Skip to main content
SpringerLink
Log in

Study the effect of operating parameters and intrinsic features of yarn and fabric on thermal conductivity of stretch knitted fabrics using artificial intelligence system

  • Published:
Fibers and Polymers Aims and scope Submit manuscript

Abstract

An artificial intelligence-based system approach is presented in which the effects of the operating parameters and intrinsic features of yarn and fabric on Thermal Conductivity of Stretch Knitted Fabrics are investigated. These parameters were pre-selected according to their possible influence on the outputs which were the thermal conductivity. An original fuzzy logic based method was proposed to select the most relevant parameters. The results show that Knitted Structure’s is the most important input parameter, followed by Lycra Proportion (%), Loop length (cm), Yarn Count, Weight per Unit Area (g/m2), Thickness (m), Gauge, Lycra Yarn Count (dtex) and Yarn Composition. According to our previous works, two types of model have been set up by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. The relative importance of the input variables was calculated using the connection weight approach. The results were found to agree with the fuzzy logic based sensitivity criterion. The trend analysis of the developed model revealed the influence of various input parameters on the thermal conductivity of knitted fabrics. Thus, it is believed that artificial intelligence System could efficiently be applied to the knit industry to understand, evaluate and predict thermal comfort parameters of stretch knitted fabrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. D. Sajn Gorjanc, K. Dimitrovski, and B. Mateja, Text. Res. J., 82, 1498 (2012).

    Article  CAS  Google Scholar 

  2. N. Ozdil, A. Marmarali, and D. Kretzschmar, Int. J. Therm. Sci., 46, 1318 (2007).

    Article  Google Scholar 

  3. M. S. Parmar, Ind. J. Fiber Text. Res., 24, 41 (1999).

    CAS  Google Scholar 

  4. Y. Li, “Textile Progress Series”, Vol.31, pp.1–13, The Textile Institute, Alden, Oxford, 2001.

    Google Scholar 

  5. A. D. Gun, Fiber. Polym., 12, 2258 (2011).

    Google Scholar 

  6. A. Das and S. M. Ishtiaque, J. Text. Apparel, Technol. Management, 3, 1 (2004).

    Google Scholar 

  7. A. Das, V. K. Kothari, and M. Balaji, J. Text. Inst., 98, 363 (2007).

    Article  CAS  Google Scholar 

  8. G. Ozçelik, A. Çay, and E. Kirtay, Fibers Text. East. Eur., 15, 55 (2007).

    Google Scholar 

  9. N. Ucar and T. Yilmaz, Fibers Text. East. Eur., 12, 34 (2005).

    Google Scholar 

  10. A. P. Cuden and U. S. Elesini, Acta Chimica Slovenica, 57, 957 (2010).

    CAS  Google Scholar 

  11. S. Tezel and Y. Kavusturan, Text. Res. J., 78, 966 (2008).

    Article  CAS  Google Scholar 

  12. R. Ciukas, A. Jovita, and K. Paulius, Fibers Text. East. Eur., 18, 89 (2010).

    Google Scholar 

  13. Y. S. Chen, J. Fan, and W. Zhang, Text. Res. J., 73, 152 (2003).

    Article  CAS  Google Scholar 

  14. B. Farnworth, Text. Res. J., 53, 717 (1983).

    Article  Google Scholar 

  15. H. J. Hoge and G. F. Fonseca, Text. Res. J., 34, 401 (1964).

    Article  CAS  Google Scholar 

  16. P. Zhang, R. H. Gong, and Y. Yanai, Text. Res. J., 72, 83 (2002).

    CAS  Google Scholar 

  17. D. Bhattacharjee and V. K. Kothari, Text. Res. J., 77, 4 (2007).

    Article  CAS  Google Scholar 

  18. M. Darvishzadeh, D. Semnani, E. Shirani, and M. Sheikhzadeh, Fiber. Polym., 13, 542 (2012).

    Article  CAS  Google Scholar 

  19. P. G. Unal, M. E. Üreyen, and D. Mecit, Fiber. Polym., 13, 87 (2012).

    Article  CAS  Google Scholar 

  20. V. K. Midha, V. K. Kothari, R. Chattopadhyay, and A. Mukhopadhyay, Fiber. Polym., 11, 661 (2010).

    Article  Google Scholar 

  21. Jayadeva, A. Guha, and A. Chattopadhyay, J. Text. Inst., 94, 186 (2003).

    Article  Google Scholar 

  22. J. D. Olden and D. A. Jackson, Ecological Modelling, 154, 135 (2002).

    Article  Google Scholar 

  23. J. D. Olden, M. K. Joy, and R. G. Death, Ecological Modelling, 178, 389 (2004).

    Article  Google Scholar 

  24. H. Alibi, F. Fayala, A. Jemni, and X. Zeng, J. Appl. Sci., 12, 2283 (2012).

    Article  Google Scholar 

  25. F. Fayala, H. Alibi, A. Jemni, and X. Zeng, J. Eng. Fiber. Fabrics, 3, 53 (2008).

    Google Scholar 

  26. X. Deng, P. Vroman, X. Zeng, and L. Koehl, J. Inform. Comput. Sci., 2, 93 (2007).

    Google Scholar 

  27. X. Deng, P. Vroman, X. Zeng, and L. Koehl, Eng. Appl. Artificial Intelligence, 23, 1368 (2010).

    Article  Google Scholar 

  28. R. AbdJelil, X. Zeng, L. Koehl, and A. Perwuelz, J. Inform. Comput. Sci., 2, 141 (2013).

    Google Scholar 

  29. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms”, 1st ed., pp.125–142, Plenum Press, New York, 1981.

    Book  Google Scholar 

  30. N. Oglakcioglu and A. Marmarali, Fibers Text. East. Eur, 15, 94 (2007).

    CAS  Google Scholar 

  31. S. B. Stankovic, D. Popovic, and G. B. Poparic, Polymer Testing, 27, 41 (2008).

    Article  CAS  Google Scholar 

  32. A. Majumdar, S. Mukhopadhyay, and R. Yadav, Int. J. Therm. Sci., 49, 2042 (2010).

    Article  Google Scholar 

  33. P. Chidambaram, R. Govind, and K. C. Venkataraman, Autex Res. J., 11, 103 (2011).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Study of the Thermal and Energy Systems (LESTE), National School Engineers of Monastir, University of Monastir, Monastir, 5019, Tunisia

    Faten Fayala, Hamza Alibi & Abdelmajid Jemni

  2. GEMTEX Research Laboratory, National School of Arts and Textiles Industries (ENSAIT), Roubaix, University North Lille of France, Roubaix Cedex, France

    Xianyi Zeng

Authors
  1. Faten Fayala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamza Alibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelmajid Jemni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianyi Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Faten Fayala.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fayala, F., Alibi, H., Jemni, A. et al. Study the effect of operating parameters and intrinsic features of yarn and fabric on thermal conductivity of stretch knitted fabrics using artificial intelligence system. Fibers Polym 15, 855–864 (2014). https://doi.org/10.1007/s12221-014-0855-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12221-014-0855-y

Keywords

Search

Navigation