Digital Twins as a Tool for Improving the Efficiency of the Milling Process

Authors

DOI:

https://doi.org/10.20535/2521-1943.2025.9.3(106).338170

Keywords:

digital twin, milling, tool wear, vibrations, adaptive control, artificial intelligence, monitoring

Abstract

In modern mechanical engineering, milling remains the most widespread and highly productive shaping process. However, the complex dynamics of the cutting forces between the tool and the workpiece, along with other phenomena accompanying machining, limit productivity improvements without compromising surface quality. One of the effective approaches to enhancing milling productivity while maintaining accuracy is the application of digital twins for monitoring and controlling the milling process.
The aim of this study is to review and summarize current approaches and developments related to the creation and use of digital twins to improve the efficiency of the milling process.
The methodology is based on a systematic review of publications from 2018–2025 in leading scientific databases (Scopus, ScienceDirect, SpringerLink, Google Scholar), selected according to the following criteria: concept development, availability of structural (architectural) descriptions, experimental results, and practical implementation of digital twins in milling.
More than 30 publications were analyzed, of which 12 articles meeting the above criteria and focusing exclusively on digital twins of the milling process were examined in detail.
The results of the study showed that the most successful digital twin architectures for milling are based on multi-layer structures integrating sensor data, mathematical models, and artificial intelligence algorithms. The implementation of bidirectional feedback enables real-time prediction of tool wear, compensation of thin-walled workpiece deformations, and stabilization of surface quality parameters. Several studies reported reduced dimensional errors, fewer defective parts, and increased tool life.
Digital twins in milling demonstrate significant practical value by combining simulation, monitoring, and process control. Further development requires the unification of architectures, the creation of open integration platforms, and the adoption of hybrid computing solutions, which will ensure scalability and industrial implementation of this technology.

References

  1. S. Liu, J. Bao and P. Zheng, “A review of digital twin-driven machining: From digitization to intellectualization”, Journal of Manufacturing Systems, vol. 67, pp. 361-378, 2023. DOI: https://doi.org/10.1016/j.jmsy.2023.02.010.
  2. L. Zhang, J. Liu and C. Zhuang, “Digital Twin Modeling Enabled Machine Tool Intelligence: A Review”, Chinese Journal of Mechanical Engineering, vol. 37, no. 1, p. 47, 2024. DOI: https://doi.org/10.1186/s10033-024-01036-2.
  3. X. Zi, S. Gao and Y. Xie, “An online monitoring method of milling cutter wear condition driven by digital twin”, Scientific Reports, vol. 14, no. 1, p. 4956, 2024. DOI: https://doi.org/10.1038/s41598-024-55551-2.
    |
  4. C. Su, X. Tang, Q. Jiang, Y. Han, T. Wang and D. Jiang, “Digital twin system for manufacturing processes based on a multi-layer knowledge graph model”, Scientific Reports, vol. 15, no. 1, p. 12835, 2025. DOI: https://doi.org/10.1038/s41598-024-85053-0.
    |
  5. X. Fu, H. Song, S. Li and Y. Lu, “Digital twin technology in modern machining: A comprehensive review of research on machining errors”, Journal of Manufacturing Systems, vol. 79, pp. 134-161, 2025. DOI: https://doi.org/10.1016/j.jmsy.2025.01.005.
  6. J. Dominguez-Caballero, S. Ayvar-Soberanis and D. Curtis, “Intelligent real-time tool life prediction for a digital twin framework”, Journal of Intelligent Manufacturing, 2025. DOI: https://doi.org/10.1007/s10845-025-02606-4.
    |
  7. C. Zhang, G. Zhou, Q. Xu, Z. Wei, C. Han and Z. Wang, “A digital twin defined autonomous milling process towards the online optimal control of milling deformation for thin-walled parts”, Research Square, 2022. [Preprint]. DOI: https://doi.org/10.21203/rs.3.rs-1936012/v1.
  8. P. Ganser, T. Venek, V. Rudel and T. Bergs, “DPART – A digital twin framework for the machining domain”, MM Science Journal, vol. 2021, pp. 5134-5141, 2021. DOI: https://doi.org/10.17973/MMSJ.2021_11_2021168.
    |
  9. D. Y. Suleiman, Q. