Актуальные проблемы в машиностроении. Том 13. № 3-4. 2026 Инновационные технологии в машиностроении ____________________________________________________________________ 33 6. Liu S., Bao J., Zheng P. A review of digital twin-driven machining. From digitization to intellectualization // Journal of Manufacturing Systems. – 2023. – Vol. 67. – P. 361–378. – DOI: 10.1016/j.jmsy.2023.02.010. 7. Zhang L., Liu J., Zhuang C. Digital twin modeling enabled machine tool intelligence. A review // Chinese Journal of Mechanical Engineering. – 2024. – Vol. 37, iss. 1. – Art. 47. – DOI: 10.1186/s10033-024-01036-2. 8. Research on online prediction of deformation of thin-walled parts based on digital twin technology / B. Geng, C. Yue, W. Xia [et al.] // The International Journal of Advanced Manufacturing Technology. – 2024. – Vol. 133, iss. 9-10. – P. 1365–1378. – DOI: 10.1007/s00170024-13817-z. 9. Research on digital twin monitoring system during milling of large parts / Y. Lu, C. Yue, X. Liu [et al.] // Journal of Manufacturing Systems. – 2024. – Vol. 77. – P. 834–847. – DOI: 10.1016/j.jmsy.2024.10.027. 10. A trochoidal toolpath planning method for 5-axis milling of blisks with equal radial cutting depth / X. Dai, Q. Qi, J. Yang, H. Ding // Journal of Manufacturing Processes. – 2024. – Vol. 123. – P. 128–141. – DOI: 10.1016/j.jmapro.2024.05.062. 11. Efficient tool path planning method of ball-end milling for high quality manufacturing / H.Y. Ma, Y.B. Kou, L.Y. Shen, C.M. Yuan // Robotics and Computer-Integrated Manufacturing. – 2025. – Vol. 93. – Art. 102905. – DOI: 10.1016/j.rcim.2024.102905. 12. Neural surface partitioner: a physics-informed neural network for five-axis machining by a non-spherical cutting tool / J. Hao, H. Chen, P. Hu [et al.] // Journal of Manufacturing Systems. – 2025. – Vol. 83. – P. 976–991. – DOI: 10.1016/j.jmsy.2025.11.015. 13. Peng D., Li H. Intelligent monitoring of milling tool wear based on milling force coefficients by prediction of instantaneous milling forces // Mechanical Systems and Signal Processing. – 2024. – Vol. 208. – Art. 111033. – DOI: 10.1016/j.ymssp.2023.111033. 14. Tool wear monitoring strategy during micro-milling of TC4 alloy based on a fusion model of recursive feature elimination-bayesian optimization-extreme gradient boosting / H. Wang, Q. Bai, J. Zhang [et al.] // Journal of Materials Research and Technology. – 2024. – Vol. 31. – P. 398–411. – DOI: 10.1016/j.jmrt.2024.06.076. 15. Wen D.L.W., Soon H.G., Kumar A.S. Micro-milling digital twin for real-time tool condition monitoring // Manufacturing Letters. – 2024. – Vol. 41, Suppl. – P. 1231–1236. – DOI: 10.1016/j.mfglet.2024.09.149. 16. Digital twin-based anomaly detection for real-time tool condition monitoring in machining / Z. Liu, Z.-Q. Lang, Y. Gui, Y.-P. Zhu, H. Laalej // Journal of Manufacturing Systems. – 2024. – Vol. 75. – P. 163–173. – DOI: 10.1016/j.jmsy.2024.06.004. 17. Review of ultrasonic vibration-assisted milling technology / A. Li, X. Zhang, J. Chen [et al.] // Precision Engineering. – 2024. – Vol. 91. – P. 601–616. – DOI: 10.1016/j.precisioneng.2024.10.021. 18. A comprehensive experimental investigation into vibration-assisted micro-milling of monocrystalline silicon / V. Satpute, D. Huo, J. Hedley, C. Dale // Journal of Manufacturing Processes. –2024. – Vol. 131. – P. 12–26. – DOI: 10.1016/j.jmapro.2024.09.008. 19. Kawasaki K. High-efficiency milling of inconel 718 superalloy. Effects of cutting conditions on tool life and surface roughness // Machines. – 2025. – Vol. 13, iss. 11. – Art. 974. – DOI: 10.3390/machines13110974.
RkJQdWJsaXNoZXIy MTk0ODM1