Abstract
Electrical discharge machining (EDM) is one of the non-traditional machining processes characterized by its ability to machine parts that electrically conductive but difficult to be machined in the traditional machining processes due to its high hardness, complex geometry, and low tolerances. To minimize EDM process cost, ensure the highest process efficiency and achieve the highest product quality, the EDM process needs to be optimized. The aim of this research is optimizing EDM process parameters for machining HSS (AISI M2) material by use of copper electrode and brass electrode considering conflicting performance measures in one and multidimensional levels. The performance measures used in the current research are material removal rate (MRR) and electrode wear rate (EWR), while machining parameters that will subject to optimization process are current (A), pulse on (TON), and pulse off (TOFF). During optimization stages, the Taguchi method, signal to noise ratio (S/N ratio), and analysis of variances (ANOVA) will be used in the first stage to find the optimal machining parameters for each performance measure and for each electrode material. In the second stage, multidimensional optimization approach encompasses using the calculated S/N ratio as input from the first stage, fuzzy logic and ANOVA to calculate multi response performance index which will be used to select optimal machining parameters for each electrode and then select the best electrode and optimal machining parameters for machining AISI M2 steel. In one-dimensional optimization, for brass electrode, to maximize MRR value, the optimal machining parameters combination is A3TON1TOFF3 and to minimize EWR value, the optimal machining parameters combination is A1TON1TOFF2. For copper electrode, to maximize MRR value, the optimal machining parameters combination is A1TON1TOFF3 and to minimize EWR value, the optimal machining parameters combination is A1TON3TOFF1. In multidimensional optimization, for the copper electrode, the optimal machining parameters combination was A1TON1TOFF3 and for the brass electrode, the optimal machining parameters combination is A3TON1TOFF3. It could be concluded that EDM machining of HSS (AISI M2) by using the copper electrode will give the best results and the optimal machining parameters combination is A1TON1TOFF3. Yet, further research needs to be conducted to study these results and analyze machining process effectiveness in terms of cost and process sustainability, i.e. environmental impact. This research has both theoretical and practical implications.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abu Qudeiri J et al (2019) Advanced electric discharge machining of stainless steels: assessment of the state of the art, gaps and future prospect. Materials 12(6):907. https://doi.org/10.3390/ma12060907
Behrens A, Ginzel J (2003) Neuro-fuzzy process control system for sinking EDM. J Manuf Process 5(1):33–39. https://doi.org/10.1016/S1526-6125(03)70038-3
Beri N et al (2008) performance evaluation of powder metallurgy electrode in electrical discharge machining of AISI D2 steel using taguchi method. Int J Mech Aerosp Ind Mechatron Manuf Eng 2(2):225–229
Bester Field DH et al (2015) Total quality management, 4th edn. Pearson, London
Biswas CK, Dewangan S (2012) Optimisation of EDM process with fuzzy logic technique. In: International conference on metallurgical, manufacturing and mechanical engineering (ICMMME’2012) Dec 26–27, 2012 DUBAI (UAE), pp 346–349
Çaydaş U, Hasçalık A, Ekici S (2009) An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst Appl 36(3):6135–6139. https://doi.org/10.1016/j.eswa.2008.07.019
Chakraborty S, Bhattacharyya B, Diyaley S (2019) Applications of optimization techniques for parametric analysis of non-traditional machining processes: a review. Manag Sci Lett 9(3):467–494. https://doi.org/10.5267/j.msl.2018.12.004
