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Porosity exploration of SMA by Taguchi, regression analysis and genetic programming

Author

Listed:
  • Neeraj Sharma

    (YMCA University of Science and Technology
    D.A.V. University)

  • Kamal Kumar

    (PEC University of Technology)

  • Tilak Raj

    (YMCA University of Science and Technology)

  • Vinod Kumar

    (M.M. University)

Abstract

Porosity plays a vital role in the field of bio-medical engineering of implantations i.e. orthopedic and orthodontics. Shape memory alloys exhibit a greater strength with a higher porosity. The strength of porous shape memory alloys were found similar to the strength of bones. In the present research, NiTi SMA is fabricated by powder metallurgy process. The processing parameters of sintering and compaction (i.e. compaction pressure, sintering temperature and sintering time) play an important role in the porosity investigation of SMA. Taguchi’s method based $$\hbox {L}_{9}$$ L 9 orthogonal array was selected for the planning of experiments. Sintering temperature and sintering time were the significant process parameters as compared to compaction pressure. Regression coefficients and equation was derived by use of regression analysis. Further this equation was solved with the help of genetic programming and results of both (i.e. Taguchi’ method and genetic programming) were compared to find the maximum porosity. The maximum porosity that can be achieved is 56 % and the confirmation experiments were performed at 95 % confidence level.

Suggested Citation

  • Neeraj Sharma & Kamal Kumar & Tilak Raj & Vinod Kumar, 2019. "Porosity exploration of SMA by Taguchi, regression analysis and genetic programming," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 139-146, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1236-8
    DOI: 10.1007/s10845-016-1236-8
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    Cited by:

    1. Dawei Zhao & Mikhail Ivanov & Yuanxun Wang & Wenhao Du, 2021. "Welding quality evaluation of resistance spot welding based on a hybrid approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1819-1832, October.

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