Obrabotka Metallov 2024 Vol. 26 No. 1

ОБРАБОТКА МЕТАЛЛОВ Том 26 № 1 2024 188 МАТЕРИАЛОВЕДЕНИЕ machining processes / N. Gautam,A. Goyal, S.S. Sharma, A.D. Oza, R. Kumar // Materials Today: Proceedings. – 2022. – Vol. 57. – P.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. – P. 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 inWEDMof Monel-400 using RSM and desirability approach // Journal of Industrial Engineering International. – 2015. – Vol. 11 (3). – P. 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. – P. 3777–3784. – DOI: 10.1007/s12206-014-0840-9. 9. Gangele A., Mishra A. Surface roughness optimization during machining of niti shape memory alloy by EDM through Taguchi’s technique // Materials Today: Proceedings. – 2020. – Vol. 29. – P. 343–347. – DOI: 10.1016/j.matpr.2020.07.287. 10. Machine learning for predictive modeling in management of operations of EDM equipment product / I. Ghosh, M. Sanyal, R. Jana, P.K. Dan // 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). – IEEE, 2016. – P. 169–174. – DOI: 10.1109/ICRCICN.2016.7813651. 11. Surface roughness prediction of machined aluminum alloy with wire electrical discharge machining by diff erent machine learning algorithms / M. Ulas, O. Aydur, T. Gurgenc, C. Ozel // Journal of Materials Research and Technology. – 2020. – Vol. 9 (6). – P. 12512–12524. – DOI: 10.1016/j.jmrt.2020.08.098. 12. Kumar N.A., Babu A.S. Infl uence of input parameters on the near-dry WEDM of Monel alloy // Materials and Manufacturing Processes. – 2018. – Vol. 33 (1). – P. 85–92. – DOI: 10.1080/10426914.201 7.1279297. 13. Shape memory eff ect and superelasticity of titanium nickelide alloys implanted with high ion doses / A. Pogrebnjak, S. Bratushka, V.M. Beresnev, N. Levintant-Zayonts // Russian Chemical Reviews. – 2013. – Vol. 82 (12). – P. 1135. – DOI: 10.1070/ RC2013v082n12ABEH004344. 14. Progress in modeling of electrical discharge machining process / W. Ming, S. Zhang, G. Zhang, J. Du, J. Ma, W. He, C. Cao, K. Liu // International Journal of Heat andMass Transfer. – 2022. –Vol. 187. – P. 122563. – DOI: 10.1016/j.ijheatmasstransfer.2022.122563. 15. Reviewing performance measures of the die-sinking electrical discharge machining process: challenges and future scopes / R.K. Shastri, C.P. Mohanty, S. Dash, K.M.P. Gopal, A.R. Annamalai, C.P. Jen // Nanomaterials. – 2022. – Vol. 12 (3). – P. 384. – DOI: 10.3390/nano12030384. 16. Boopathi S. An extensive review on sustainable developments of dry and near-dry electrical discharge machining processes // Journal of Manufacturing Science and Engineering. – 2022. – Vol. 144 (5). – P. 050801. – DOI: 10.1115/1.4052527. 17. The eff ect of EDM die-sinking parameters on material removal rate of beryllium copper using full factorial method / M.A. Ali, M. Samsul, N.I. Hussein, M. Rizal, R. Izamshah, M. Hadzley, M.S. Kasim, M.A. Sulaiman, S. Sivarao // Middle-East Journal of Scientifi c Research. – 2013. – Vol. 16 (1). – P. 44–50. – DOI: 10.5829/idosi.mejsr.2013.16.01.2249. 18. Infl uence of machining parameters on electro discharge machining of NiTi shape memory alloys / S. Daneshmand, E.F. Kahrizi, E. Abedi, M.M. Abdolhosseini // International Journal of Electrochemical Science. – 2013. – Vol. 8 (30). – P. 3095–3104. – DOI: 10.1016/S1452-3981(23)14376-8. 19. Eff ect of tool rotational and Al2O3 powder in electro discharge machining characteristics of NiTi-60 shape memory alloy / S. Daneshmand, V. Monfared, A.A. Lotfi Neyestanak // Silicon. – 2017. – Vol. 9 (2). – P. 273–283. – DOI: 10.1007/s12633-016-9412-1. 20. Baroi B.K., Jagadish, Patowari P.K. A review on sustainability, health, and safety issues of electrical discharge machining // Journal of the Brazilian Society of Mechanical Sciences and Engineering. – 2022. – Vol. 44 (2). – P. 59. – DOI: 10.1007/s40430-021-03351-4. 21. Infl uences of cryogenically treated work material on near-dry wire-cut electrical discharge machining process / E.Kannan,Y.Trabelsi, S. Boopathi, S.Alagesan // Surface Topography: Metrology and Properties. – 2022. – Vol. 10 (1). – P. 015027. – DOI: 10.1088/2051672X/ac53e1. 22. Abdulkareem S., Khan A.A., Konneh M. Reducing electrode wear ratio using cryogenic cooling during electrical discharge machining // The International Journal of Advanced Manufacturing Technology. – 2009. – Vol. 45. – P. 1146–1151. – DOI: 10.1007/s00170009-2060-5. 23. Gill S.S., Singh J. Eff ect of deep cryogenic treatment on machinability of titanium alloy (Ti-6246) in electric discharge drilling // Materials and Manufacturing Processes. – 2010. – Vol. 25 (6). – P. 378–385. – DOI: 10.1080/10426910903179914. 24. Srivastava V., Pandey P.M. Performance evaluation of electrical discharge machining (EDM) process using cryogenically cooled electrode // Materials

RkJQdWJsaXNoZXIy MTk0ODM1