Diametral deviation and tool wear analysis in EDM of Fe-based shape memory alloy: An experimental study with microstructural analysis and advanced optimization
Restricted accessResearch articleFirst published online December, 2022
Diametral deviation and tool wear analysis in EDM of Fe-based shape memory alloy: An experimental study with microstructural analysis and advanced optimization
Shape memory alloys (SMAs) are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft components, military instruments, fabricating elements, and bio-medical devices, among others. Despite their remarkable characteristics, effective production of these alloys continues to be a problem for researchers worldwide. This paper has been aimed to examine the considered machining responses that is, tool wear rate (TWR) and diametral deviation (DD) in electrical discharge machining (EDM) of Fe-based shape memory alloy (SMA) using a Cu-electrode under the varying settings of input factors namely as; pulse on time (Ton), pulse off time (Toff), peak current (Ip), and gap voltage (GV). The central composite design matrix has been employed for planning the main runs. The experimental results reflect the lowest and highest for TWR as; 0.0694 and 0.1357 g/min; and for DD as; 0.02 and 0.19 mm, respectively. The microstructure analysis of the EDMed work samples and electrode surfaces have also been conducted using scanning electron microscopy (SEM). The micrographs exposed the development of debris, craters, micro-cracks, and recast layer creation on the workpiece surface and electrode tool as well. The creation of debris develops as a result of large spark energy at the work piece-tool contact as a result of a high peak current and long pulse on time. Furthermore, single and multi-objective optimization of investigated responses (i.e. TWR and DD) were tried using the desirability approach, Genetic Algorithm (GA), and teacher learning based optimization (TLBO), techniques. The obtained multi-response optimized values for TWR and DD by using desirability approach, Genetic algorithm (GA), and Teacher Learning Based Optimization (TLBO) are as; 0.0815 g/min and 0.0527 mm; 0.1529 g/min and 0.2547 mm; 0.1675 g/min and 0.2533 mm., correspondingly.
Mohd JaniJLearyMSubicA, et al. A review of shape memory alloy research, applications and opportunities. Mater Des2014; 56: 1078–1113.
2.
SunLHuangWMDingZ, et al. Stimulus-responsive shape memory materials: a review. Mater Des2012; 33: 577–640.
3.
SinghRSinghRPTrehanR. State of the art in processing of shape memory alloys with electrical discharge machining: a review. Proc IMechE, Part B: J Engineering Manufacture2021; 235(3): 333–366.
4.
SinghRPSinghalS. Investigation of machining characteristics in rotary ultrasonic machining of alumina ceramic. Mater Manuf Process2017; 32(3): 309–326.
5.
TosunNCogunC. Analysis of wire erosion and workpiece surface roughness in wire electrical discharge machining. Proc IMechE, Part B: J Engineering Manufacture2003; 217(5): 633–642.
6.
JainSParasharV. Critical review on the impact of EDM process on biomedical materials. Mater Manuf Process2021; 36: 1701–1724.
7.
KongQLiuGFanX, et al. A nested circle-fitting method for micro-hole centering and its application in electrochemical deburring. Proc IMechE, Part B: J Engineering Manufacture2019; 233(12): 2292–2305.
8.
SinghRSinghRPTrehanR. Parametric investigation of tool wear rate in EDM of Fe-based shape memory alloy: microstructural analysis and optimization using genetic algorithm. World J Eng2022; 19: 418–428.
9.
SinghRDvivediAKumarP. EDM of high aspect ratio micro-holes on Ti-6Al-4V alloy by synchronizing energy interactions. Mater Manuf Process2020; 35(11): 1188–1203.
10.
Abu QudeiriJEMouradAHIZioutA, et al. Electric discharge machining of titanium and its alloys: review. Int J Adv Manuf Technol2018; 96(1–4): 1319–1339.
11.
SharmaNRajTJangraKK. Parameter optimization and experimental study on wire electrical discharge machining of porous Ni40Ti60 alloy. Proc IMechE, Part B: J Engineering Manufacture2017; 231(6): 956–970.
12.
MandalPMondalSC. Surface characteristics of mild steel using EDM with Cu-MWCNT composite electrode. Mater Manuf Process2019; 34(12): 1326–1332.
13.
AlidoostiAGhafari-NazariAMoztarzadehF, et al. Electrical discharge machining characteristics of nickel–titanium shape memory alloy based on full factorial design. J Intell Mater Syst Struct2013; 24(13): 1546–1556.
14.
