Identification of the effects and interactions of factors on the EDM process in order to model it using Taguchi method

Year 2022, Volume: 4 Issue: 2, 76 - 103, 30.06.2022

Taoufik Kamoun Walid Meslameni

https://doi.org/10.47933/ijeir.1058096

Cited By: 1

Abstract

This article presents the identification of the influence of the effects and interactions of the machining parameters (EDM) of the machine (EROTECH basic 450) in order to model the material removal rate (MRR), the tool wear rate (TWR) and the roughness of the part (Ra). We look at all the machining parameters and collect the effects by the design of experiments method. The modeling carried out is thus carried out by the response surfaces method (RSM). We use the statistical method (ANOVA) analysis of variance to approve the robustness of the models and to verify that they are statistically significant. The Taguchi method was implemented to formulate mathematical models to predict EDM machining parameters. The prediction of responses by empirical models is compared with experimental validation tests and the results are satisfactory.

Keywords

Experimental design, EDM, Screening, Modelling, MRR, TWR, Surface Roughness

References

• [1] Meslameni, W., Kamoun, T., & Hbaieb, M. (2019). Experimental modeling of EDM process using the experimental design method, Int J Applied Research and Technology, 2, 39-47.
• [2] Agarwal, N., Shrivastava, N., & Pradhan, M.K. (2019). Optimization of relative wear ratio during EDM of titanium alloy using advanced techniques, SN Applied Sciences, 2, 99. https://doi.org/10.1007/s42452-019-1877-2
• [3] Prathipati, R., Dora, S.P., & Chanamala, R. (2020). Wear behavior of wire electric discharge machined surface of 316L stainless steel, SN Applied Sciences, 2, 412. https://doi.org/10.1007/s42452-020-2244-z
• [4] Prathipati, R., Ch, R., & Dora, S.P. (2019). Corrosion behavior of surface induced by wire EDM on 316L stainless steel: an experimental investigation, SN Applied Sciences, 1, 952. https://doi.org/10.1007/s42452-019-0992-4
• [5] Dewan, P.R., Lepcha, L.P., Khaling, A.N., Prasad, N., & Rai, S. (2018). Experimental Analysis and Optimization of EDM Process Parameters, IOP Conf. Series: Materials Science and Engineering, 377, 012220. doi:10.1088/1757-899X/377/1/012220
• [6] Kolse, D.M., & Shrivastava, R.K. (2017). Effect of Electrode Materials and Optimization of Electric Discharge Machining of M2 Tool Steel Using Grey-Taguchi Analysis, International Journal of Scientific Research in Science and Technology, 3(8), 1020-1027.
• [7] El‑Bahloul, S.A. (2020). Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing, SN Applied Sciences, 2, 49. https://doi.org/10.1007/s42452-019-1849-6
• [8] Mohanty, U.K., Rana, J., & Sharma, A. (2017). Multi-objective optimization of electro-discharge machining EDM parameter for sustainable machining, Materials Today: Proceedings, 4(8), 9147–9157. https://doi.org/10.1016/j.matpr.2017.07.271
• [9] Chandramouli, S., & Eswaraiah, K. (2018). Experimental investigation of EDM Process parameters in Machining of 17-4 PH Steel using Taguchi Method, Materials Today: Proceedings, 5(2), 5058–5067. https://doi.org/10.1016/j.matpr.2017.12.084
• [10] Satpathy, A., Tripathy, S., Senapati, N.P., & Brahma, M.K. (2017). Optimization of EDM process parameters for AlSiC- 20% SiC reinforced metal matrix composite with multi response using TOPSIS, Materials Today: Proceedings, 4(2), 3043–3052. https://doi.org/10.1016/j.matpr.2017.02.187
• [11] Parsana, S., Radadia, N., Sheth, M., Sheth, N., Savsani, V., Prasad, N.E., & Ramprabhu, T. (2018). Machining parameter optimization for EDM machining of Mg–RE–Zn–Zr alloy using multi-objective Passing Vehicle Search algorithm, Archives of Civil and Mechanical Engineering, 8(3), 799-817. https://doi.org/10.1016/j.acme.2017.12.007
• [12] Saffaran, A., Moghaddam, M.A., & Kolahan, F. (2020). Optimization of backpropagation neural network‑based models in EDM process using particle swarm optimization and simulated annealing algorithms, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42, 73. https://doi.org/10.1007/s40430-019-2149-1
• [13] Subrahmanyam, M., & Nancharaiah, T. (2020). Optimization of process parameters in wire-cut EDM of Inconel 625 using Taguchi’s approach, Materials Today: Proceedings, 23(3), 642-646. https://doi.org/10.1016/j.matpr.2019.05.449
• [14] Mohamed, M.F., Lenin, K. (2020). Optimization of Wire EDM process parameters using Taguchi technique, Materials Today: Proceedings, 21(1), 527-530. https://doi.org/10.1016/j.matpr.2019.06.662
• [15] Tlili, A., & Ghanem, F. (2018). A numerical investigation on the local mechanical behavior of a 316-L part during and after an EDM basic electrical discharge, Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-018-2618-1
• [16] Singh, N.K., Agrawal, S., Johari, D., & Singh, Y. (2019). Predictive analysis of surface roughness in argon‑assisted EDM using semiempirical and ANN techniques, SN Applied Sciences, 1, 995. https://doi.org/10.1007/s42452-019-1032-0
• [17] Abu Qudeiri, J.E., Saleh, A., Ziout, A., Mourad, A.I., & Elkaseer, A. (2019). Advanced Electric Discharge Machining of Stainless Steels: Assessment of the State of the Art, Gaps and Future Prospect, Materials, 12, 907. doi:10.3390/ma12060907
• [18] Sharma, S., Vates, U.K., & Bansal, A. (2021). Parametric optimization in wire EDM of D2 tool steel using Taguchi method, Materials Today: Proceedings, 45(2), 757-763. https://doi.org/10.1016/j.matpr.2020.02.802.
• [19] Satishkumar, P., Murthi, C.S., & Meenakshi, R. (2021). Optimization of machining parameters in wire EDM of OFHC copper using Taguchi analysis, Materials Today: Proceedings, 37(2), 922-928. https://doi.org/10.1016/j.matpr.2020.06.120
• [20] Girisha, L., Sridhar, S., Tadepalli, L.D., Swetha, M., Subbiah, R., & Marichamy, S. (2021). Performance analysis and taguchi approach on wire cut EDM using microwave sintered chromium composite, Materials Today: Proceedings, 45(2), 2105-2108. https://doi.org/10.1016/j.matpr.2020.09.700
• [21] Ganapati, S.T., Pachapuri, M.S.A., & Adake, C.V. (2019). Influence of process parameters of electrical discharge machining on MRR, TWR and surface roughness: A review, AIP Conference Proceedings, 2148, 030045. https://doi.org/10.1063/1.5123967
• [22] Meslameni, W., & Ben Salem, C. (2021). Modeling of the springback in folding using the experimental design method. Journal of applied research on industrial engineering, 8(3), 290-308. Doi: 10.22105/JARIE.2021.280059.1284