Engineering and Technology Journal

Integrating Grey Relation Analysis and Artificial Neural Networks for Optimal Machining of Tungsten Carbide Composite Using Hybrid Electrochemical Discharge

Document Type : Research Paper

Authors

Production Engineering and Metallurgy Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

Abstract

Hybrid Electrochemical Discharge Machining (ECDM) streamlines the manufacturing process for challenging materials. Tungsten carbide (WC) is widely recognized as a formidable material due to its exceptional hardness and its ability to maintain hardness even at elevated temperatures. This study has conducted a comprehensive investigation of the multi-response optimization of ECDM process parameters to enhance the machining of tungsten carbides, utilizing Grey Relation Analysis (GRA) and Artificial Neural Network (ANN) methods. This optimization's primary objective is to achieve the maximum Material Removal Rate (MRR) and machining depth. The study involved a systematically designed experiment based on the Taguchi design method. Scanning Electron Microscopy (SEM) analysis reveals shallow craters, minimal microcracks, and small melt re-deposits on the machined surfaces, elucidating the smooth surface achieved after ECD machining. The average surface roughness achieved in this study, utilizing Electrochemical Discharge Machining (ECDM) for tungsten, measured at 0.9275 µm. Optimal parameters were determined, including a current of 80 A, a stand-off distance of 0.1 mm, a 30 mm gap, and an electrolyte composed of KCl + KOH for machining tungsten carbide. The results of the Analysis of Variance (ANOVA) indicate that electrolyte concentration has the most significant impact on machining depth and material removal rate (50.55%), followed by the current value (31.32%). Additionally, the ANN results aligned closely with those obtained through GRA. Compositional analysis of the surface using Energy-Dispersive X-ray Spectroscopy (EDS) mapping confirms the presence of oxides and carbon on the machined surface.

Graphical Abstract

Highlights

  • This study implements the machining of tungsten carbide using hybrid electrochemical discharge machining
  • Grey relation analysis (GRA) and artificial neural network (ANN) approaches are utilized for optimization
  • The maximum material removal rate and highest machining depth are attained by applying the optimum parameters
  • The electrolyte concentration affects material removal rate and machining depth most, followed by current.