AbidiMHAl-AhmariAMUmerU, et al. Multi-objective optimization of micro-electrical discharge machining of nickel-titanium-based shape memory alloy using MOGA-II. Measurement2018; 125: 336–349.
15.
GaikwadMUKrishnamoorthyAJattiVS. Investigation and optimization of process parameters in electrical discharge machining (EDM) process for NiTi 60. Mater Res Express2019; 6(6): 065707.
16.
DaneshmandSKahriziEFAbediE, et al. Influence of machining parameters on electro discharge machining of NiTi shape memory alloys. Int J Electro Sci2013; 8(3): 3095–3104.
17.
GaikwadVJattiVS. Optimization of material removal rate during electrical discharge machining of cryo-treated NiTi alloys using Taguchi’s method. J King Saud Univ Eng Sci2018; 30(3): 266–272.
18.
ÇakıroğluRGünayM. Comprehensive analysis of material removal rate, tool wear and surface roughness in electrical discharge turning of L2 tool steel. J Mater Res Technol2020; 9(4): 7305–7317.
19.
GünayMKorkmazMEYaşarN. Performance analysis of coated carbide tool in turning of Nimonic 80A superalloy under different cutting environments. J Manuf Process2020; 56: 678–687.
20.
GünayMMeralT. Modelling and multiresponse optimization for minimizing burr height, thrust force and surface roughness in drilling of ferritic stainless steel. Sādhanā2020; 45(1): 1–10.
21.
YurtkuranH. An evaluation on machinability characteristics of titanium and nickel based superalloys used in aerospace industry. İmalat Teknolojileri ve Uygulamaları2021; 2(2): 10–29.
22.
DwivediAPChoudhurySK. Effect of tool rotation on MRR, TWR, and surface integrity of AISI-D3 steel using the rotary EDM process. Mater Manuf Process2016; 31(14): 1844–1852.
23.
ManjaiahMNarendranathSBasavarajappaS, et al. Investigation on material removal rate, surface and subsurface characteristics in wire electro discharge machining of Ti50Ni50-xCux shape memory alloy. Proc IMechE, Part L: J Materials: Design and Applications2018; 232(2): 164–177.
24.
NarendranathSManjaiahMBasavarajappaS, et al. Experimental investigations on performance characteristics in wire electro discharge machining of Ti50Ni42.4Cu7.6 shape memory alloy. Proc IMechE, Part B: J Engineering Manufacture2013; 227(8): 1180–1187.
25.
SharmaNGuptaKDavimJP. On wire spark erosion machining induced surface integrity of Ni55.8Ti shape memory alloys. Arch Civil Mech Eng2019; 19(3): 680–693.
26.
DavimJP (ed.). Statistical and computational techniques in manufacturing. Berlin: Springer Science & Business Media, 2012.
27.
PawarPJRaoRVDavimJP. Multiobjective optimization of grinding process parameters using particle swarm optimization algorithm. Mater Manuf Process2010; 25(6): 424–431.
28.
MagabeRSharmaNGuptaK, et al. Modeling and optimization of Wire-EDM parameters for machining of Ni55.8Ti shape memory alloy using hybrid approach of Taguchi and NSGA-II. Int J Adv Manuf Technol2019; 102(5): 1703–1717.
29.
DavimJP (ed.). Machining: fundamentals and recent advances. London: Springer Science & Business Media, 2008.
30.
SahooPDasSK. Tribology—a tool for mechanical and industrial engineering. In: DavimJP (ed.) Mechanical and industrial engineering. Cham: Springer, 2022, pp.1–37.
31.
DavimJP. Surface integrity in machining. London: Springer, 2010.
32.
WaliaASSrivastavaVRanaPS, et al. Prediction of tool shape in electrical discharge machining of EN31 steel using machine learning techniques. Metals2021; 11(11): 1668.
33.
Patel Gowdru ChandrashekarappaMKumarSJagadishJ, et al. Experimental analysis and optimization of EDM parameters on HcHcr steel in context with different electrodes and dielectric fluids using hybrid Taguchi-based PCA-utility and CRITIC-utility approaches. Metals2021; 11(3): 419.
34.
PrakashCSinghSPruncuCI, et al. Surface modification of Ti-6Al-4V alloy by electrical discharge coating process using partially sintered Ti-Nb electrode. Materials2019; 12(7): 1006.
35.
ZhaoYZGuoKSivalingamV, et al. Surface integrity evolution of machined NiTi shape memory alloys after turning process. Adv Manuf2021; 9: 446–456.
36.
