Abstract
In modern day manufacturing industries, micro-electrical discharge machining (micro-EDM) has emerged out as an efficient material removal process to produce miniaturized components having varying industrial applications. To explore its fullest machining potential, it is always required to operate the micro-EDM process while setting its various input parameters at their optimal levels. In this paper, four popular multi-criteria decision making (MCDM) techniques, in the form of weighted aggregated sum product assessment, technique for order of preference by similarity to ideal solution, combinative distance-based assessment and complex proportional assessment are separately hybridized with teaching-learning-based optimization (TLBO) algorithm to solve the parametric optimization problems of a micro-EDM process. The polynomial regression (PR) models are considered here as the inputs to these hybrid optimizers. Their optimization performance is subsequently validated against the conventionally adopted weighted sum multi-objective optimization (WSMO) approach at four different weight scenarios. It is revealed that for the micro-EDM process, all the MCDM-PR-TLBO approaches provide better solutions as compared to PR-WSMO-TLBO method for the considered weight scenarios. The best performance of the MCDM-PR-TLBO approaches is achieved when 50% weight is assigned to material removal rate. Moreover, it is also noticed that MCDM-PR-TLBO approaches are less computationally intensive than PR-WSMO-TLBO with approximately 9.61–26.70% saving in computational time.
Similar content being viewed by others
Abbreviations
- ANN:
-
Artificial neural network
- CCD:
-
Central composite design
- COPRAS:
-
COmplex PRoportional ASsessment
- GA:
-
Genetic algorithm
- MBDO:
-
Metamodel-based design optimization
- MOGA:
-
Multi-objective genetic algorithm
- OC:
-
Overcut
- PR:
-
Polynomial regression
- Ra:
-
Average surface roughness
- TLBO:
-
Teaching-learning-based optimization
- TWR:
-
Tool wear rate
- WSMO:
-
Weighted sum multi-objective optimization
- CODAS:
-
COmbinative Distance-based ASsessment
- EDM:
-
Electrical discharge machining
- EWR:
-
Electrode wear ratio
- GRA:
-
Grey Relational analysis
- MCDM:
-
Multi-criteria decision making
- MRR:
-
Material removal rate
- PCA:
-
Principal component analysis
- PSO:
-
Particle swarm optimization
- RSM:
-
Response surface methodology
- TOPSIS:
-
Technique for order of preference by similarity to ideal solution
- WASPAS:
-
Weighted aggregated sum product assessment
References
Sivaprakasam, P., Udaya Prakash, J., Hariharan, P., Gowri, S.: Micro-electric discharge machining (Micro-EDM) of aluminium alloy and aluminium matrix composites—A review. Adv. Mater. Process. Technol. (2021). https://doi.org/10.1080/2374068X.2020.1865127
Schubert, A., Zeidler, H., Kühn, R., Matthias Hackert-Oschätzchen, M.: Microelectrical discharge machining: a suitable process for machining ceramics. J. Ceram. (2015). https://doi.org/10.1155/2015/470801
Phan Muthuramalingam, N.H.T.: Multi-criteria decision-making of vibration-aided machining for high silicon-carbon tool steel with Taguchi-topsis approach. SILICON 13, 2771–2783 (2021)
Kansal, H.K., Singh, S., Kumar, P.: Parametric optimization of powder mixed electrical discharge machining by response surface methodology. J. Mater. Process. Technol. 169, 427–436 (2005)
Huu, P.-N.: Multi-objective optimization in titanium powder mixed electrical discharge machining process parameters for die steels. Alex. Eng. J. 59, 4063–4079 (2020)
Fahad, K., Waghmare, C.A., Sohani, M.S.: Optimization and comparative analysis of silicon and chromium powder-mixed EDM process by TOPSIS technique. Eng. Appl. Sci. Res. 48, 190–199 (2021)
Nguyen, H. Q., et al.: Multi-objective optimization of PMEDM process for minimum surface roughness and maximum material removal speed when processing SKD11 steel. In: Proceedings of International Conference on Engineering Research and Applications. Springer, Cham 1–6 (2021)
Fahad, K., Waghmare, C.A., Sohani, M.S.: Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm. Int. J. Interact. Des. Manuf. 15, 695–706 (2021)