Keywords

Main Subjects

References
  1. Tang, X. Kang, W. Zhao, J. Qian, and B. Lauwers, Discharge Characteristics in Electrochemical Discharge Machining of Ceramic-coated Ni-superalloy, Procedia CIRP, 95 (737–742) 2020. https://doi.org/10.1016/j.procir.2020.02.307
  2. V. Vani, S.K.Chak, A Review on Electro Chemical Discharge Machining (Ecdm) of Al-Based Metal Matrix Composites (Mmc), Int.J. Mech. Prod. Eng., 4 (2016) 128-134.
  3. K. K. R. Mediliyegedara, A. K. M. De Silva, D. K. Harrison, and J. A. McGeough, An intelligent pulse classification system for electro-chemical discharge machining (ECDM) - A preliminary study, J. Mater. Process. Technol., 149 (2004) 499–503. https://doi.org/10.1016/j.jmatprotec.2004.04.002
  4. K. Chak, Electro Chemical Discharge Machining: Process Capabilities, Int. J. Mech. Prod. Eng., 4 (2016) 135–146.
  5. P. Study, Synthesis and Sintering of Tungsten and Titanium Carbide: A Parametric Study, 2022.
  6. Janmanee and S. Kumjing, A study of tungsten carbide surfaces during the electrical discharge machining using artificial neural network model, Int. J. Appl. Eng. Res., 12 ( 2017) 3214–3227.
  7. Fadhil Ibrahim, M. Adel Abdullah, and S. Kadhim Ghazi, Prediction of Surface Roughness and Material Removal Rate for 7024 AL-Alloy in EDM Process, Eng. Technol. J., 34 (2016) 2796–2804. https://doi.org/10.30684/etj.34.15a.2
  8. K. Shather, Prediction of Particle size and Material Removal Rate of zinc oxide, February, 2020.
  9. Kumar, J. S. Tiwana, and A. Singla, Optimization of the machining parameters for EDM wire cutting of Tungsten Carbide, MATEC Web Conf., 57 (2016)1–4. https://doi.org/10.1051/matecconf/20165703006
  10. Equbal and A. K. Sood, Electrical Discharge Machining: An Overview on Various Areas of Research, Manuf. Ind. Eng., 13 (2014) 1-6 . https://doi.org/10.12776/MIE.V13I1-2.339
  11. K. Shrivastava and A. K. Dubey, Electrical discharge machining-based hybrid machining processes: A review, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 228 (2014)799–825. https://doi.org/10.1177/0954405413508939
  12. A. Khan et al., A detailed machinability assessment of dc53 steel for die and mold industry through wire electric discharge machining, Metals (Basel) 11( 2021). https://doi.org/10.3390/met11050816
  13. U. Farooq, M. P. Mughal, N. Ahmed, N. A. Mufti, A. M. Al-Ahmari, and Y. He, On the investigation of surface integrity of Ti6Al4V ELI using si-mixed electric discharge machining, Materials (Basel)., 13 (2020) 1549. https://doi.org/10.3390/ma13071549
  14. Rafaqat et al., Hole-Making in D2-Grade Steel Tool by Electric-Discharge Machining through Non-Conventional Electrodes, Processes, 10 (1–24) 2022. https://doi.org/10.3390/pr10081553
  15. U. Farooq et al., A Novel Flushing Mechanism to Minimize Roughness and Dimensional Errors during Wire Electric Discharge Machining of Complex Profiles on Inconel 718, Materials (Basel)., 15 (2022). https://doi.org/10.3390/ma15207330
  16. Shibuya, Y. Ito, and W. Natsu, Electrochemical machining of tungsten carbide alloy micro-pin with NaNO3 solution, Int. J. Precis. Eng. Manuf., 13 (2012) 2075–2078 2. https://doi.org/10.1007/s12541-012-0273-2
  17. Zander, A. Schupp, O. Beyss, B. Rommes, and A. Klink, Oxide formation during transpassive material removal of martensitic 42CrMo4 steel by electrochemical machining, Materials (Basel)., 14 (2021) 1–16. https://doi.org/10.3390/ma14020402
  18. hassan Abdel-Gawad El-Hofy, advanced machining processes n0ntraditional and hybrid machining processes, 4,2005.
  19. S. H and B. R. I, Parametric optimization of Electro Chemical Spark Machining using Taguchi based Grey Relational Analysis, IOSR J. Mech. Civ. Eng., (2012) 46–52.
  20. Durairaj, D. Sudharsun, and N. Swamynathan, Analysis of process parameters in wire EDM with stainless steel using single objective Taguchi method and multi objective grey relational grade, Procedia Eng., 64 (2013) 868–877. https://doi.org/10.1016/j.proeng.2013.09.163
  21. Manikandan, S. Kumanan, and C. Sathiyanarayanan, Engineering Science and Technology , an International Journal Multiple performance optimization of electrochemical drilling of Inconel 625 using Taguchi based Grey Relational Analysis, Eng. Sci. Technol. an Int. J., 20 (2017) 662–671. https://doi.org/10.1016/j.jestch.2016.12.002
