Scholar
  4. 4.
    A. Makradi, C. L. Cox, S. Ahzi, and S. Belouettar, J. Appl. Polym. Sci., 100, 1705 (2006).CrossRefGoogle Scholar
  5. 5.
    A. Mataram, A. F. Ismail, D. S. A. Mahmod, and T. Matsuura, Mater. Lett., 64, 1875 (2010).CrossRefGoogle Scholar
  6. 6.
    T. Hobbs and A. J. Lesser, Polymer, 41, 6223 (2000).CrossRefGoogle Scholar
  7. 7.
    S.-M. Chuo, M.-H. Wan, L. A. Wang, and J.-S. Wang, J. Lightwave Technol., 27, 2983 (2009).CrossRefGoogle Scholar
  8. 8.
    B. Suman and P. Tandon, Chem. Eng. Sci., 65, 5537 (2010).CrossRefGoogle Scholar
  9. 9.
    M. A. Mabrouk, Polymer Testing, 21, 653 (2002).CrossRefGoogle Scholar
  10. 10.
    A. Mawardi and R. Pitchumani, IEEE Photonics J., 2, 620 (2010).CrossRefGoogle Scholar
  11. 11.
    X. Liang, Y.-S. Ding, L.-H. Ren, K.-R. Hao, H.-P. Wang, and J.-J. Chen, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42, 367 (2012).CrossRefGoogle Scholar
  12. 12.
    K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, IEEE Trans. on Evolutionary Computation, 6, 182 (2002).CrossRefGoogle Scholar
  13. 13.
    H. Safikhani, A. Hajiloo, and M. A. Ranjbar, Comput. Chem. Eng., 35, 1064 (2011).CrossRefGoogle Scholar
  14. 14.
    C. A. Coello Coello and N. C. Cortes, Genetic Programming and Evolvable Machines, 6, 163 (2005).CrossRefGoogle Scholar
  15. 15.
    F. Freschi and M. Repetto, Eng. Opt., 38, 975 (2006).CrossRefGoogle Scholar
  16. 16.
    K. Atashkari, N. Nariman-Zadeh, M. Gölć, A. Khalkhali, and A. Jamali,, Energy Conv. Manag., 48, 1029 (2007).CrossRefGoogle Scholar
  17. 17.
    K. Deb and S. Tiwari, Eur. J. Operational Res., 185, 1062 (2008).CrossRefGoogle Scholar
  18. 18.
    G. P. Coelho, A. E. A. da Silva, and F. J. Von Zuben, Neural Comput. Appl., 19, 1103 (2010).CrossRefGoogle Scholar
  19. 19.
    M. H. Asiabar, S. H. Ghodsypour, and R. Kerachian, Comput. Ind. Eng., 56, 1566 (2009).CrossRefGoogle Scholar
  20. 20.
    Z. J. Li, H. T. Liao, and D. W. Coit, Reliab. Eng. Syst. Saf., 94, 1585 (2009).CrossRefGoogle Scholar
  21. 21.
    A. Kaveh and K. Laknejadi, Expert Syst. Appl., 38, 15475 (2011).CrossRefGoogle Scholar
  22. 22.
    S. N. Omkar, J. Senthilnath, R. Khandelwal, G. N. Naik, and S. Gopalakrishnan, Appl. Soft Comput., 11, 489 (2011).CrossRefGoogle Scholar
  23. 23.
    I. Aydin, M. Karakose, and E. Akin, Appl. Soft Comput., 11, 120 (2011).CrossRefGoogle Scholar
  24. 24.
    M. Pavone, G. Narzisi, and G. Nicosia, J. Global Opt., 53, 769 (2012).CrossRefGoogle Scholar
  25. 25.
    R. C. Liu, X. R. Zhang, N. Yang, Q. F. Lei, and L. C. Jiao, Appl. Soft Comput., 12, 302 (2012).CrossRefGoogle Scholar
  26. 26.
    W. L. Han and X. H. Wang, Fiber. Polym., 13, 626 (2012).CrossRefGoogle Scholar
  27. 27.
    E. G. Okafor and Y. C. Sun, Reliab. Eng. Syst. Saf., 103, 61 (2012).CrossRefGoogle Scholar
  28. 28.
    G. D. Chen, X. Han, G. P. Liu, C. Jiang, and Z. H. Zhao, Appl. Soft Comput., 12, 14 (2012).CrossRefGoogle Scholar
  29. 29.
    C. D. Boor, Appl. Mathematical Sci., 27, 1 (2001).CrossRefGoogle Scholar
  30. 30.
    V. K. Karakasis and A. Stafylopatis, IEEE Transactions on Evolutionary Computation, 12, 662 (2008).CrossRefGoogle Scholar
  31. 31.
    C. A. Coello Coello and M. R. Sierra, “Congress on Evolutionary Computation”, pp.482–489, IEEE Press, Canberra, 2003.Google Scholar
  32. 32.
    S. Chiocchio, E. Martin, P. Barabaschi, H. W. Barels, J. How, and W. Spears, Fusion Eng. Des., 82, 548 (2007).CrossRefGoogle Scholar

Copyright information

© The Korean Fiber Society and Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jiajia Chen
    • 1
    • 4
  • Yongsheng Ding
    • 1
    • 2
  • Yaochu Jin
    • 1
    • 3
  • Kuangrong Hao
    • 1
    • 2
  1. 1.College of Information Sciences and TechnologyDonghua UniversityShanghaiP. R. China
  2. 2.Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of EducationDonghua UniversityShanghaiP. R. China
  3. 3.Department of ComputingUniversity of SurreyGuildfordUK
  4. 4.The College of Information, Mechanical and Electrical EngineeringShanghai Normal UniversityShanghaiP. R. China

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