Machinery & Energetics

Influence of regenerative effect and mode coupling on the stability of milling operations

Yaroslav Grechaniuk, Kyrylo Varodov
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Abstract

Quality of milling operations requires dynamic stability of the process, which depends on the tool geometry, structural rigidity and type of vibrations. The study aimed to identify the influence of internal dynamic disturbances on the milling process, in particular, the mechanisms of self-excited vibrations arising from vibrational interactions and residual irregularities of the machined surface. The methodology included a comparative analysis of milling stabilisation models with an assessment of their advantages, limitations and practical recommendations for implementation in real production processes. Mathematical models were presented that addressed the effects of time delay and the interaction between the directions of oscillation of the tool and the workpiece. The study determined that repeated contact of the tool with the irregularities of the pre-treated surface leads to a variable thickness of the material layer and forms an unstable operating mode. The interaction between different oscillatory modes, when machining flexible or thin-walled parts, leads to more complex self-oscillatory processes, including chaotic oscillations. The most promising for practical use are models with adaptive control, which can respond to changes in process parameters in real time. The study established that the dynamics of the milling process significantly depend on the geometry of the tool, the length of the protrusion, stiffness, mass-inertial characteristics, and wear of the cutting part. Process damping, caused by the side contact of the tool with the workpiece, reduces the amplitude of vibrations in the low-frequency range. The control structure based on the system’s state vector included position, speed, vibration amplitude, spindle speed and tool feed. None of the methods is universal, so it is advisable to apply an adaptive approach to choosing a stability model, considering constructive, dynamic and economic factors. The practical value of the results is that they can be used by engineers and technologists to implement adaptive milling control to reduce vibrations and improve machining accuracy

Keywords

self-oscillation, delayed system, multimodal dynamics, semi-sampling, state vector, intelligent control, adaptive modelling

