Application of SWARA-CoCoSo-based approach for tool selection of an electrical discharge machining process

• Autorzy: Kumar Vidyapati , Mohata Abhishek , Mistri Ankita , Bartoszuk Marian

Identyfikatory

DOI

10.57223/spies.2023.1.1.04

• Języki publikacji: EN

Abstrakty

EN

Electrical discharge machining (EDM) is a non-traditional machining process widely used in manufacturing to create complex geometries on hard-to-machine materials. The tool material used in EDM plays a crucial role in determining the machining performance and final surface finish of the workpiece. In this research, we aimed to optimize the tool selection for creating circular holes on SG iron (grade 450/12) using EDM. To this end, we employed a step-wise weight assessment ratio analysis (SWARA) based combined compromise solution (CoCoSo) approach to evaluate the performance of different tool materials under various machining conditions. The machining conditions considered in this study included peak current (I), pulse-on time (Ton), and inter-electrode gap (IEG). The results of our study showed that the CoCoSo approach is an effective method for tool selection in EDM, and it can be used to identify the optimal tool material and machining conditions for creating circular holes on SG iron. The final appraisal scores obtained from the ranking of tool materials indicated that copper tools scored highest (2.4767, ranking 1), followed by copper tungsten (2.3615, ranking 2), while brass scored lowest (1.6606, ranking 3). Furthermore, Spearman's rank correlations for different integrated MCDM techniques were performed, which demonstrated the efficacy of this technique. It has been demonstrated that implementing the SWARA-CoCoSo method can effectively optimize the EDM process with regard to sustainable machining practices.

Słowa kluczowe

EN

tool selection; MCDM; SWARA method; CoCoSo; EDM process

• Wydawca: Oficyna Wydawnicza Politechniki Opolskiej
• Czasopismo: Sustainable Production, Instrumentation and Engineering Sciences
• Rocznik: 2023
• Tom: vol. 1, nr 1
• Strony: 19--26
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Kumar Vidyapati

• Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur WestBengal, India

autor

Mohata Abhishek

• Department of Mechanical Engineering, Jalpaiguri Government Engineering College, WestBengal, India

autor

Mistri Ankita

• Department of Mechanical Engineering, Indian Institute of Technology, Dhanbad,Jharkhand, India

autor

Bartoszuk Marian

• Department of Manufacturing Engineering and Materials Science, Opole University of Technology, 45-758 Opole, Poland