Modica, F., Marrocco, V., Irene Fassi, I.: Micro-electro-discharge machining (Micro-EDM). In: International, S. (ed.) Micro-Manufacturing Technologies and Their Applications, I Fassi, D Shipley, pp. 149–173. Publishing, Switzerland (2017)
Raju, L., Hiremath, S.S.: A state-of-the-art review on micro electro-discharge machining. Procedia Technol. 25, 1281–1288 (2016)
Pandey, A.K., Anas, M.: Sustainability and recent trends in micro-electric discharge machining (µ-EDM): A state-of-the-art review. Mater. Today: Proc. (2021). https://doi.org/10.1016/j.matpr.2021.11.250
Sivaprakasam, P., Hariharen, P., Kathikheyen, S., Balusamy, S.: Modeling and optimization of micro electro discharge machining process: a review. Adv. Mater. Res. 622–623, 590–594 (2013)
Vijay Babu, T., Ravishankar, D.V., Koorapati, E.P.: Investigation of process parameters in micro-EDM machining. Int. J. Innov. Res. Sci. Eng. Technol. 3(10), 16736–16741 (2014)
Vasantha Prasath, N., Mohanraj, R., Nandhakumar, M.: Effect of process parameters on the performance of micro-EDM. Int. J. Sci. Res. 6(7), 629–634 (2017)
Natarajan, N., Arunachalam, R.M.: Optimization of micro-EDM with multiple performance characteristics using Taguchi method and grey relational analysis. J. Sci. Ind. Res. 70, 500–505 (2011)
Phipon, R., Pradhan, B.B.: Process parameters optimization of micro electric discharge machining process using genetic algorithm. Int. J. Eng. Res. Appl. 2(5), 1986–1993 (2012)
Tiwary, A.P., Pradhan, B.B., Bhattacharyya, B.: Parametric optimization of micro-EDM process using response surface methodology and principal component analysis. J. Manuf. Technol. Res. 5(3/4), 117–136 (2013)
Jesudas, T., Ramesh, S., Arunachalam, R.M.: Prediction and optimization of micro EDM process parameter using multiple regression and artificial neural network. Elixir Mech. Eng. 66, 20895–20900 (2014)
Tiwary, A.P., Pradhan, B.B., Bhattacharyya, B.: Study on the influence of micro-EDM process parameters during machining of Ti–6Al–V superalloy. Int. J. Adv. Manuf. Technol. 76, 151–160 (2015)
Manivannan, R., Pradeep Kumar, M.: Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS. J. Mech. Sci. Technol. 30(1), 137–144 (2016)
Krishnaraj, V.: Optimization of process parameters in micro-EDM of Ti–6Al–4V alloy. J. Manuf. Sci. Product. 16(1), 41–49 (2016)
Meena, V.K., Azad, M.S., Singh, S., Narinder Singh, N.: Micro-EDM multiple parameter optimization for Cp titanium. Int. J. Adv. Manuf. Technol. 89, 897–904 (2017)
Vijayanand, M.S., Ilangkumaran, M.: Optimization of micro-EDM parameters using grey-based fuzzy logic coupled with the Taguchi method. 51(6), 989–995 (2017)
Bhosle, R.B., Sharma, S.B.: Multi-performance optimization of micro-EDM drilling process of Inconel 600 alloy. Mater. Today: Proc. 4, 1988–1997 (2017)
Abidi, M.H., Al-Ahmari, A.M., Umer, U., Rasheed, M.S.: Multi-objective optimization of micro-electrical discharge machining of nickel-titanium-based shape memory alloy using MOGA-II. Measurement 125, 336–349 (2018)
Tiwary, A.P., Das, P.P., Chakraborty, S., Pradhan, B.B., Bhattacharyya, B.: Optimization of micro-EDM process during micro-hole machining on Ti–6Al–4V using WASPAS Method. Mater. Sci. Eng. 377, 012202 (2018)
Sapkal, S.U., Jagtap, P.S.: Optimization of micro EDM drilling process parameters for titanium alloy by rotating electrode. Procedia Manuf. 20, 119–126 (2018)
Limbitote, J.S., Kurkute, V.: Optimizing parameters of micro-EDM on Inconel 718 using single hole brass electrode. Int. J. Grid Distrib. Comput. 13(2), 2664–2669 (2020)
Khare, S.K., Phull, G.S., Agarwal, S.: Optimization the machining parameters of surface roughness during Micro-EDM by Taguchi method. Mater. Today: Proc. 27, 475–479 (2020)
Dewangan, S., Deepak Kumar, S., Jha, S.K., Biswas, C.K.: Optimization of Micro-EDM drilling parameters of Ti-6Al-4V alloy. Mater. Today: Proc. 33, 5481–5485 (2020)
Dilip, D.G., Panda, S., Mathew, J.: Characterization and parametric optimization of micro-hole surfaces in micro-EDM drilling on Inconel 718 superalloy using genetic algorithm. Arab. J. Sci. Eng. 45, 5057–5074 (2020)