Suggested citation
Grechaniuk, Ya., & Varodov, K. (2025). Influence of regenerative effect and mode coupling on the stability of milling operations. Machinery & Energetics, 16(3), 58-69. https://doi.org/ 10.31548/machinery/3.2025.58
References
  1. Akdeniz, E., & Arslan, H. (2024). Experimental study on new tool holder design to reduce vibration in turning operations. Journal of Vibration Engineering & Technologies, 12(4), 6341-6353. doi: 10.1007/s42417-023-01255-2.
  2. Bertolini, R., Andrea, G., Alagan, N.T., & Bruschi, S. (2023). Tool wear reduction in ultrasonic vibration-assisted turning of SiC-reinforced metal-matrix composite. Wear, 523, article number 204785. doi: 10.1016/j.wear.2023.204785.
  3. Chang, B., Yi, Z., Zhang, F., Duan, L., & Duan, J. (2024). A comprehensive research on wear resistance of GH4169 superalloy in longitudinal-torsional ultrasonic vibration side milling with tool wear and surface quality. Chinese Journal of Aeronautics, 37(4), 556-573. doi: 10.1016/j.cja.2023.07.009.
  4. Chen, Z., Feng, P., Wang, J., Feng, F., & Zha, H. (2022). Understanding the abnormal effects of ultrasonic vibration on tool wear and surface generation in Zr-based bulk metallic glass cutting. CIRP Journal of Manufacturing Science and Technology, 39, 1-17. doi: 10.1016/j.cirpj.2022.07.004.
  5. Chikwendu, O.C., Emeka, U.C., & Obiuto, N.C. (2025). Digital twin applications for predicting and controlling vibrations in manufacturing systems. World Journal of Advanced Research and Reviews, 25(1), 764-772. doi: 10.30574/wjarr.2025.25.1.3821.
  6. Ehsan, S., Ali, M.A., Khan, S.A., Sana, M., Yasir, M., Anwar, S., & Farooq, M.U. (2024). Understanding the effects of cutting conditions on vibrations, surface integrity, machining temperature and tool wear mechanisms in end milling of AISI D2 Steel. Tribology International, 198, article number 109894. doi: 10.1016/j.triboint.2024.109894.
  7. Gomes, M.C., Brito, L.C., da Silva, M.B., & Duarte, M.A.V. (2021). Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precision Engineering, 67, 137-151. doi: 10.1016/j.precisioneng.2020.09.025.
  8. ISO 3685:1993. (1993). Tool-life testing with single-point turning tools. Retrieved from https://www.iso.org/standard/9151.html.
  9. Kam, M., & Şeremet, M. (2021). Experimental investigation of the effect of machinability on surface quality and vibration in hard turning of hardened AISI 4140 steels using ceramic cutting tools. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 235(5), 1565-1574. doi: 10.1177/09544089211007366.
  10. Kuntoğlu, M., Gupta, M.K., Aslan, A., Salur, E., & Garcia-Collado, A. (2022). Influence of tool hardness on tool wear, surface roughness and acoustic emissions during turning of AISI 1050. Surface Topography: Metrology and Properties, 10(1), article number 015016. doi: 10.1088/2051-672X/ac4f38.
  11. Lan, Q., Chen, B., Yao, B., & He, W. (2024). Tool wear state recognition with deep transfer learning based on spindle vibration for milling process. Computer Modeling in Engineering & Sciences, 138(3), 2825-2844. doi: 10.32604/cmes.2023.030378.
  12. Li, C., Zhao, G., Ji, D., Zhang, G., Liu, L., Zeng, F., & Zhao, Z. (2024). Influence of tool wear and workpiece diameter on surface quality and prediction of surface roughness in turning. Metals, 14(11), article number 1205. doi: 10.3390/met14111205.
  13. Li, K.-M., & Lin, Y.-Y. (2023). Tool wear classification in milling for varied cutting conditions: With emphasis on data pre-processing. International Journal of Advanced Manufacturing Technology, 125(1), 341-355. doi: 10.1007/s00170-022-10701-6.
  14. Liu, X., Xiong, Y., & Yang, Q. (2025). Ultrasonic vibration-assisted machining particle-reinforced al-based metal matrix composites – a review. Metals, 15(5), article number 470. doi: 10.3390/met15050470.
  15. Loveikin, V., Romasevych, Yu., & Kadykalo, I. (2023). Dynamic analysis of the joint movement of the hoisting and slewing mechanisms of a boom crane. Machinery & Energetics, 14(4), 75-85. doi: 10.31548/machinery/4.2023.75.
  16. Maeng, S., Ito, H., Kakinuma, Y., & Min, S. (2023). Study on cutting force and tool wear in machining of die materials with textured PCD tools under ultrasonic elliptical vibration. International Journal of Precision Engineering and Manufacturing-Green Technology, 10(1), 35-44. doi: 10.1007/s40684-022-00416-0.