DavisRSinghADebnathK, et al. Surface modification of medical-grade Ni55.6Ti44.4 alloy via enhanced machining characteristics of Zn powder mixed-μ-EDM. Surf Coat Technol2021; 425: 127725.
37.
BalasubramaniyanCRajkumarKSantoshS. Wire-EDM machinability investigation on quaternary Ni44Ti50Cu4Zr2 shape memory alloy. Mater Manuf Process2021; 36(10): 1–10.
38.
JattiVS. Multi-characteristics optimization in EDM of NiTi alloy, NiCu alloy and BeCu alloy using Taguchi’s approach and utility concept. Alex Eng J2018; 57(4): 2807–2817.
39.
BeckRJAspinwallDKSooSL, et al. Fatigue performance of surface ground and wire electrical discharge machined TiNi shape memory alloy. Proc IMechE, Part B: J Engineering Manufacture2022; 236: 355–362.
40.
ZhouHDingWFLiZ, et al. Predicting the grinding force of titanium matrix composites using the genetic algorithm optimizing back-propagation neural network model. Proc IMechE, Part B: J Engineering Manufacture2019; 233(4): 1157–1167.
41.
LeeseRIvanovA. Electrochemical micromachining: review of factors affecting the process applicability in micro-manufacturing. Proc IMechE, Part B: J Engineering Manufacture2018; 232(2): 195–207.
42.
KiranPMohantySDasAK. Surface modification through sustainable micro-EDM process using powder mixed bio-dielectrics. Mater Manuf Process2022; 37: 640–651.
43.
ValakiJBRathodPPKhatriBC. Environmental impact, personnel health and operational safety aspects of electric discharge machining: a review. Proc IMechE, Part B: J Engineering Manufacture2015; 229(9): 1481–1491.
44.
SharmaNGuptaK. Wire spark erosion machining of Ni rich NiTi shape memory alloy for bio-medical applications. Procedia Manuf2019; 35: 401–406.
45.
Al-AhmariAMARasheedMSMohammedMK, et al. A hybrid machining process combining micro-EDM and laser beam machining of nickel–titanium-based shape memory alloy. Mater Manuf Process2020; 13(3): 447–455.
46.
PrakashCKansalHKPablaB, et al. Electric discharge machining – a potential choice for surface modification of metallic implants for orthopedic applications: a review. Proc IMechE, Part B: J Engineering Manufacture2016; 230(2): 331–353.
47.
BisariaHShandilyaP. The machining characteristics and surface integrity of Ni-rich NiTi shape memory alloy using wire electric discharge machining. Proc IMechE, Part C: J Mechanical Engineering Science2019; 233(3): 1068–1078.
48.
RaoRVPawarPJ. Modelling and optimization of process parameters of wire electrical discharge machining. Proc IMechE, Part B: J Engineering Manufacture2009; 223: 1431–1440.
49.
TakaleAChouguleN. Optimization of process parameters of wire electro discharge machining for Ti49.4Ni50.6 shape memory alloys using the Taguchi technique. Int J Struct Integr2019; 10(4): 548–568.
50.
DangXP. Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm. Mater Manuf Process2018; 33(4): 397–404.
51.
QuartoMD’UrsoGGiardiniC, et al. A comparison between finite element model (FEM) simulation and an Integrated Artificial Neural Network (ANN)-Particle swarm optimization (PSO) approach to forecast performances of Micro Electro Discharge Machining (Micro-EDM) drilling. Micromachines2021; 12(6): 667.
52.
JafferySHIKhanMAliL, et al. Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy. Proc IMechE, Part B: J Engineering Manufacture2016; 230(6): 1017–1034.
53.
SrinivasanVPPalaniPKBalamuruganS. Experimental investigation on EDM of Si3N4–TiN using grey relational analysis coupled with teaching-learning-based optimization algorithm. Ceram Int2021; 47(13): 19153–19168.
54.
ShahaliHYazdiMRSMohammadiA, et al. Optimization of surface roughness and thickness of white layer in wire electrical discharge machining of DIN 1.4542 stainless steel using micro-genetic algorithm and signal to noise ratio techniques. Proc IMechE, Part B: J Engineering Manufacture2012; 226(5): 803–812.
55.
BisariaHShandilyaP. Study on crater depth during material removal in WEDC of Ni-rich nickel–titanium shape memory alloy. J Braz Soc Mech Sci Eng2019; 41(3): 157.
56.
PradhanMKDasR. Recurrent neural network estimation of material removal rate in electrical discharge machining of AISI D2 tool steel. Proc IMechE, Part B: J Engineering Manufacture2011; 225(3): 414–421.
