Multi-objective Optimization in WEDM of Inconel 750 Alloy: Application of TOPSIS Embedded Grey Wolf Optimizer

  • G. Venkata Ajay KumarEmail author
  • K. L. Narasimhamu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


The current work focuses on multi-objective wire electrical discharge machining (WEDM) parameters optimization of Inconel 750 alloy. Taguchi L18 orthogonal array (OA) was used to carry the experiments in various WEDM parameters such as wire feed rate, pulse-on-time, pulse-off-time and water pressure. The output responses estimated are machining speed (cutting) and machined surface roughness of the part. Optimum machining parameters estimation is difficult in Taguchi process; a multi-objective optimization (MOO) technique known as TOPSIS is embedded with grey wolf optimizer (GWO). Initially, the multi-responses are converted to the relative closeness value, and then the heuristic approach is applied. Based on the optimal parametric setting value from GWO, a confirmation test has been conducted and compared with the fitness value.


Grey wolf optimizer Wire electric discharge machining Parametric optimization TOPSIS 


  1. 1.
    Williams, R.E., Rajurkar, K.P.: Study of wire electrical discharge machined surface characteristics. J. Mater. Process. Technol. 28(1–2), 127–138 (1991)CrossRefGoogle Scholar
  2. 2.
    Bewlay, BP., Weimer, M., Kelly, T., Suzuki, A., Subramanian, PR., Baker, I., Heilmaier, M., Kumer, S., Yashimi, K. (eds.) Inter Metallic Based Alloys-Science, Technology and Applications, Mrs Symposium Proceedings, vol. 1, no. 516, p. 49 (2013)Google Scholar
  3. 3.
    Anurag, S.: Wire-EDM: a potential manufacturing process for gamma titanium aluminides in future aero engines. Int. J. Adv. Manuf. Technol. 94(1–4), 351–356 (2018)CrossRefGoogle Scholar
  4. 4.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Lai, Y.J., Liu, T.Y., Hwang, C.L.: Topsis for MODM. Eur. J. Oper. Res. 76(3), 486–500 (1994)zbMATHCrossRefGoogle Scholar
  6. 6.
    Chakraborty, S., Mitra, A.: Parametric optimization of abrasive water-jet machining processes using grey wolf optimizer. Mater. Manuf. Process. 1–12 (2018)Google Scholar
  7. 7.
    Nain, S.S., Garg, D., Kumar, S.: Investigation for obtaining the optimal solution for improving the performance of WEDM of super alloy Udimet-L605 using particle swarm optimization. Eng. Sci. Technol. Int. J. 21(2), 261–273 (2018)CrossRefGoogle Scholar
  8. 8.
    Yuvaraj, N., Pradeep Kumar, M.: Multiresponse optimization of abrasive water jet cutting process parameters using TOPSIS approach. Mater. Manuf. Process. 30(7), 882–889 (2015)CrossRefGoogle Scholar
  9. 9.
    Rao, R.V.: Advanced Modeling and Optimization of Manufacturing Processes : International Research and Development (2010)Google Scholar
  10. 10.
    Garg, M.P., Kumar, A., Sahu, C.K.: Mathematical modeling and analysis of WEDM machining parameters of nickel-based super alloy using response surface methodology. Sādhanā 42(6), 981–1005 (2017)Google Scholar
  11. 11.
    Selvam, M.P., Kumar, P.R.: Optimization Kerf width and surface roughness in wire cut electrical discharge machining using brass wire. Mech Mech Eng 21(1), 37–55 (2017)Google Scholar
  12. 12.
    Ashok, R., Poovazhagan, L., Srinath Ramkumar, S., Vignesh Kumar, S.: Optimization of material removal rate in wire-edm using fuzzy logic and artificial neural network. In: Applied Mechanics and Materials, vol. 867, pp. 73–80. Trans Tech Publications (2017)Google Scholar
  13. 13.
    Kumar, P., Meenu, M., Kumar, V.: Optimization of process parameters for WEDM of Inconel 825 using grey relational analysis. Decis. Sci. Lett. 7(4), 405–416 (2018)CrossRefGoogle Scholar
  14. 14.
    Huang, Y., Ming, W., Guo, J., Zhang, Z., Liu, G., Li, M., Zhang, G.: Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analyses. Int. J. Adv. Manuf. Technol. 69(5–8), 993–1008 (2013)CrossRefGoogle Scholar
  15. 15.
    Majumder, H., Maity, K.: Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM. Measurement 118, 1–13 (2018)CrossRefGoogle Scholar
  16. 16.
    Rajyalakshmi, G., Ramaiah, P.V.: Multiple process parameter optimization of wire electrical discharge machining on Inconel 825 using Taguchi grey relational analysis. Int. J. Adv. Manuf. Technol. 69(5–8), 1249–1262 (2013)CrossRefGoogle Scholar
  17. 17.
    Varun, A., Venkaiah, N.: Simultaneous optimization of WEDM responses using grey relational analysis coupled with genetic algorithm while machining EN 353. Int. J. Adv. Manuf. Technol. 76(1–4), 675–690 (2015)CrossRefGoogle Scholar
  18. 18.
    Rao, R.V., Pawar, P.J.: Modelling and optimization of process parameters of wire electrical discharge machining. Proc. Inst. Mech. Eng. Part B J Eng. Manuf. 223(11), 1431–1440 (2009)CrossRefGoogle Scholar
  19. 19.
    Nayak, B.B., Mahapatra, S.S., Chatterjee, S., Abhishek, K.: Parametric appraisal of WEDM using harmony search algorithm. Mater. Today Proc. 2(4–5), 2562–2568 (2015)CrossRefGoogle Scholar
  20. 20.
    Yoon, K.P., Hwang, C.L.: Multiple Attribute Decision Making: An Introduction, vol. 104. Sage publications (1995)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringAnnamacharya Institute of Technology & Sciences (Autonomous)RajampetIndia
  2. 2.Department of Mechanical EngineeringSree Vidyanikethan Engineering College (Autonomous)A. RangampetIndia

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