OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 and copper (Cu) electrodes. Experiments were carried out with varying the gap current, magnetic fi eld strength, and pulse on time. The pulse turn off time of 7 μs and the gap voltage of 55 V were kept constant for all experiments. The thickness of the white layer and the formation of surface cracks were also investigated as a function of the EDM process parameters. To determine the fi nal process input parameter levels for the primary experiments, an pilot study was fi rst conducted. Second, the Box-Behnken design of experiments was followed in the planning and execution of the primary studies. Based on the experiments, a mathematical model was created to predict and maximize MRR by optimizing EDM performance. This study allows us to draw the following conclusions. ● The cryogenically treated BeCu workpiece and untreated Cu electrode combination provided higher MRR among the other combinations of workpiece and the tool selected in the present study. ● The gap current had the biggest impact on the MRR, followed by the timely pulse and the magnetic fi eld strength, which had a negligible eff ect. The MRR was a minimum of 0.9 mm3/min and a maximum of 11.807 mm3/min. ● The observed white layer thickness at a low material removal rate for the horizontal surface was a minimum of 6.38 μm and a maximum of 10.47 μm. Similarly, for the vertical surfaces, the maximum and minimum were 13.83 μm and 6.99 μm, respectively. ● The observed white layer thickness at a high material removal rate on the horizontal surface was a minimum of 12.92 μm and a maximum of 14.24 μm. Similarly, for the vertical surface, the maximum and minimum were 15.58 μm and 11.67 μm, respectively. ● SEM images were obtained on the wall and bottom surfaces of the workpiece. Negligible surface cracks were observed for low, medium, and high material removal rates. ● It is evident that, owing to the cryogenic treatment of the workpiece and external magnetic strength, the white layer formation and surface crack formation were low. References 1. Vora J., Khanna S., Chaudhari R., Patel V.K., Paneliya S., Pimenov D.Y., Giasin K., Prakash C. Machining parameter optimization and experimental investigations of nano-graphene mixed electrical discharge machining of nitinol shape memory alloy. Journal of Materials Research and Technology, 2022, vol. 19, pp. 653–668. DOI: 10.1016/j.jmrt.2022.05.076. 2. Akıncıoğlu S. Taguchi optimization of multiple performance characteristics in the electrical discharge machining of the TiGr2. Facta Universitatis. Series: Mechanical Engineering, 2022, vol. 20 (2), pp. 237–253. DOI: 10.22190/FUME201230028A. 3. Danish M., Al-Amin M., Abdul-Rani A.M., Rubaiee S., Ahmed A., Zohura F.T., Ahmed R., Yildirim M.B. Optimization of hydroxyapatite powder mixed electric discharge machining process to improve modifi ed surface features of 316L stainless steel. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2023, vol. 237 (3), pp. 881–895. DOI: 10.1177/09544089221111584. 4. Kam M., İpekçi A., Argun K. Experimental investigation and optimization of machining parameters of deep cryogenically treated and tempered steels in electrical discharge machining process. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2022, vol. 236 (5), pp. 1927–1935. DOI: 10.1177/09544089221078133. 5. Gautam N., Goyal A., Sharma S.S., Oza A.D., Kumar R. Study of various optimization techniques for electric discharge machining and electrochemical machining processes. Materials Today: Proceedings, 2022, vol. 57, pp. 615–621. DOI: 10.1016/j.matpr.2022.02.005. 6. Shukla S.K., Priyadarshini A. Application of machine learning techniques for multi objective optimization of response variables in wire cut electro discharge machining operation. Materials Science Forum, 2019, vol. 969, pp. 800–806. DOI: 10.4028/www.scientifi c.net/MSF.969.800. 7. Kumar Vin., Kumar Vik., Jangra K.K. An experimental analysis and optimization of machining rate and surface characteristics in WEDM of Monel-400 using RSM and desirability approach. Journal of Industrial Engineering International, 2015, vol. 11 (3), pp. 297–307. DOI: 10.1007/s40092-015-0103-0. 8. Kumar S.V., Kumar M.P. Optimization of cryogenic cooled EDM process parameters using grey relational analysis. Journal of Mechanical Science and Technology, 2014, vol. 28, pp. 3777–3784. DOI: 10.1007/s12206014-0840-9.
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