Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.

Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Department of Industrial Engineering College of Engineering King Saud University P O Box 800 11421 Riyadh Saudi Arabia

Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB Technical University of Ostrava 70800 Ostrava Czech Republic

Faculty of Engineering Department of Mechanical Engineering Gazi University Maltepe Ankara Turkey

Symbiosis Institute of Technology Symbiosis International University Pune 412115 India

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Sanghani CR, Acharya GD. A review of research on improvement and optimization of performance measures for electrical discharge machining. Int. J. Eng. Res. Appl. 2014;4(1):433–450.

Rahim MASBA, Minhat M, Hussein NISB, Salleh MS. A comprehensive review on cold work of AISI D2 tool steel. Metall. Res. Technol. 2018;115:104. doi: 10.1051/metal/2017048. DOI

Majhi SK, Mishra TK, Pradhan MK, Soni H. Effect of machining parameters of AISI D2 tool steel on electro discharge machining. Int. J. Curr. Eng. Technol. 2014;4(1):19–23.

Hamidzadeh MA, Meratian M, Mohammadi Zahrani M. A study on the microstructure and mechanical properties of AISI D2 tool steel modified by niobium. Mater. Sci. Eng. A. 2012;556:758–766. doi: 10.1016/j.msea.2012.07.061. DOI

Sharif S, Kurniawan MA, Orady D. Performance evaluation of vegetable oil as an alternative cutting lubricant when end milling stainless steel using TiAlN coated carbide tools. Trans. N. Am. Manuf. Res. Inst. SME. 2009;37:9–14.

Singh J. Green EDM strategies to minimize environmental impact and improve process efficiency. J. Manuf. Sci. Prod. 2017;13:29–33.

Valaki JB, Rathod PP. Environmental impact, personnel health and operational safety aspects of electric discharge machining: A review. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2015;9:1481–1491. doi: 10.1177/0954405414543314. DOI

Radu M-C, Tampu R, Nedeff V, Patriciu O-I, Schnakovszky C, Herghelegiu E. Experimental investigation of stability of vegetable oils used as dielectric fluids for electrical discharge machining. Processes. 2020;8:1187. doi: 10.3390/pr8091187. DOI

Singh SP, Singh D. Biodiesel production through the use of different sources and characterization of oils and their esters as the substitute of diesel: A review. Renew. Sustain. Energy Rev. 2020;14:200–216. doi: 10.1016/j.rser.2009.07.017. DOI

Singaravel B, Chandra Shekar K, Gowtham Reddy G, Deva Prasad S. Experimental investigation of vegetable oil as dielectric fluid in electric discharge machining of Ti-6Al-4V. Ain Shams Eng. J. 2020;11:143–147. doi: 10.1016/j.asej.2019.07.010. DOI

Ng PS. Investigation of biodiesel dielectric in sustainable electrical discharge machining. Int. J. Adv. Manuf. Technol. 2016;90:9–12.

Valaki JB, Rathod PP, Sankhavara CD. Investigations on technical feasibility of Jatropha curcas oil based bio dielectric fluid for sustainable electric discharge machining (EDM) J. Manuf. Process. 2016;22:151–160. doi: 10.1016/j.jmapro.2016.03.004. DOI

Valaki JB, Rathod PP. Investigating feasibility through performance analysis of green dielectrics for sustainable electric discharge machining. Mater. Manuf. Process. 2016;31:549. doi: 10.1080/10426914.2015.1070430. DOI

Valaki JB, Rathod PP. Assessment of operational feasibility of waste vegetable oil based bioelectric fluid for sustainable electric discharge machining (EDM) Int. J. Adv. Manuf. Technol. 2015;8:1509–1518.

Abbas NM, Yusoff N, Wahab RM. Electrical discharge machining (EDM): Practices in Malaysian industries and possible change towards green manufacturing. Proc. Eng. 2012;4:1684–1688. doi: 10.1016/j.proeng.2012.07.368. DOI

Chakraborty T. Feasibility of Jatropha and Rice bran vegetable oils as sustainable EDM dielectrics. Mater. Manuf. Process. 2023;38(1):50–63. doi: 10.1080/10426914.2022.2089891. DOI

Srivastava S. An insight on powder mixed electric discharge machining: A state of the art review. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2022;237(5):657–690. doi: 10.1177/09544054221111896. DOI

Singha, G., Sidhub, S. S., Bainsb, P. S., Bhuia, A.S. Surface evaluation of ED machined 316L stainless steel in TiO2 nanopowder mixed dielectric medium. Mater. Today Proc.18, 1297–1303 (2019).