Chakravorty R, Gauri SK, Chakraborty S (2012a) Optimisation of the correlated responses of EDM process using modified principal component analysis-based utility theory. Int J Manuf Technol Manag 26(1/2/3/4):21. https://doi.org/10.1504/ijmtm.2012.051431
Chakravorty R, Gauri SK, Chakraborty S (2012b) Optimization of correlated responses of EDM process. Mater Manuf Process 27(3):337–347. https://doi.org/10.1080/10426914.2011.577875
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):2040–2047. https://doi.org/10.1016/j.matpr.2017.02.049
Conţiu G et al (2015) Fuzzy analytical hierarchy process applied to determine the material. Appl Math Mech Eng 58(2):385–394
Crossan MM, Apaydin M (2010) A multi-dimensional framework of organizational innovation: a systematic review of the literature. J Manag Stud 47(6):1154–1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x
Deris AM et al (2019) Modeling and optimization of electric discharge machining performances using harmony search algorithm. J Electr Eng 18(3):56–61
Dweiri F, Al-Jarrah M, Al-Wedyan H (2003) Fuzzy surface roughness modeling of CNC down milling of Alumic-79. J Mater Process Technol 133(3):266–275. https://doi.org/10.1016/S0924-0136(02)00847-6
Gangil M, Pradhan MK (2017) Modeling and optimization of electrical discharge machining process using RSM: a review. Mater Today Proc 4(2):1752–1761. https://doi.org/10.1016/j.matpr.2017.02.017
Ghayatadak MM, Bhandare AS (2019) Optimization of electric discharge machining process parameters for H13 steel by using Taguchi method. In: Shanker K, Shankar R, Sindhwani R (eds) Advances in industrial and production engineering. Lecture notes in mechanical engineering. Springer, Singapore, pp 525–534. https://doi.org/10.1007/978-981-13-6412-9_51
Grzesik W (2008) Cutting tool materials’. Adv Mach Process Metall Mater. https://doi.org/10.1016/b978-008044534-2.50006-0
Habib SS (2009) Study of the parameters in electrical discharge machining through response surface methodology approach. Appl Math Model 33(12):4397–4407. https://doi.org/10.1016/j.apm.2009.03.021
Joy JA, Jung D-W (2019) Process parameters optimization for material removal rate & surface roughness in EDM of En31 tool steel. Int J Mech Eng Robot Res 8(2):182–188. https://doi.org/10.18178/ijmerr.8.2.182-188
Kaneko T, Onodera T (2004) Improvement in machining performance of die-sinking EDM by using self-adjusting fuzzy control. J Mater Process Technol 149(1–3):204–211. https://doi.org/10.1016/j.jmatprotec.2004.02.006
Khachane A, Jatti V (2019) Multi-objective optimization of EDM process parameters using Jaya algorithm combined with grey relational analysis. In: Venkata-Rao R, Taler J (eds) Advanced engineering optimization through intelligent techniques. Advances in intelligent systems and computing. Springer, Singapore, pp 127–135. https://doi.org/10.1007/978-981-13-8196-6_12
Khan MY, Sudhakar Rao P (2019) Optimization of process parameters of electrical discharge machining process for performance improvement. Int J Innov Technol Explor Eng 8(11):3830–3836. https://doi.org/10.35940/ijitee.K2262.0981119
Khan D, Goswami H, Somkuwar V (2018) Process parameter optimization of die sinking EDM: a review. Int Res J Eng Technol 05(07):1493–1500
Kumar S, Dhanabalan S (2019) Influence on machinability and form tolerance of Inconel 718 in Edm using different diameter multi hole Cu electrodes. SN Appl Sci 1(5):396. https://doi.org/10.1007/s42452-019-0403-x
Kumar P, Nalwaya S (2019) EDM process parameter analysis and optimization using CD function multi objective optimization on EN-08 work piece material. Int J Adv Eng Res Sci 6(8):32–37. https://doi.org/10.22161/ijaers.68.5
Kuo C-FJ, Lin W-T (2017) A study of multi-quality processing parameter optimization for sueded fabric. Text Res J 87(4):389–398. https://doi.org/10.1177/0040517516631314
