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Multi-response optimization of laser-assisted jet electrochemical machining parameters based on gray relational analysis

  • Anup Malik
  • Alakesh Manna
Technical Paper

Abstract

The gray relational analysis is an important technique can be effectively used for forecasting, decision making in different areas of manufacturing and products processing. In this research, the gray relational analysis has been employed to optimize the input process parameters of the developed laser-assisted jet electrochemical machine (LA-JECM) for better machining performance characteristics. Taguchi method-based design of experiment L16 (44) orthogonal array was employed and experiments were carried out for investigation. The optimal parametric combination for multi-response optimization was identified based on the collective implementation of Taguchi methodology and gray relational analysis during microdrilling of Inconel-718. A LA-JECM has been developed and utilized for experimental investigation. The experimental results revealed that there is 29.16% increase in MRR; 48.43% decrease in taper and 36.83% reduction in surface roughness height, Ra (µm) when experiments were carried out on LA-JECM over JECM. The laser assistance with JECM improves the machining quality and reduces machining time. Taguchi methodology and gray relational analysis based multi-optimization found that the parametric setting, i.e., at supply voltage 80 V, electrolyte concentration 40 g/l, inter-electrode gap 3 mm, and duty cycle 60% gives maximum material removal rate with minimum taper angle and surface roughness height (Ra, µm) of the machined hole.

Keywords

Laser-assisted jet electrochemical machining Taguchi methodology Gray relational analysis Multi-response optimization 

Abbreviations

JECM

Jet electrochemical machining

LA-JECM

Laser-assisted jet electrochemical machining

VS

Supply voltage (X1, volt)

EC

Electrolyte concentration (X2, g/l)

IEG

Inter-electrode gap (X3, mm)

DC

Duty cycle (X4, %)

MRR

Material removal rate (mg/min)

TAP

Taper (degree)

Ra

Surface roughness height (µm)

