Experimental investigation and optimization of machining performance characteristics during WEDM of inconel 718: On evaluation of wire electrodes and advanced parameter techniques
Restricted accessResearch articleFirst published online 2021
Experimental investigation and optimization of machining performance characteristics during WEDM of inconel 718: On evaluation of wire electrodes and advanced parameter techniques
Wire Electrical-Discharge-Machining (WEDM) is a well-known unconventional machining process to produce intricate shapes. However, obtaining a satisfactory WEDM cutting performance is indeed a challenging task during precision cutting. Hence, this investigation aims to attempt a favorable machining parameter setting in order to corner-cutting during WEDM for In-718. Here, machining performance characteristics have been considered based on corner deviation (CD) along with Material Removal Rate (MRR) and surface roughness (SR). Taguchi’s experiment design technique (L16) has been considered to run the experiments. The controllable process parameters are considered as Spark-on-time (Son), flushing-pressure (Fp), wire-tension (Tw), and discharge-current (Id). The aforesaid machining performance characteristics have been achieved through the two most popular wire electrodes, i.e., Zinc-coated brass electrode (Zn-BE) and Brass Wire Electrode (BWE), and compared the results. The comparison of performances by the wire electrodes on CD, MRR, and SR varied from 0.0286 mm to 0.0844 mm, 0.0045 g/min to 0.0214 g/min and 3.12 µm to 4.80 µm for BWE and 0.0218 mm to 0.0783 mm, 0.0090 g/min to 0.0342 g/min and 2.58 µm to 4.40 µm for Zn-BE respectively. However, machining with Zn-WE yields reduced CD, SR, and increased MRR value and shows less defect on the WEDMed surfaces than its counterpart. The present study developed the mathematical model based on non-linear regression for correlating the machining parameters with the machining responses. The next step of this study is that a unique optimization strategy, namely grey relation analysis (GRA) integrated with Teaching Learning-Based Optimization (TLBO), has been implemented for achieving optimal parametric setting. The satisfactory machining setting obtained from GRA-TLBO has been compared with GRA-JAYA and GRA-genetic algorithm (GA). The proposed methodology appears more fruitful in terms of computational time and effort.
AggarwalVPruncuCISinghJ, et al.
Empirical investigations during WEDM of Ni-27Cu-3.15 Al-2Fe-1.5 Mn based superalloy for high temperature corrosion resistance applications. Materials2020;
13: 3470.
2.
SenBHussainSAIGuptaAD, et al.
Application of type-2 fuzzy AHP-ARAS for selecting optimal WEDM parameters. Metals2020;
11: 42.
3.
DodunOGonçalves-CoelhoAMSlătineanuL, et al.
Using wire electrical discharge machining for improved corner cutting accuracy of thin parts. Int J Adv Manuf Technol2009;
41: 858–864.
4.
BisariaHShandilyaP.Processing of curved profiles on Ni-rich nickel–titanium shape memory alloy by WEDM. Mater Manuf Proc2019;
34: 1333–1341.
5.
SelvakumarGSarkarSMitraS.Experimental investigation on die corner accuracy for wire electrical discharge machining of monel 400 alloy. Proc IMechE, Part B: J Engineering Manufacture2012;
226: 1694–1704.
6.
PuriABhattacharyyaB.An analysis and optimisation of the geometrical inaccuracy due to wire lag phenomenon in WEDM. Int J Mach Tools Manuf2003;
43: 151–159.
7.
SanchezJARodilJLHerreroA, et al.
On the influence of cutting speed limitation on the accuracy of wire-EDM corner-cutting. J Mater Process Technol2007;
182: 574–579.
8.
SelvakumarGThiruppathi KuttalingamKGSelvarajM, et al.
Enhancing die corner accuracy using path modification strategy in wire electrical discharge machining of monel 400. Proc IMechE, Part C: J Mechanical Engineering Science2018;
232: 207–216.
9.
SelvakumarGBalasubramanianVLeninN.Investigation on corner accuracy in wire cut EDM of AISI D3 tool steel. Int J Rapid Manuf2020;
9: 58–70.
10.
IshfaqKAhmedNRehmanAU, et al.
WEDM of AA6061: an insight investigation of axial and lateral dimensional errors. Mater Manuf Proc2020;
35: 762–713.
11.
SunkaraJK, et al.
Experimental investigation on precision machining of multiple holes by WEDM on aluminium-6061 using genetic algorithm. Multiscale and Multidisciplinary Modeling, Experiments and Design2019; 3: 1–12.
12.
YanM-THuangP-H.Accuracy improvement of wire-EDM by real-time wire tension control. Int J Mach Tools Manuf2004;
44: 807–814.
13.
SanchezJALópez de LacalleLNLamikizA.A computer-aided system for the optimization of the accuracy of the wire electro-discharge machining process. Int J Comput Integr Manuf2004;
17: 413–420.
14.
LinC-TChungI-FHuangS-Y.Improvement of machining accuracy by fuzzy logic at corner parts for wire-EDM. Fuzzy Sets Syst2001;
122: 499–511.
15.
JadamTDattaSMasantaM.Study of surface integrity and machining performance during main/rough cut and trim/finish cut mode of WEDM on Ti–6Al–4V: effects of wire material. J Braz Soc Mech Sci Eng2019;
41: 151.
16.
AntarMTSooSLAspinwallDK, et al.
Productivity and workpiece surface integrity when WEDM aerospace alloys using coated wires. Proc Eng2011;
19: 3–8.
17.