Singh, A.K., Patowari, P.K., Chandrasekaran, M.: Experimental study on drilling micro-hole through micro-EDM and optimization of multiple performance characteristics. J. Braz. Soc. Mech. Sci. Eng. 42, 506 (2020)
Kumar, P., Pattanaik, L.N., R. K. Singh RK,: Simultaneous parametric optimization of micro-EDM drilling of brass C360 using Taguchi based grey relation analysis. Eng. Rev. 41(1), 14–24 (2021)
Quarto, M., D’Urso, G., Giardini, C., Maccarini, G., Carminati, M.: 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. Micromachines 12, 667 (2021)
Kar, S., Sarmah, P., Baroi, B.K., Patowari, P.K.: Parametric optimization of μEDM drilling on titanium using principal component analysis. J. Braz. Soc. Mech. Sci. Eng. 43, 543 (2021)
Nagrale, M.S., Mastud, S.: Optimisation and experimental investigation of material removal rate and tool wear rate of micro electro discharge machining (MEDM) of Hastelloy C276. Adv. Mater. Process. Technol. (2021). https://doi.org/10.1080/2374068X.2021.1970995
Singh, G., Prajapati, D.R., Satsangi, P.S., PS,: Optimization of µEDM process assisted with rotating magnetic pulling force and ultrasonic vibration. Proc. IMechE E: J. Process Mech. Eng. 235, 937–949 (2021)
Kar, S., Patowari, P.K.: An experimental investigation of the erosion phenomenon in μED-milling of titanium and its parametric optimization using desirability analysis. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-021-06387-93
Quarto, M., D’Urso, G., Giardini, C.: Micro-EDM optimization through particle swarm algorithm and artificial neural network. Precis. Eng. 73, 63–70 (2022)
Wang, G.G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. J. Mech. Des. 129(4), 370–380 (2007)
Ryberg, A.B.: Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures, pp. 1870–1899. Linkoping University Electronic Press, Linköping (2017)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011)
Kumar, V., Diyaley, S., Chakraborty, S.: Teaching learning-based optimization of electrical discharge machining processes. Facta Universitatis Ser.: Mech. Eng. 18(2), 281–300 (2020)
Shandilya, P., Rouniyar, A.K., Saikiran, D., D,: Multi-objective parametric optimization on machining of Inconel-825 using wire electrical discharge machining. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 234(20), 4056–4068 (2020)
Rao, R.V., Kalyankar, V.D.: Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Eng. Appl. Artif. Intell. 26, 524–531 (2013)
Diyaley, S., Chakraborty, S.: An analysis on the parametric optimization of electrochemical honing process. J. Adv. Manuf. Syst. 19(2), 249–276 (2020)
Abhishek, K., Kumar, V.R., Datta, S., Mahapatra, S.S.: Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching-learning based optimization algorithm). J. Intell. Manuf. 28, 1769–1785 (2017)
Gupta, M.K., Mia, M., Pruncu, C.I., Kapłonek, W., Nadolny, K., Patra, K., Mikolajczyk, T., Pimenov, D.Y., Sarikaya, M., Sharma, V.S.: Parametric optimization and process capability analysis for machining of nickel-based superalloy. Int. J. Adv. Manuf. Technol. 102, 3995–4009 (2019)
Rao, R.V., Kalyankar, V.D.: Multi-pass turning process parameter optimization using teaching-learning-based optimization algorithm. Scientia Iranica E 20(3), 967–974 (2013)
Diyaley, S., Chakraborty, S.: Optimization of multi-pass face milling parameters using metaheuristic algorithms. Facta Universitatis Ser.: Mech. Eng. 17(3), 365–383 (2019)
Rao, R.V., Kalyankar, V.D., Waghmare, G.: Parameters optimization of selected casting processes using teaching-learning-based optimization algorithm. Appl. Math. Model. 38, 5592–5608 (2014)
Rao, R.V., Rai, D.P.: Optimization of fused deposition modeling process using teaching learning-based optimization algorithm. Eng. Sci. Technol. Int. J. 19, 587–603 (2016)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Das, P.P., Tiwary, A.P. & Chakraborty, S. A hybrid MCDM approach for parametric optimization of a micro-EDM process. Int J Interact Des Manuf 16, 1739–1759 (2022). https://doi.org/10.1007/s12008-022-00869-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12008-022-00869-2