Dof

Degree of freedom

References

  1. 1.
    Pajak PT, De Silva AKM, McGeough JA, Harrison DK (2004) Modelling the aspects of precision and efficiency in laser-assisted jet electrochemical machining (LAJECM). J Mater Process Technol 149:512–518.  https://doi.org/10.1016/j.jmatprotec.2003.10.055 CrossRefGoogle Scholar
  2. 2.
    Hua Z, Jiawen X (2010) Modeling and experimental investigation of laser drilling with jet electrochemical machining. Chinese J Aeronaut 23:454–460.  https://doi.org/10.1016/S1000-9361(09)60241-7 CrossRefGoogle Scholar
  3. 3.
    Zhang H, Xu J, Wang J (2009) Investigation of a novel hybrid process of laser drilling assisted with jet electrochemical machining. Opt Lasers Eng 47:1242–1249.  https://doi.org/10.1016/j.optlaseng.2009.05.009 CrossRefGoogle Scholar
  4. 4.
    Zhang Z, Cai M, Feng Q, Zeng Y (2014) Comparison of different laser-assisted electrochemical methods based on surface morphology characteristics. Int J Adv Manuf Technol 71:565–571.  https://doi.org/10.1007/s00170-013-5508-6 CrossRefGoogle Scholar
  5. 5.
    De Silva AKM, Pajak PT, McGeough JA, Harrison DK (2011) Thermal effects in laser-assisted jet electrochemical machining. CIRP Ann Manuf Technol 60:243–246.  https://doi.org/10.1016/j.cirp.2011.03.132 CrossRefGoogle Scholar
  6. 6.
    Mi D, Natsu W (2017) Design of ECM tool electrode with controlled conductive area ratio for holes with complex internal features. Precs Eng 47:54–61.  https://doi.org/10.1016/j.precisioneng.2016.07.004 CrossRefGoogle Scholar
  7. 7.
    Speidel A, Mitchell-Smith J, Walsh DA, Hirsch M, Clare A (2016) Electrolyte jet machining of titanium alloys using novel electrolyte solutions. Proc CIRP 42:367–372.  https://doi.org/10.1016/j.procir.2016.02.200 CrossRefGoogle Scholar
  8. 8.
    Mi D, Natsu W (2016) Simulation of Micro ECM for complex-shaped holes. Proc CIRP 42:345–349.  https://doi.org/10.1016/j.procir.2016.02.186 CrossRefGoogle Scholar
  9. 9.
    Kozak J, Zybura-Skrabalak M (2016) Some problems of surface roughness in electrochemical machining (ECM). Proc CIRP 42:101–106.  https://doi.org/10.1016/j.procir.2016.02.198 CrossRefGoogle Scholar
  10. 10.
    Natsu W, Ikeda T, Kunieda M (2007) Generating complicated surface with electrolyte jet machining. Precis Eng 31(1):33–39.  https://doi.org/10.1016/j.precisioneng.2006.02.004 CrossRefGoogle Scholar
  11. 11.
    Walker JC, Kamps TJ, Lam JW, Mitchell-Smith J, Clare AT (2017) Tribological behaviour of an electrochemical jet machined textured Al-Si automotive cylinder liner material. Wear 376–377:1611–1621.  https://doi.org/10.1016/j.wear.2017.01.085 CrossRefGoogle Scholar
  12. 12.
    Speodel A, Lutey AHA, Mitchell-Smith J, Rance GA, Liverani E, Ascari A, Fortunato A, Clare A (2016) Surface modification of mild steel using a combination of laser and electrochemical processes. Surf Coat Technol 307:849–860.  https://doi.org/10.1016/j.surfcoat.2016.09.077 CrossRefGoogle Scholar
  13. 13.
    Kawanaka T, Kunieda M (2015) Mirror-like finishing by electrolyte jet machining. CIRP Ann Manuf Technol 64(1):237–240.  https://doi.org/10.1016/j.cirp.2015.04.029 CrossRefGoogle Scholar
  14. 14.
    Chen X, Qu N, Li H, Xu Z (2015) Pulsed electrochemical micromachining for generating micro-dimple arrays on a cylindrical surface with a flexible mask. Appl Surf Sci 343:141–147.  https://doi.org/10.1016/j.apsusc.2015.03.087 CrossRefGoogle Scholar
  15. 15.
    Kalra CS, Kumar V, Manna A (2015) Analysis of electrochemical behavior on micro-drilling of cast hybrid Al/(Al2O3p + SiCp + Cp)-MMC using micro-ECM process. Proc Inst Mech Eng Part L J Mater Des Appl.  https://doi.org/10.1177/1464420715615907 Google Scholar
  16. 16.
    Nomura H, Mi D, Natsu W (2016) Fabrication and experimental verification of electrochemical machining tool for complex-shaped hole. Proc CIRP 42:117–120.  https://doi.org/10.1016/j.procir.2016.02.204 CrossRefGoogle Scholar
  17. 17.
    Kozak J, Rajurkar KP, Balkrishna R (1996) Study of electrochemical jet machining process. J Manuf Sci Eng 118(4):490–498.  https://doi.org/10.1115/1.2831058 CrossRefGoogle Scholar
  18. 18.
    Kawanaka T, Kato S, Kunieda M, Murray JW, Clare AT (2014) Selective surface texturing using electrolyte jet machining. Proc CIRP 13:345–349.  https://doi.org/10.1016/j.procir.2014.04.058 CrossRefGoogle Scholar
  19. 19.
    Kozak J, Rajurkar KP, Makkar Y (2004) Selected problems of micro-electrochemical machining. J Mater Process Technol 149(1–3):426–431.  https://doi.org/10.1016/j.jmatprotec.2004.02.031 CrossRefGoogle Scholar
  20. 20.
    Qu NS, Fang XL, Zhang YD, Zhu D (2013) Enhancement of surface roughness in electrochemical machining of Ti6Al4V by pulsating electrolyte. Int J Adv Manuf Technol 69:2703–2709.  https://doi.org/10.1007/s00170-013-5238-9 CrossRefGoogle Scholar
  21. 21.
    Adalarasan R, Santhanakumar M, Rajmohan M (2015) Application of Gray Taguchi-based response surface methodology (GT-RSM) for optimizing the plasma arc cutting parameters of 304L stainless steel. Int J Adv Manuf Technol 78:1161–1170.  https://doi.org/10.1007/s00170-014-6744-0 CrossRefGoogle Scholar
  22. 22.
    Singh S, Singh I, Dvivedi A (2013) Multi objective optimization in drilling of Al6063/10% SiC metal matrix composite based on gray relational analysis. J Eng Manuf 227:1767–1776.  https://doi.org/10.1177/0954405413494383 CrossRefGoogle Scholar
  23. 23.
    Azhiri B, Teimouri R, Ghasemi Baboly M, Leseman Z (2014) Application of Taguchi, ANFIS and gray relational analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int J Adv Manuf Technol 71:279–295.  https://doi.org/10.1007/s00170-013-5467-y CrossRefGoogle Scholar
  24. 24.
    Lu HS, Chang CK, Hwang NC, Chung CT (2009) gray relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling. J Mater Process Technol 209:3808–3817.  https://doi.org/10.1016/j.jmatprotec.2008.08.030 CrossRefGoogle Scholar
  25. 25.
    Dhuria GK, Singh R, Batish A (2016) Application of a hybrid Taguchi-entropy weight-based GRA method to optimize and neural network approach to predict the machining responses in ultrasonic machining of Ti-6Al-4V. J Brazilian Soc Mech Sci Eng 39(7):2619–2634.  https://doi.org/10.1007/s40430-016-0627-2 CrossRefGoogle Scholar
  26. 26.
    Kumar S, Kumar S (2015) Multi-response optimization of process parameters for friction stir welding of joining dissimilar Al alloys by gray relation analysis and Taguchi method. J Brazilian Soc Mech Sci Eng 37:665–674.  https://doi.org/10.1007/s40430-014-0195-2 CrossRefGoogle Scholar
  27. 27.
    Adalarasan R, Sundaram AS (2015) Parameter design in friction welding of Al/SiC/Al 2 O 3 composite using gray theory based principal component analysis (GT–PCA). J Brazilian Soc Mech Sci Eng 37:1515–1528.  https://doi.org/10.1007/s40430-014-0294-0 CrossRefGoogle Scholar
  28. 28.
    Deng JL (1989) Introduction to gray system theory. J Gray Syst 1:1–24zbMATHGoogle Scholar
  29. 29.
    Manna A (2013) Multi-response optimisation of machining parameters during drilling LM6Mg15SiC-Al-MMC based on gray relational analysis. Int J Mach Mach Mater 14:275–294.  https://doi.org/10.1504/IJMMM.2013.056368 Google Scholar
  30. 30.
    Patel KM, Pandey PM, Rao PV (2010) Optimisation of process parameters for multi-performance characteristics in EDM of Al 2 O 3 ceramic composite. Int J Adv Manuf Technol 47:1137–1147.  https://doi.org/10.1007/s00170-009-2249-7 CrossRefGoogle Scholar
  31. 31.
    Price G (1998) Thermodynamics of chemical processes. Oxford Science Publications, Oxford, United Kingdom, IsteditionGoogle Scholar
  32. 32.
    Phadke MS (1989) Quality engineering using robust design. Prentice-Hall, New JersyGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringLovely Professional UniversityPhagwaraIndia
  2. 2.Department of Mechanical EngineeringPEC University of TechnologyChandigarhIndia

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