Obrabotka Metallov 2026 Vol. 28 No. 2

ОБРАБОТКА МЕТАЛЛОВ Том 28 № 2 2026 330 МАТЕРИАЛОВЕДЕНИЕ по сравнению с традиционными статистическими моделями. Важно отметить, что все три методологии (Taguchi, RSM и ML) согласованно определили AlTiN как наилучшее покрытие. Тесное согласие между прогнозами ML и экспериментальными результатами Taguchi (отклонение ~8,2 %) подтверждает надежность и валидность предложенной гибридной структуры. Оптимизация на основе машинного обучения предсказала глобальный оптимум для покрытия AlTiN при толщине 4 мкм, температуре 50 °C и нагрузке 15 Н, с прогнозируемой интенсивностью изнашивания 0,010823 мм3/(Н·м). Список литературы 1. Intelligent tool wear monitoring using XGBoost, SVR, and DNN models in NMQL environment / O. Almomani, B. Venkatesh, S.P. Chaudhary, A. Mishra, S. Sujai, S. Juneja, P. Pradhan, S.P. Venkatesan, A. Bhowmik, Y. Tamene // Scientifi c Reports. – 2026. – Vol. 16 (1). – P. 10030. – DOI: 10.1038/s41598-02640968-8. 2. Deep learning prediction of dry friction in DLC coatings using literature-derived data / O. Cherguy, R. Chmielowski, E. Hachem, I. Lahouij // Tribology Letters. – 2025. – Vol. 73 (4). – P. 125. – DOI: 10.1007/ s11249-025-02056-2. 3. Daghbouch A., Louhichi B., Terres M.A. Optimization and prediction ofmass loss during adhesivewear of nitrided AISI 4140 steel parts // Crystals. – 2025. – Vol. 15 (10). – P. 875. – DOI: 10.3390/cryst15100875. 4. Infl uence of the interlayer and the substrate on the wear behavior of PVD tool coatings during turning of C60+ N / B. Bergmann, B. Denkena, N. Junge, C. Kalscheuer, K. Bobzin, X. Liu // Wear. – 2026. – Vol. 584–585. – P. 206396. – DOI: 10.1016/j. wear.2025.206396. 5. Minimizing thin fi lm thickness in TiN coatings using genetic algorithms / M.I. Jarrah, A.S.M. Jaya, M.A.Azam, M.R. Muhamad, H.Akbar //AIPConference Proceedings. – 2018. – Vol. 2016 (1). – P. 020061. – DOI: 10.1063/1.5055463. 6. Ambhore N., Naranje V., Shelke S. Machining performance evaluation in turning of hardened steel using machine learning // Materials and Manufacturing Processes. – 2025. – Vol. 40 (14). – P. 1935–1942. – DO I: 10.1080/10426914.2025.2559622. 7. Fuzzy rule-based model to estimate surface roughness and wear in hard coatings / A.S.M. Jaya, S.Z.M. Hashim, H. Haron, M.R. Muhamad, M.N.A. Rahman // Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. – IEEE, 2012. – P. 1076–1081. – DOI: 10.1109/ ICSMC.2012.6377873. 8. Jiang J., Omar I., Khan M. Predicting wear damage in moving mechanical contacts: Comparative analysis of regression algorithms and feature selection techniques // ISA Transactions. – 2025. – Vol. 167. – P. 675–687. – DOI: 10.1016/j.isatra.2025.08.027. 9. Kahraman F., Sugözü B. An integrated approach based on the Taguchi method and response surface methodology to optimize parameter design of asbestos-free brake pad material // Turkish Journal of Engineering. – 2019. – Vol. 3 (3). – P. 127–132. – DOI: 10.31127/TUJE.479458. 10. Wear resistance prediction of AlCoCrFeNi-X (Ti, Cu) high-entropy alloy coatings based on machine learning / J. Kang, Y. Niu, Y. Zhou, Y. Fan, G. Ma // Metals. – 2023. – Vol. 13 (5). – P. 939. – DOI: 10.3390/ met13050939. 11. Machine learning predictive modeling of plasma-sprayed lanthanum zirconate coatings / S. Karthikeyan, R. Prasanna, V. Balasubramanian, G. Perumal, V. Mugendiran // Surface Engineering. – 2025. – Vol. 41 (10–12). – P. 1045–1054. – DOI: 10.1177/02670844251389512. 12. Application of machine learning to solid particle erosion of APS-TBC and EB-PVD TBC at elevated temperatures / Y. Liu, R. Ravichandran, K. Chen, P. Patnaik // Coatings. – 2021. – Vol. 11 (7). – P. 845. – DOI: 10.3390/coatings11070845. 13. A review of explainable AI methods and their application in manufacturing systems / G. Tzionis, P. Mouratidis, G. Kougka, I. Gialampoukidis, S. Vrochidis, I. Kompatsiaris, M. Vlachopoulou // Discover Applied Sciences. – 2025. – Vol. 8. – P. 52. – DOI: 10.1007/s42452-025-07908-z. 14. Machine learning and Taguchi techniques for predicting wear mechanisms of Ni–Cu alloy composites / J. Kumaraswamy, Thirumalesh, A.S. Ashok, N.B. Shankar, S.R. Praveen // Results in Surfaces and Interfaces. – 2024. – Vol. 17. – DOI: 10.1016/j. rsurfi .2024.100307. 15. Mallick A., Sahu R.K., Gangi Setti S. Machine learning enabled hardness prediction in centrifugal cast functionally graded materials for superior wear performance // Journal of Tribology. – 2025. – Vol. 148 (3). – P. 031701. – DOI: 10.1115/1.4069854. 16. Tribo-informatics approach to predict wear and friction coeffi cient of Mg/Si₃N₄ composites using machine learning techniques / M.B. Pasha, R.N. Rao, S. Ismail, M. Gupta, P.S. Prasad // Tribology International. – 2024. – Vol. 196. – P. 109696. – DOI: 10.1016/j.triboint.2024.109696. 17. PatelK. Material lifespanpredictor // International Journal of Research in Science and Technology. –

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