Lin JL et al (2000) Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. J Mater Process Technol 102(1–3):48–55. https://doi.org/10.1016/S0924-0136(00)00438-6
Lin CL, Lin JL, Ko TC (2002) Optimisation of the EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. Int J Adv Manuf Technol 19(4):271–277. https://doi.org/10.1007/s001700200034
Lin Y-C et al (2006) Machining characteristics and optimization of machining parameters of SKH 57 high-speed steel using electrical-discharge machining based on Taguchi method. Mater Manuf Process 21(8):922–929. https://doi.org/10.1080/03602550600728133
Mandaloi G et al (2015) Effect on crystalline structure of AISI M2 steel using copper electrode through material removal rate, electrode wear rate and surface finish. Measurement 61:305–319. https://doi.org/10.1016/j.measurement.2014.10.057
Mandaloi G et al (2016) Effect on crystalline structure of AISI M2 steel using tungsten–thorium electrode through MRR, EWR, and surface finish. Measurement 90:74–84. https://doi.org/10.1016/j.measurement.2016.04.041
Nayim SMTI et al (2019) Recent trends & developments in optimization and modelling of electro-discharge machining using modern techniques: a review. In: AIP conference proceedings, p 030051. https://doi.org/10.1063/1.5123973
Patel D et al (2015) Study of sand composition on mould properties and selection of Taguchi orthogonal array …In: 4th international conference on progress in production, mechanical and automobile engineering (ICPMAE-2015), vol 2, no 3, pp 31–34
Patel RK, Pradhan MK (2019) Process parameters optimization of electrical discharge machining of Al7075/SiC/WS2 by using MCDM. In: Advances in computational methods in manufacturing, lecture notes on multidisciplinary industrial engineering, pp 329–336. https://doi.org/10.1007/978-981-32-9072-3_28
Paul TR et al (2019) Multi-objective optimization of some correlated process parameters in EDM of Inconel 800 using a hybrid approach. J Braz Soc Mech Sci Eng 41(7):1–11. https://doi.org/10.1007/s40430-019-1805-9
Prabhu S, Uma M, Vinayagam BK (2013) Adaptive neuro-fuzzy interference system modelling of carbon nanotube-based electrical discharge machining process. J Braz Soc Mech Sci Eng 35(4):505–516. https://doi.org/10.1007/s40430-013-0047-5
Pradhan MK (2018) Optimisation of EDM process for MRR, TWR and radial overcut of D2 steel: a hybrid RSM-GRA and entropy weight-based TOPSIS approach. Int J Ind Syst Eng 29(3):273. https://doi.org/10.1504/IJISE.2018.10013960
Pradhan MK, Biswas CK (2011) Multi-response optimisation of EDM of AISI D2 tool steel using response surface methodology. Int J Mach Mach Mater 9(1/2):66. https://doi.org/10.1504/IJMMM.2011.038161
Prakash C et al (2018) Multi-objective particle swarm optimization of EDM parameters to deposit HA-coating on biodegradable Mg-alloy. Vacuum 158:180–190. https://doi.org/10.1016/j.vacuum.2018.09.050
Purohit R et al (2015) Optimization of electric discharge machining of M2 tool steel using grey relational analysis. Mater Today Proc 2(4–5):3378–3387. https://doi.org/10.1016/j.matpr.2015.07.312
Ramesh NN, Harinarayana K, Naik BB (2014) Machining characteristics of H.S.S. & titanium using electro discharge sawing and wire—electrodischarge machining. Procedia Mat Sci 6(Icmpc):1253–1259. https://doi.org/10.1016/j.mspro.2014.07.199
Reddy CB, Reddy GJ, Reddy CE (2012) Growth of electrical discharge machining and its applications—a review. Int J Eng Res Dev 4(12):13–22
Roy T, Dutta RK (2019) Integrated fuzzy AHP and fuzzy TOPSIS methods for multi-objective optimization of electro discharge machining process. Soft Comput 23(13):5053–5063. https://doi.org/10.1007/s00500-018-3173-2
Sadr P, Kolahdooz A, Eftekhari SA (2017) The effect of electrical discharge machining parameters on alloy DIN 1.2080 using the Taguchi method and determinant of optimal design of experiments. J Naval Archit Mar Eng 14(1):47–64. https://doi.org/10.3329/jname.v14i1.31632
