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Experimental Investigation of Effect of Process Parameters on Surface Roughness in Electrochemical Machining

  • Loc P. Ngo
  • Tai P. Nguyen
  • Thanh T. TranEmail author
Conference paper
  • 32 Downloads
Part of the Springer Proceedings in Materials book series (SPM, volume 6)

Abstract

With the rapid development of removal technology of materials, manufacturing methods of advanced materials with highly flexible shape has been improved. Electro-chemical machining (ECM) is one of the most advanced manufacturing methods for not only manufacturing the high hardness metal with flexible profiles, but also provides a better solution to surface roughness of finished products and rate of material removal in the comparison with other advanced manufacturing methodologies such as electric discharge machining (EDM). However, there are many parameters involving to the ECM process such as velocity of the electrode, pressure of water, voltage, frequency and pulse of the current. Therefore, a set of optimization parameters for machining process of a material type is necessary to provide the best solution in industrial applications. This research focuses on investigating an optimal set of process parameters of the ECM machine by using the surface response methodology (SRM). A database of process parameters is generated by machining and minimizing the surface roughness of the SS AISI 316 stainless steel via suing the copper electrode. Then, an objective function (or regression function) is generated by applying the SRM to the selected database. Finally, genetic algorithm is used to achieve the optimal process parameters. Experiments are implemented with achieved processing parameters to analyze and validate the proposed method.

Notes

Acknowledgements

This work was supported by to the Ministry of Education and Training for the funded project of code KC-519 (Decision 5652 on 28/12/2018).

References

  1. 1.
    R.H. Todd, D.K. Allen, L. Alting, Manufacturing Processes Reference Guide (Industrial Press, New York, 1994)Google Scholar
  2. 2.
    B. Bhattacharyya, J. Munda, M. Malapati, Int. J. Mach. Tools Manuf 44(15), 1577–1589 (2004)CrossRefGoogle Scholar
  3. 3.
    K. Rajurkar, M.M. Sundaram, A.P. Malshe, Procedia CIRP 6, 13–26 (2013)CrossRefGoogle Scholar
  4. 4.
    B. Bhattacharyya, S. Mitra, A.K. Boro, Robot. Comput.-Integr. Manuf. 18(3–4), 283–289 (2002)Google Scholar
  5. 5.
    M. Datta, D. Landolt, Electrochem. Acta 26(7), 899–907 (1981)CrossRefGoogle Scholar
  6. 6.
    J. Kozak, K. Rajurkar, Y. Makkar, J. Mater. Process. Technol. 149(1–3), 426–431 (2004)Google Scholar
  7. 7.
    M. Purcar, L. Bortels, B. van den Bossche, J. Deconinck, J. Mater. Process. Technol. 149(1–3), 472–478 (2004)CrossRefGoogle Scholar
  8. 8.
    R.V. Rao, P.J. Pawar, R. Shankar, Proc IMechE J. Engg. Manuf. 222, 949–958 (2008)CrossRefGoogle Scholar
  9. 9.
    P. Asokan, R. Ravi Kumar, R. Jeyapaul, M. Santhi, Int. J. Adv. Manuf. Tech. 39, 55–63 (2008)Google Scholar
  10. 10.
    C.-K. Yang, C.-P. Cheng, C.-C. Mai, A. Cheng Wang, J.-C. Hung, B.-H. Yan, Int. J. Mach. Tools Manuf. 50(12), 1088–1096 (2010).  https://doi.org/10.1016/j.ijmachtools.2010.08.006
  11. 11.
    D. Chakradhar, A.V. Gopal, Int. J. Mode. Optim. 1, 113–117 (2011)Google Scholar
  12. 12.
    L.P.T. Huynh, H.A. Nguyen, H.Q. Nguyen, L.K.H. Phan, T.T. Tran. J. Korean Soc. Precis. Eng. 36(8), 705–712 (2019)Google Scholar
  13. 13.
    K.-H. Choi, T.T. Tran, B.-S. Yang, D.-S. Kim, An new approach for intelligent control system design using the modified genetic algorithm, Int. J. Intelligent Systems Technologies and Applications, 9(3/4), 300–315 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Global Production Engineering and ManagementVietnamese-German UniversityThu Dau Mot CityVietnam

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