Multi-objective optimization of some correlated process parameters in EDM of Inconel 800 using a hybrid approach

  • T. R. Paul
  • A. Saha
  • H. MajumderEmail author
  • V. Dey
  • P. Dutta
Technical Paper


Electrical discharge machining (EDM) is an extensively used non-traditional machining process used for conductive materials to get intricate or complex shapes. For any manufacturing industry, optimum parameters of control variables are of sheer importance to improve multiple performance characteristics like surface integrity and productivity. This paper presents multi-objective optimization on the basis of ratio analysis (MOORA) method coupled with principal component analysis (PCA) in order to achieve the optimal combination of EDM parameters. In this research work, response surface methodology was used for designing the experiments considering three input parameters, namely pulse-on time, pulse-off time and pulsed current. All the experiments were conducted at different parametric combinations and the performance, namely material removal rate (MRR) and surface roughness (Ra). Proposed MOORA-PCA hybrid results and conventional MOORA results were compared, and it is found that proposed methods are accurate for predicting the responses. Finally, the control variables, namely pulse-on time (TON), pulse-off time (TOFF) and pulsed current (Ip), were set to 300 µs, 85 µs and 18 A, respectively, to get maximum MRR and minimum surface roughness.


Multi-objective optimization Inconel 800 MOORA PCA Surface roughness 



  1. 1.
    Muthuramalingam T, Mohan B (2015) A review on influence of electrical process parameters in EDM process. Arch Civ Mech Eng 15(1):87–94CrossRefGoogle Scholar
  2. 2.
    Tsai H, Yan B, Huang F (2003) EDM performance of Cr/Cu-based composite electrodes. Int J Mach Tools Manuf 43(3):245–252CrossRefGoogle Scholar
  3. 3.
    Pachaury Y, Tandon P (2017) An overview of electric discharge machining of ceramics and ceramic based composites. J Manuf Process 25:369–390CrossRefGoogle Scholar
  4. 4.
    Sharma P, Chakradhar D, Narendranath S (2017) Analysis and optimization of WEDM performance characteristics of Inconel 706 for aerospace application. Silicon 10(3):921–930CrossRefGoogle Scholar
  5. 5.
    Majumder H, Maity K (2017) Optimization of machining condition in WEDM for titanium grade 6 using MOORA coupled with PCA—a multivariate hybrid approach. J Adv Manuf Syst 16(02):81–99CrossRefGoogle Scholar
  6. 6.
    Ho K, Newman S (2003) State of the art electrical discharge machining (EDM). Int J Mach Tools Manuf 43(13):1287–1300CrossRefGoogle Scholar
  7. 7.
    Khan A, Maity K (2016) Parametric optimization of some non-conventional machining processes using MOORA method. Int J Eng Res Afr 20:19–40CrossRefGoogle Scholar
  8. 8.
    Chakravorty R, Gauri SK, Chakraborty S (2012) Optimization of correlated responses of EDM process. Mater Manuf Processes 27(3):337–347CrossRefGoogle Scholar
  9. 9.
    Bhaumik M, Maity KP (2016) Multi response optimization by using the hybrid technique in electro discharge machining of AISI 304. Int J Eng Res Afr 26:68–75CrossRefGoogle Scholar
  10. 10.
    Gadakh V (2011) Application of MOORA method for parametric optimization of milling process. Int J Appl Eng Res 1(4):743Google Scholar
  11. 11.
    Majumder H, Saha A (2018) Application of MCDM based hybrid optimization tool during turning of ASTM A588. Decis Sci Lett 7(2):143–156CrossRefGoogle Scholar
  12. 12.
    Gadakh V, Shinde VB, Khemnar N (2013) Optimization of welding process parameters using MOORA method. Int J Adv Manuf Technol 69(9–12):2031–2039CrossRefGoogle Scholar
  13. 13.
    Saha A, Mondal SC (2017) Multi-objective optimization of manual metal arc welding process parameters for nano-structured hardfacing material using hybrid approach. Measurement 102:80–89CrossRefGoogle Scholar
  14. 14.
    Saha A, Mondal SC (2017) Multi-objective optimization of welding parameters in MMAW for nano-structured hardfacing material using GRA coupled with PCA. Trans Indian Inst Met 70(6):1491–1502CrossRefGoogle Scholar
  15. 15.
    Adalarasan R, Santhanakumar M, Sundaram AS (2014) Optimization of weld characteristics of friction welded AA 6061–AA 6351 joints using grey-principal component analysis (G-PCA). J Mech Sci Technol 28(1):301–307CrossRefGoogle Scholar