Shukla M et al (2018) Experimental investigation of EDM parameters on Al-LM6/SiC/B4C hybrid composites. Appl Mech Mater 877(8):149–156. https://doi.org/10.4028/www.scientific.net/AMM.877.149
Singh V, Pradhan S (2013) Optimization of EDM process parameters: a review. Int J Eng Technol Adv Eng 4(3):345–355
Singh RK et al (2014) Investigation of MRR and TWR on high speed steel using copper and brass electrode for EDM. Int J Innov Res Sci Eng Technol 3(12):18252–18260. https://doi.org/10.15680/IJIRSET.2014.0312068
Singh BK, Kasdekar DK, Parashar V (2015) Application of GRA for optimal machining parameter selection in EDM. Int J Hybrid Inf Technol 8(10):371–382. https://doi.org/10.14257/ijhit.2015.8.10.34
Singh N et al (2018a) Comparison of machining characteristics of Inconel 601 with normal and cryogenic cooled electrode in EDM using RSM. IOP Conf Ser Mater Sci Eng 455:012078. https://doi.org/10.1088/1757-899X/455/1/012078
Singh N, Routara BC, Das D (2018b) Study of machining characteristics of Inconel 601in EDM using RSM. Mater Today Proc 5(2):3438–3449. https://doi.org/10.1016/j.matpr.2017.11.590
Singh NK, Singh Y, Kumar S, Sharma A (2019a) Comparative study of statistical and soft computing-based predictive models for material removal rate and surface roughness during helium-assisted EDM of D3 die steel. SN Appl Sci 1(6):1–12. https://doi.org/10.1007/s42452-019-0545-x
Singh NK, Singh Y, Kumar S, Upadhyay R (2019b) Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses. SN Appl Sci 2(1):1–14. https://doi.org/10.1007/s42452-019-1533-x
Singh NK, Agrawal S et al (2019c) Predictive analysis of surface roughness in argon-assisted EDM using semiempirical and ANN techniques. SN Appl Sci 1(9):995. https://doi.org/10.1007/s42452-019-1032-0
Singh NK, Singh Y, Kumar S, Sharma A (2019d) ‘Predictive analysis of surface roughness in EDM using semi-empirical. ANN and ANFIS techniques: a comparative study’, Mater Today Proc. https://doi.org/10.1016/j.matpr.2019.08.234
Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207–222. https://doi.org/10.1111/1467-8551.00375
Tzeng Y, Chen F (2007) Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach. Mater Des 28(4):1159–1168. https://doi.org/10.1016/j.matdes.2006.01.028
Ubaid AM et al (2017) Optimization of electro discharge machining process parameters with fuzzy logic for stainless steel 304 (ASTM A240). J Manuf Sci Eng 140(1):011013. https://doi.org/10.1115/1.4038139
Vinet L, Zhedanov A (2011) A “missing” family of classical orthogonal polynomials. J Phys A Math Theor 44(8):085201. https://doi.org/10.1088/1751-8113/44/8/085201
Xiaoke L et al (2019) RBF and NSGA-II based EDM process parameters optimization with multiple constraints. Math Biosci Eng 16(5):5788–5803. https://doi.org/10.3934/mbe.2019289
Yadav S, Kumar Verma R (2019) Multi Criteria optimization approaches in electric discharge machining. Mater Today Proc 18:4243–4252. https://doi.org/10.1016/j.matpr.2019.07.382
Yilmaz O, Eyercioglu O, Gindy NNZ (2006) A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters. J Mater Process Technol 172(3):363–371. https://doi.org/10.1016/j.jmatprotec.2005.09.023
Zadeh LA (1976) A fuzzy-algorithm approach to the definition of complex or imprecise concepts. Int J Man Mach Stud 8:249–291
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
This research has no any declarations of interest to be disclosed.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ubaid, A.M., Aghdeab, S.H., Abdulameer, A.G. et al. Multidimensional optimization of electrical discharge machining for high speed steel (AISI M2) using Taguchi-fuzzy approach. Int J Syst Assur Eng Manag 11, 1021–1045 (2020). https://doi.org/10.1007/s13198-020-00951-6
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-020-00951-6