Obrabotka Metallov 2024 Vol. 26 No. 1

ОБРАБОТКА МЕТАЛЛОВ Том 26 № 1 2024 170 МАТЕРИАЛОВЕДЕНИЕ экономичного способа оценки тепловыделения и шероховатости поверхности при точении различных материалов твердосплавными инструментами с TiAlN-покрытием. Список литературы 1. Empirical modelling and optimization of temperature and machine vibration in CNC hard turning / P.S. Ghosh, S. Chakraborty, A.R. Biswas, N.K. Mandal // Materials Today: Proceedings. – 2018. – Vol. 5 (5). – P. 12394–12402. – DOI: 10.1016/j.matpr.2018.02.218. 2. Groover M.P. Fundamentals of modern manufacturing: materials, processes, and systems. – 4th ed. – Hoboken, NJ: Wiley, 2010. – 1012 p. – ISBN 978-0470467002. 3. Cutting temperature measurement using an improved two-color infrared thermometer in turning Inconel 718 with whisker-reinforced ceramic tools / J. Zhao, Z. Liu, B. Wang, Y. Hua, Q. Wang // Ceramics International. – 2018. – Vol. 44 (15). – P. 19002–19007. – DOI: 10.1016/j.ceramint.2018.07.142. 4. Kakade H.B., Patil N.G. Comparative investigations into high speed machining of AB titanium alloy (Ti–6al–4v) under dry and compressed Co2 gas cooling environment // AIP Conference Proceedings. – 2018. – Vol. 2018 (1). – P. 20009-1–20009-9. – DOI: 10.1063/1.5058246. 5. Gunjal S.U., Sanap S.B., Patil N.G. Role of cutting fl uids under minimum quantity lubrication: an experimental investigation of chip thickness // Materials Today: Proceedings. – 2020. – Vol. 28 (2). – P. 1101– 1105. – DOI: 10.1016/j.matpr.2020.01.090. 6. Кулкарни А.П., Чинчаникар С., Саргаде В.Г. Теория размерностей и моделирование температуры на границе раздела стружка-инструмент при точении SS304 на основе искусственных нейронных сетей // Обработка металлов (технология, оборудование, инструменты). – 2021. – Т. 23, № 4. – С. 47–64. – DOI: 10.17212/1994-6309-2021-23.4-47-64. 7. Modelling of fl ank wear, surface roughness and cutting temperature in sustainable hard turning of AISI D2 steel / R. Kumar, A.K. Sahoo, R.K. Das, A. Panda, P.C. Mishra // ProcediaManufacturing. – 2018. –Vol. 20. – P. 406–413. – DOI: 10.1016/j.promfg.2018.02.059. 8. GosaiM., Bhavsar S.N. Experimental study on temperature measurement in turning operation of hardened steel (EN36) // Procedia Technology. – 2016. – Vol. 23. – P. 311–318. – DOI: 10.1016/j.protcy.2016.03.032. 9. Abhang L.B., Hameedullah M. Chip-tool interface temperature prediction model for turning process // International Journal of Engineering Science and Technology. – 2010. – Vol. 2 (4). – P. 382–393. 10. Doniavi A., Eskanderzade M., Tahmsebian M. Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology // Journal of Applied Sciences. – 2007. – Vol. 7 (17). – P. 2509–2513. – DOI: 10.3923/jas.2007.2509.2513. 11. Verma V., Kumar J., Singh A. Optimization of material removal rate and surface roughness in turning of 316 steel by using full factorial method // Materials Today: Proceedings. – 2020. – Vol. 25. – P. 793–798. – DOI: 10.1016/j.matpr.2019.09.029. 12. Investigation on surface roughness and chip reduction coeffi cient during turning aluminium matrix composite / D. Das, R.F. Ali, B.B. Nayak, B.C. Routara // Materials Today: Proceedings. – 2019. – Vol. 5 (11). – P. 23541–23548. – DOI: 10.1016/j.matpr.2018.10.142. 13. Bhople N., Patil N., Mastud S. The experimental investigations into dry turning of austempered ductile iron // Procedia Manufacturing. – 2018. – Vol. 20. – P. 227–232. – DOI: 10.1016/j.promfg.2018.02.033. 14. Analysis of surface roughness and cutting force components in hard turning with CBN tool: prediction model and cutting conditions optimization / H. Aouici, M.A. Yallese, K. Chaoui, T. Mabrouki, J.F. Rigal // Measurement. – 2012. – Vol. 45 (3). – P. 344–353. – DOI: 10.1016/j.measurement.2011.11.011. 15. Longbottom J.M., Lanham J.D. Cutting temperature measurement while machining – a review // Aircraft Engineering and Aerospace Technology. – 2005. – Vol. 77 (2). – P. 122–130. – DOI: 10.1108/ 00022660510585956. 16. Korkut I., Acır A., Boy M. Application of regression and artifi cial neural network analysis in modelling of tool–chip interface temperature in machining // Expert Systems with Applications. – 2011. – Vol. 38 (9). – P. 11651–11656. – DOI: 10.1016/j.eswa.2011.03.044. 17. Dhar N.R., Kamruzzaman M. Cutting temperature, tool wear, surface roughness and dimensional deviation in turning AISI-4037 steel under cryogenic condition // International Journal of Machine Tools and Manufacture. – 2007. – Vol. 47 (5). – P. 754– 759. – DOI: 10.1016/j.ijmachtools.2006.09.018. 18. Patil N.G., Brahmankar P.K. Semi-empirical modeling of surface roughness in wire electro-discharge machining of ceramic particulate reinforced Al matrix composites // Procedia CIRP. – 2016. – Vol. 42. – P. 280–285. – DOI: 10.1016/j.procir.2016.02.286. 19. Patel D.R., Kiran M.B. A non-contact approach for surface roughness prediction in CNC turning using a linear regression model // Materials Today: Proceedings. – 2020. – Vol. 26. – P. 350–355. – DOI: 10.1016/j.matpr.2019.12.029. 20. Patel V.D., Gandhi A.H. Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool // Measurement. – 2019. – Vol. 138. – P. 34–38. – DOI: 10.1016/j.measurement.2019.01.077.

RkJQdWJsaXNoZXIy MTk0ODM1