  16. 16.
    Adalarasan R, Sundaram AS (2015) Parameter design in friction welding of Al/SiC/Al2O3 composite using grey theory based principal component analysis (GT-PCA). J Braz Soc Mech Sci Eng 37(5):1515–1528CrossRefGoogle Scholar
  17. 17.
    Paiva AP, Ferreira JR, Balestrassi PP (2007) A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization. J Mater Process Technol 189(1):26–35CrossRefGoogle Scholar
  18. 18.
    Yih-Fong T, Fu-Chen C (2006) Multiobjective process optimisation for turning of tool steels. Int J Mach Mach Mater 1(1):76–93Google Scholar
  19. 19.
    Saha A, Mondal SC (2016) Multi-objective optimization in WEDM process of nanostructured hardfacing materials through hybrid techniques. Measurement 94:46–59CrossRefGoogle Scholar
  20. 20.
    Rao TB, Krishna AG (2013) Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiCp MMC. Adv Manuf 1(3):265–275MathSciNetCrossRefGoogle Scholar
  21. 21.
    Saha A, Mondal SC (2018) Multi-criteria selection of optimal welding parameter in MMAW hardfacing using MOORA method coupled with PCA. Int J Mater Prod Technol 57(1–3):240–255CrossRefGoogle Scholar
  22. 22.
    Saha A, Mondal SC (2019) Statistical analysis and optimization of process parameters in wire cut machining of welded nanostructured hardfacing material. Silicon 11:1313–1326. CrossRefGoogle Scholar
  23. 23.
    Saha A, Mondal SC (2017) Machining optimization of nano-structured hardfaced tool insert in WEDM using MOORA method. In: International conference on research into design. Springer, BerlinCrossRefGoogle Scholar
  24. 24.
    Saha A, Mondal SC (2017) Welding parameters optimization in MMAW assisted nano-structured hardfacing using desirability function analysis embedded with Taguchi method. In: International conference on research into design. Springer, BerlinCrossRefGoogle Scholar
  25. 25.
    Ubaid AM et al (2017) Optimization of EDM process parameters with fuzzy logic for Stainless Steel 304 (ASTM A240)Google Scholar
  26. 26.
    Chandramouli S, Eswaraiah K (2017) Optimization of EDM process parameters in machining of 17-4 PH steel using Taguchi method. Mater Today Proc 4(2):2040–2047CrossRefGoogle Scholar
  27. 27.
    Tomadi S et al (2009) Analysis of the influence of EDM parameters on surface quality, material removal rate and electrode wear of tungsten carbide. In: Proceedings of the international multiconference of engineers and computer scientistsGoogle Scholar
  28. 28.
    Shashikant AKR, Kumar K (2014) Optimization of machine process parameters on material removal rate in EDM for EN19 material using RSM. IOSR J Mech Civ Eng 2320:24–28Google Scholar
  29. 29.
    Nikalje A, Kumar A, Srinadh KS (2013) Influence of parameters and optimization of EDM performance measures on MDN 300 steel using Taguchi method. Int J Adv Manuf Technol 69(1–4):41–49CrossRefGoogle Scholar
  30. 30.
    Brauers WK (2004) Optimization methods for, vol 342. Kluwer Academic Publishers, BostonzbMATHGoogle Scholar
  31. 31.
    Brauers WK, Zavadskas EK (2009) Robustness of the multi-objective MOORA method with a test for the facilities sector. Technol Econ Dev Econ 15(2):352–375CrossRefGoogle Scholar
  32. 32.
    Karande P, Chakraborty S (2012) Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Mater Des 37:317–324CrossRefGoogle Scholar
  33. 33.
    Stanujkic D et al (2012) An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technol Econ Dev Econ 18(2):331–363CrossRefGoogle Scholar
  34. 34.
    Tansel İç Y, Yıldırım S (2013) MOORA-based Taguchi optimisation for improving product or process quality. Int J Prod Res 51(11):3321–3341CrossRefGoogle Scholar
  35. 35.
    Siahaan APU, Rahim R, Mesran M (2017) Student admission assessment using multi-objective optimization on the basis of ratio analysisGoogle Scholar
  36. 36.
    Pearson K (1901) LIII. On lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 2(11):559–572CrossRefGoogle Scholar
  37. 37.
    Majumder H et al (2017) Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement 107:19–30CrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Production Engineering DepartmentNational Institute of Technology, AgartalaAgartalaIndia
  2. 2.Production Engineering DepartmentHaldia Institute of TechnologyHaldiaIndia

Personalised recommendations