Bibliografia

• [1] K.H. Ho and S. T. Newman, “State of the art electrical discharge machining (EDM),” Int. J. Mach. Tools Manuf., vol. 43, no. 13, pp. 1287–1300, 2003, doi: 10.1016/S0890-6955(03)00162-7.
• [2] S. Chakraborty and V. Kumar, “Development of an intelligent decision model for non-traditional machining processes,” Decis. Mak. Appl. Manag. Eng., vol. 4, no. 1, pp. 194–214, 2021, doi: 10.31181/dmame2104194c.
• [3] S. Chakraborty, P. P. Das, and V. Kumar, “Application of grey-fuzzy logic technique for parametric optimization of non-traditional machining processes,” Grey Syst. Theory Appl., vol. 8, no. 1, pp. 46–68, 2018, doi: 10.1108/gs-08-2017-0028.
• [4] J. E. Abu Qudeiri, A. H. I. Mourad, A. Ziout, M. H. Abidi, and A. Elkaseer, “Electric discharge machining of titanium and its alloys: review,” International Journal of Advanced Manufacturing Technology, vol. 96, no. 1–4. pp. 1319–1339, 2018, doi: 10.1007/s00170-018-1574-0.
• [5] V. KUMAR, P. P. DAS, and S. CHAKRABORTY, “Grey-fuzzy method-based parametric analysis of abrasive water jet machining on GFRP composites,” Sādhanā, vol. 45, no. 1, p. 106, Dec. 2020, doi:10.1007/s12046-020-01355-9.
• [6] P. Kumar, L. N. Pattanaik, and R. K. Singh, “Simultaneous parametric optimization of micro-EDM drilling of brass C360 using Taguchi based grey relation analysis,” Eng. Rev., vol. 41, no. 1, pp. 14–24, 2021, doi: 10.30765/er.1377.
• [7] D. Mandal, S. K. Pal, and P. Saha, “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II,” J. Mater. Process. Technol., vol. 186, no. 1–3, pp. 154–162, 2007, doi: 10.1016/j.jmatprotec.2006.12.030.
• [8] S. Dewangan and C. K. Biswas, “Optimisation of machining parameters using grey relation analysis for EDM with impulse flushing,” Int. J. Mechatronics Manuf. Syst., vol. 6, no. 2, pp. 144–158, 2013, doi: 10.1504/IJMMS.2013.053826.
• [9] S. Dewangan, S. Gangopadhyay, and C. K. Biswas, “Multi-response optimization of surface integrity characteristics ofEDM process using grey-fuzzy logic-based hybrid approach,” Eng. Sci. Technol. an Int. J., vol. 18, no. 3, pp. 361–368, 2015, doi: 10.1016/j.jestch.2015.01.009.
• [10] A. Golshan, S. Gohari, and A. Ayob, “Multi-objective optimisation of electrical discharge machining of metal matrix composite Al/SiC using non-dominated sorting genetic algorithm,” Int. J. Mechatronics Manuf. Syst., vol. 5, no. 5–6, pp. 385–398, 2012, doi: 10.1504/IJMMS.2012.049972.
• [11] Jagadish and A. Ray, “Multi-Objective Optimization of Green EDM: An Integrated Theory,” J. Inst. Eng. Ser. C, vol. 96, no. 1, pp. 41–47, 2015, doi: 10.1007/s40032-014-0142-0.
• [12] A. Majumder, “Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO,” J. Mech. Sci. Technol., vol. 27, no. 7, pp. 2143–2151, 2013, doi: 10.1007/s12206-013-0524-x.
• [13] A. Majumder, P. K. Das, A. Majumder, and M. Debnath, “An approach to optimize the EDM process parameters using desirability-based multi-objective PSO,” Prod. Manuf. Res., vol. 2, no. 1, pp. 228–240, 2014, doi: 10.1080/21693277.2014.902341.
• [14] A. Majumder, “Parametric Optimization of Electric Discharge Machining by GA-based Response Surface Methodology,” J. Manuf. Sci. Prod., vol. 12, no. 1, pp. 25–30, 2012, doi: 10.1515/jmsp-2011-0016.
• [15] A. Majumder, “Comparative study of three evolutionaryalgorithms coupled with neural network model for optimization of electric discharge machining process parameters,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 229, no. 9, pp. 1504–1516, 2015, doi: 10.1177/0954405414538960.
• [16] N. M. Liu, J. T. Horng, and K. T. Chiang, “The method of grey-fuzzy logic for optimizing multi-response problems during the manufacturing process: A case study of the light guide plate printing process,” Int. J. Adv. Manuf. Technol., vol. 41, no. 1–2, pp. 200–210, 2009, doi: 10.1007/s00170-008-1448-y.
• [17] M. Azadi Moghaddam and F. Kolahan, “Optimization of EDM process parameters using statistical analysis and simulated annealing algorithm,” Int. J. Eng. Trans. A Basics, vol. 28, no. 1, pp. 157–166, 2015, doi: 10.5829/idosi.ije.2015.28.01a.20.
• [18] U. Kumar Mohanty, J. Rana, and A. Sharma, “Multi-objective optimization of electro-discharge machining (EDM) parameter for sustainable machining,” in Materials Today: Proceedings, 2017, vol. 4, no. 8, pp. 9147–9157, doi: 10.1016/j.matpr.2017.07.271.
• [19] V. Kumar and S. Chakraborty, “Analysis of the Surface Roughness Characteristics of EDMed Components Using GRA Method,” 2022, pp. 461–478.
• [20] M. Niamat, S. Sarfraz, W. Ahmad, E. Shehab, and K. Salonitis, “Parametric modelling and multi-objective optimization of electro discharge machining process parameters for sustainable production,” Energies, vol. 13, no. 1, 2019, doi: 10.3390/en13010038.
• [21] A. Sharma, V. Kumar, A. Babbar, V. Dhawan, K. Kotecha, and C.Prakash, “Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based Hybrid Techniques,” Materials (Basel)., vol. 14, no. 19, p. 5820, Oct. 2021, doi: 10.3390/ma14195820.
• [22] V. Kumar, S. Diyaley, and S. Chakraborty, “Teaching-learning-based parametric optimization of an electrical discharge machining process,” Facta Univ. Ser. Mech. Eng., vol. 18, no. 2, pp. 281–300, 2020, doi: 10.22190/FUME200218028K.
• [23] D. Satija, P. Bhute, V. Gohil, and D. B. Meshram, “A study in electrical discharge machining using copper tungsten electrode,” Mater. Today Proc., no. xxxx, pp. 1–5, 2023, doi: 10.1016/j.matpr.2023.01.043.
• [24] A. B. Khoshaim, T. Muthuramalingam, E. B. Moustafa, and A. Elsheikh, “Influencesof tool electrodes on machinability of titanium α-β alloy with ISO energy pulse generator in EDM process,” Alexandria Eng. J., vol. 63, pp. 465–474, 2023, doi: 10.1016/j.aej.2022.07.059.
• [25] Ferhat CERİTBİNMEZ Elif Simay GÖKKAYA Erdoğan KANCA, “MRR, EWRand KERF Analysis in Cold Work Tool Steel Machining in EDM Method by Copper and Brass Electrode,” vol. 6, no. 1, pp. 35–51, 2023.
• [26] A. Mohata, N. Mukhopadhyay, and V. Kumar, “CRITIC-COPRAS-Based Selection of Commercially Viable Alternative Fuel CRITIC-COPRAS-Based Selection of Commercially Viable Alternative Fuel,” no. February, pp. 51–69, 2023, doi: 10.1007/978-981-19-6107-6.
• [27] S. Das, B. Sarkar, and V. Kumar, “RIM-Based Performance Evaluation of DLC Coating Under Conflicting RIM-Based Performance Evaluation of DLC Coating Under Conflicting,” no. February, pp. 303–320, 2023, doi: 10.1007/978-981-19-6107-6.
• [28] V. Keršuliene, E. K. Zavadskas, and Z. Turskis, “Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (Swara),” J. Bus. Econ. Manag., vol. 11, no. 2, pp. 243–258, Jan. 2010, doi: 10.3846/jbem.2010.12.
• [29] V. Kumar, K. Kalita, P. Chatterjee, E. K. Zavadskas, and S. Chakraborty, “A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection,” Informatica, vol. 0, no. 0, pp. 1–20, Dec. 2021, doi: 10.15388/21-INFOR466.
• [30] M. Yazdani, P. Zarate, E. Kazimieras Zavadskas, and Z. Turskis, “A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems,” Manag. Decis., vol. 57, no. 9, pp. 2501–2519, Jan. 2018, doi: 10.1108/MD-05-2017-0458.
• [31] M. ZELENY, “Compromise programming,” Mult. Criteria Decis. Mak., 1973.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).