  17. Marousi, M., Rimpault, X., Turenne, S., & Balazinski, M. (2023). Initial tool wear and process monitoring during titanium metal matrix composite machining (TiMMC). Journal of Manufacturing Processes, 86, 208-220. doi: 10.1016/j.jmapro.2022.12.047.
  18. Nasir, V., Dibaji, S., Alaswad, K., & Cool, J. (2021). Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals. Manufacturing Letters, 30, 32-38. doi: 10.1016/j.mfglet.2021.10.002.
  19. Novitskyi, M., & Slipchuk, А. (2024). Features of the use of vibration dampers in the design of vibration-resistant metal cutting tools. In Proceeding of Ⅰst international conference “Applied mechanics” (pp. 52-55). Ternopil: Ivan Pulyuy Ternopil National Technical University.
  20. Omelyanov, O., Polievoda, Y., & Zamriі, M. (2021). Prospects for the use of vibration during cutting material. Vibrations in Engineering and Technologies, 1(100), 104-114. doi: 10.37128/2306-8744-2021-1-10.
  21. Pukhovskyi, E.S. (2022). The effect of vibrations on the stability of a multi-blade tool. Technical Engineering, 2(90), 44-51. doi: 10.26642/ten-2022-2(90)-44-51.
  22. Rahman, A.Z., Jauhari, K., Al Huda, M., Untariyati, N.A., Azka, M., Rusnaldy, R., & Widodo, A. (2024). Correlation analysis of vibration signal frequency with tool wear during the milling process on martensitic stainless steel material. Arabian Journal for Science and Engineering, 49(8), 10573-10586. doi: 10.1007/s13369-023-08397-1.
  23. Rauf, A., Khan, M.A., Jaffery, S.H.I., & Butt, S.I. (2024). Effects of machining parameters, ultrasonic vibrations and cooling conditions on cutting forces and tool wear in meso scale ultrasonic vibrations assisted end-milling (UVAEM) of Ti-6Al-4V under dry, flooded, MQL and cryogenic environments – a statistical analysis. Journal of Materials Research and Technology, 30, 8287-8303. doi: 10.1016/j.jmrt.2024.05.202.
  24. Sarath, S., & Paul, P.S. (2021). Application of smart fluid to control vibration in metal cutting: a review. World Journal of Engineering, 18(3), 458-479. doi: 10.1108/WJE-06-2020-0232.
  25. Silva, F.J.G., Martinho, R.P., Magalhães, L.L., Fernandes, F., Sales-Contini, R.C., Durão, L.M., Casais, R.C.B., & Sousa, V.F.C. (2024). A comparative study of different milling strategies on productivity, tool wear, surface roughness, and vibration. Journal of Manufacturing and Materials Processing, 8(3), article number 115. doi: 10.3390/jmmp8030115.
  26. Tomashevskyi, O., & Balytska, N. (2023). The process of metal and alloy micro-milling: An analytical review. Technical Engineering, 2(92), 74-88. doi: 10.26642/ten-2023-2(92)-74-88.
  27. Yang, B., Wang, M., Liu, Z., Che, C., Zan, T., Gao, X., & Gao, P. (2023). Tool wear process monitoring by damping behavior of cutting vibration for milling process. Journal of Manufacturing Processes, 102, 1069-1084. doi: 10.1016/j.jmapro.2023.07.077.
  28. Yin, S., Yip, W.S., Dong, Z., Kang, R., & To, S. (2025). Experimental and simulation investigation of ultrasonic elliptical vibration cutting of tungsten alloys in ultra-precision machining. Journal of Materials Research and Technology, 34, 77-89. doi: 10.1016/j.jmrt.2024.12.026.
  29. Yu, F., Zhang, C., Zhu, Q., Liu, C., & Dong, Z. (2023). Investigation of ultrasonic mechanism and development of tool wear model in ultrasonic elliptic vibration assisted cutting of stainless steel. Tribology International, 189, article number 108962. doi: 10.1016/j.triboint.2023.108962.
  30. Zenkin, M., Ivanko, A., & Chernysh, M. (2025). Influence of cutting tool vibrations on the surface quality of cut sheet materials and methods for their minimization. Innovative Technologies and Scientific Solutions for Industries, 2(32), 188-198. doi: 10.30837/2522-9818.2025.2.188.
  31. Zhang, H., Wang, B., Qu, L., & Wang, X. (2024). Optimization of tool wear and cutting parameters in SCCO2-MQL ultrasonic vibration milling of SiCp/Al composites. Machines, 12(9), article number 646. doi: 10.3390/machines12090646.
  32. Zhang, P., Zhang, X., Cao, X., Yu, X., & Wang, Y. (2021). Analysis on the tool wear behavior of 7050-T7451 aluminum alloy under ultrasonic elliptical vibration cutting. Wear, 466-467, article number 203538. doi: 10.1016/j.wear.2020.203538.
  33. Zhuang, K., Shi, Z., Sun, Y., Gao, Z., & Wang, L. (2021). Digital twin-driven tool wear monitoring and predicting method for the turning process. Symmetry, 13(8), article number 1438. doi: 10.3390/sym13081438.