KapoorJSinghSKhambaJS. Recent developments in wire electrodes for high performance WEDM. In: Proceedings of the world congress on engineering, 30 June – 2 July, 2010, London, U.K.
18.
PorośDZaborskiS.Semi-empirical model of efficiency of wire electrical discharge machining of hard-to-machine materials. J Mater Process Technol2009;
209: 1247–1253.
19.
KumarKVSaitANPanneerselvamK, et al.
Machinability study of hybrid-polymer composite pipe using response surface methodology and genetic algorithm. J Sandwich Struct Mater2014;
16: 418–439.
20.
SahooPJA.Optimization of turning parameters for surface roughness using RSM and GA. Adv Product Eng Manag2011;
6: 197–208.
21.
ShanthiJ, et al.
Optimization of variable control parameters of WEDM in aluminum alloy Al 7075 using zinc coated copper wire. J. TAGA2018;
14: 3053–3063.
22.
QiaoH.A systematic computer-aided approach to cooling system optimal design in plastic injection molding. Int J Mech Sci2006;
48: 430–439.
23.
SenthilkumarKMarimuthuP.Prediction of machining parameters for CNC turning of inconel X-750 using genetic algorithm. Int J Adv Engg Tech2016;
726: 728.
24.
ArulSSamuel RajDVijayaraghavanL, et al.
Modeling and optimization of process parameters for defect toleranced drilling of GFRP composites. Mater Manuf Proc2006;
21: 357–365.
25.
SayarshadHRGhoseiriK.A simulated annealing approach for the multi-periodic rail-car fleet sizing problem. Comput Operat Res2009;
36: 1789–1799.
26.
YangS-HSrinivasJMohanS, et al.
Optimization of electric discharge machining using simulated annealing. J Mater Process Techn2009;
209: 4471–4475.
27.
PawarPRaoRV.Parameter optimization of machining processes using teaching–learning-based optimization algorithm. Int J Adv Manuf Technol2013;
67: 995–1006.
28.
RaoRVKalyankarV.Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Sci Iran2013;
20: 967–974.
29.
RaoRVKalyankarV.Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng Appl Artif Intell2013;
26: 524–531.
30.
RaoRVSavsaniVJVakhariaD.Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inform Sci2012;
183: 1–15.
31.
MellalMAWilliamsEJ.Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic. J Intell Manuf2016;
27: 927–942.
32.
RaoRVMoreKCTalerJ, et al.
Dimensional optimization of a micro-channel heat sink using jaya algorithm. Appl Therm Eng2016;
103: 572–582.
33.
KumarAVivekanandaKAbhishekK.Experimental investigation and optimization of process parameter for inconel 718 using wire electrical discharge machining. J Adv Manuf Syst2019;
18: 339–362.
34.
YangRTTzengCJYangYK, et al.
Optimization of wire electrical discharge machining process parameters for cutting tungsten. Int J Adv Manuf Technol2012;
60: 135–147.
35.
PandeyRKPandaSJM.Optimization of bone drilling parameters using grey-based fuzzy algorithm. Measurement2014;
47: 386–392.
36.
RamamurthyASivaramakrishnanRMuthuramalingamT, et al.
Performance analysis of wire electrodes on machining Ti-6Al-4V alloy using electrical discharge machining process. Mach Sci Technol2015;
19: 577–592.
37.
AbhishekKRakesh KumarVDattaS, et al.
Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm.). J Intell Manuf2017;
28: 1769–1717.
38.
RaoRVSavsaniVJVakhariaD.Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aid Des2011;
43: 303–315.
39.
RaoRPatelV.An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput2012;
3: 535–560.
RaoR.Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput2016;
7: 19–34.
42.
RaoRVMoreKCTalerJ, et al.
Corrigendum to “dimensional optimization of a micro-channel heat sink using jaya algorithm. Appl Therm Eng2017;
122: 831.
43.
GhodsiyehDGolshanAShirvanehdehJA.Review on current research trends in wire electrical discharge machining (WEDM). Indian J Sci Technol2013;
6: 1–4140.
44.
GolshanAGhodsiyehDIzmanS.Multi-objective optimization of wire electrical discharge machining process using evolutionary computation method: effect of cutting variation. Proc IMechE, Part B: J Engineering Manufacture2014; 229(1): 0954405414523593.
45.
KuriakoseSShunmugamM.Characteristics of wire-electro discharge machined Ti6Al4V surface. Mater Lett2004;
58: 2231–2237.
46.
KumarAKumarVKumarJ.Microstructure analysis and material transformation of pure titanium and tool wear surface after wire electric discharge machining process. Mach Sci Technol2014;
18: 47–77.
47.
HuangCAHsuFYYaoSJ, et al.
Microstructure analysis of the martensitic stainless steel surface fine-cut by the wire electrode discharge machining (WEDM). Mater Sci Eng A2004;
371: 119–126.
48.
DeyAPandeyKM.Wire electrical discharge machining characteristics of AA6061/cenosphere as-cast aluminum matrix composites. Mater Manuf Proc2018;
33: 1346–1353.
49.
KaraFKöklüUKabasakaloğluUJMT.Taguchi optimization of surface roughness in grinding of cryogenically treated AISI 5140 steel. 2020;
62: 1041–1047.
50.
EserAAşkar AyyıldızEAyyıldızM, et al.
Artificial intelligence-based surface roughness estimation modelling for milling of AA6061 alloy. Adv Mater Sci Eng2021;
2021: 1–10.
51.
ÖztürkBKaraF.Calculation and estimation of surface roughness and energy consumption in milling of 6061 alloy. Adv Mater Sci Eng2020;
2020: 1–12.