Marashi H, Sarhan AAD, Hamdi M. Employing Ti nanopowder dielectric to enhance surface characteristics in electrical discharge machining of AISI D2 steel. Appl. Surf. Sci. 2015;357:892–899. doi: 10.1016/j.apsusc.2015.09.105. DOI

Chen X, Mao SS. Titanium dioxide nanomaterials: Synthesis, properties, modifications, and applications. Chem. Rev. 2007;107:2891–2959. doi: 10.1021/cr0500535. PubMed DOI

Sahu DR, Mandal A. Critical analysis of surface integrity parameters and dimensional accuracy in powder-mixed EDM. Mater. Manuf. Process. 2020 doi: 10.1080/10426914.2020.1718695. DOI

Rouniyar AK, Shandilya P. Experimental investigation on recast layer and surface roughness on aluminum 6061 alloy during magnetic field assisted powder mixed electrical discharge machining. J. Mater. Eng. Perform. 2020 doi: 10.1007/s11665-020-05244-4. DOI

Jatti VS, Dhabale RB, Mishra A, Khedkar NK, Jatti VS, Jatti AV. Machine learning based predictive modeling of electrical discharge machining of cryo-treated NiTi, NiCu and BeCu alloys. Appl. Syst. Innov. 2022;5:107. doi: 10.3390/asi5060107. DOI

Ulas M. Surface roughness prediction of machined aluminum alloy with wire electrical discharge machining by different machine learning algorithms. J. Mater. Res. Technol. 2020;9(6):12512–12524. doi: 10.1016/j.jmrt.2020.08.098. DOI

Vakharia V. Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model. J. Mater. Res. Technol. 2022;18:325–337. doi: 10.1016/j.jmrt.2022.02.093. DOI

Singh R. Machine learning algorithms based advanced optimization of EDM parameters: An experimental investigation into shape memory alloys. Sens. Int. 2022;3:100179. doi: 10.1016/j.sintl.2022.100179. DOI

Paturi UMR. Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining. Mach. Learn. Appl. 2021;6:100099.

Walia AS, Srivastava V, Rana PS, Somani N, Gupta NK, Singh G, Pimenov DY, Mikolajczyk T, Khanna N. Prediction of tool shape in electrical discharge machining of EN31 steel using machine learning techniques. Metals. 2021;11:1668. doi: 10.3390/met11111668. DOI

Shukla SK, Priyadarshini A. Application of machine learning techniques for multi objective optimization of response variables in wire cut electro discharge machining operation. Mater. Sci. Forum. 2021;969:800–806. doi: 10.4028/www.scientific.net/MSF.969.800. DOI

Walia AS, Srivastava V, Verma K. Modelling of surface roughness and change in out-of-roundness of tool during electrical discharge machining with cermet tool tip using machine learning, 2022. Processes. 2022;10:252. doi: 10.3390/pr10020252. DOI

Astakhov VP. Surface Integrity-Definition and Importance in Functional Performance. Michigan State University; 2010.

Grzesik W, Kruszynsk B, Ruszaj A. Surface integrity of machined surfaces. Int. J. Mach. Tools Manuf. 2010;47:255–262. doi: 10.1016/j.ijmachtools.2006.03.018. DOI

Cavaleri, L. et al. Surface roughness prediction of electro-discharge machined components using artificial neural networks. Int. Conf. Integr. Reliab. Fail. Fac. Eng. 24–28 (2016).

Rahimi H. Experimental investigation of the effect of EDM parameters and dielectric type on the surface integrity and topography. Int. J. Adv. Manuf. Technol. 2022;118:1767–1778. doi: 10.1007/s00170-021-08040-z. DOI

Jui JJ, Imran Molla MM, Bari BS, Rashid M, Hasan MJ. Flat price prediction using linear and random forest regression based on machine learning techniques. Embrac. Ind. 2021;4:205–217.

Shanmugasundar G, Vanitha M, Cep R, Kumar V, Kalita K, Ramachandran M. A comparative study of linear, Random Forest and AdaBoost regressions for modeling non-traditional machining. Processes. 2021;9:2015. doi: 10.3390/pr9112015. DOI

Ramachandran, V., Jeba, J. A comparison of machine learning techniques for the prediction of the student’s academic performance. Emerg. Trends Comput. Expert Technol. 1052–1062 (2020).

Sachin Kumar T, Gopi NH, Gupta MK, Gaur V, Krolczyk GM, ChuanSong Wu. Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control. J. Intell. Manuf. 2023;34:21–55. doi: 10.1007/s10845-022-02029-5. DOI

Multi-objective optimization of an EDM process for Monel K-500 alloy using response surface methodology-multi-objective dragonfly algorithm