  22. Kumar, H. Bishwakarma, P. Sharma, P. K. Singh, and A. K. Das, Optimization of process parameters in micro-electrochemical discharge machining by using grey relational analysis, Mater. Sci. Forum, 978 (2020) 121–132. https://doi.org/10.4028/www.scientific.net/MSF.978.121
  23. Pawar, A. Kumar, and R. Ballav, Grey relational analysis optimization of input parameters for electrochemical discharge drilling of silicon carbide by gunmetal tool electrode, Ann. Chim. Sci. des Mater., 44 (2020) 239–249. https://doi.org/10.18280/acsm.440402
  24. A. Hammood, Optimization of Cutting Parameters for Milling Process of ( 4032 ) Al-Alloy using Taguchi-Based Grey Relational Analysis, 17 (2021) 1–12.
  25. Tamiloli, J. V.enkatesan, B.V. Ramnath, A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert, Measurement, 84 (2016) 68–82. https://doi.org/10.1016/j.measurement.2016.02.008
  26. Paul and S. S. Hiremath, Evaluation of Process Parameters of ECDM Using Grey Relational Analysis, Procedia Mater. Sci., 5 (2014) 2273–2282. https://doi.org/10.1016/j.mspro.2014.07.446
  27. N. Haq, P. Marimuthu, and R. Jeyapaul, Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method, Int. J. Adv. Manuf. Technol., 37 (2008) 250–255. https://doi.org/10.1007/s00170-007-0981-4
  28. Rajput, S. S. Pundir, M. Goud, and N. M. Suri, Multi-Response Optimization of ECDM Parameters for Silica (Quartz) Using Grey Relational Analysis, Silicon, 13 (2021) 1619–1640. https://doi.org/10.1007/s12633-020-00538-7
  29. U. Farooq, S. Anwar, and A. Hurairah, Reducing micro-machining errors during electric discharge machining of titanium alloy using nonionic liquids, Mater. Manuf. Process., 38 (2023) 1–16. https://doi.org/10.1080/10426914.2023.2236199
  30. W. Liu, T. M. Yue, and Z. N. Guo, An analysis of the discharge mechanism in electrochemical discharge machining of particulate reinforced metal matrix composites, Int. J. Mach. Tools Manuf., 50 (2010) 86–96. https://doi.org/10.1016/j.ijmachtools.2009.09.004
  31. R. Kolhekar and M. Sundaram, Study of gas film characterization and its effect in electrochemical discharge machining, Precis. Eng., 53 (2018) 203–211. https://doi.org/10.1016/j.precisioneng.2018.04.002
  32. GENG and Z. XU, Electrochemical discharge machining for fabricating holes in conductive materials: A review, J. Adv. Manuf. Sci. Technol., 1(2021) 2021006. https://doi.org/10.51393/j.jamst.2021006
  33. G. Dileepkumar, K. N. Bharath, B. C. Naveen, and A. Kumar, A Study of Response Parameters during ECDM on Quartz Material, IOP Conf. Ser. Mater. Sci. Eng., 925 ((2020). https://doi.org/10.1088/1757-899X/925/1/012053
  34. Sharma and S. Kumar, Experimental research to Optimize Process Parameters in Machining of Non Conducting Material with hybrid non conconventional machining, Int. J. Trend Sci. Res. Dev., 1(2017) . https://doi.org/10.31142/ijtsrd84
  35. Kumar Kasdekar, V. Parashar, and C. Arya, Artificial neural network models for the prediction of MRR in Electro-chemical machining, Mater. Today Proc., 5 (2018) 772–779. https://doi.org/10.1016/j.matpr.2017.11.146
  36. P. Mughal et al., Surface modification for osseointegration of Ti6Al4V ELI using powder mixed sinking EDM, J. Mech. Behav. Biomed. Mater., 113 (2021) 104145. https://doi.org/10.1016/j.jmbbm.2020.104145
  37. Kumar and P. Rawat, Investigating the Effects of Wire Electric Discharge Machining Parameters on MRR and Surface Integrity in Machining of Tungsten Carbide Cobalt ( WC-24% Co ) Composite Material, 2 (2015) 29–35.
  38. U. Farooq et al., Curved profiles machining of Ti6Al4V alloy through WEDM: investigations on geometrical errors, J. Mater. Res. Technol., 9 (2020) 16186–16201. https://doi.org/10.1016/j.jmrt.2020.11.067
  39. H. Lee and X. Li, Study of the surface integrity of the machined workpiece in the EDM of tungsten carbide, J. Mater. Process. Technol., 139 (2003) 315–321. https://doi.org/10.1016/S0924-0136(03)00547-8
  40. B. Toparli, Wire-EDM Cutting Strategies of WC-Co Hardmetals : Effect of Number of EDM Pass on Surface Integrity Wire-EDM Cutting Strategies of WC-Co Hardmetals. (2022) 1–18. https://doi.org/10.21203/rs.3.rs-1664872/v1
Engineering and Technology Journal
Volume 41, Issue 12
Production & Metallurgy Engineering
18 articles
December 2023
Page 1594-1610
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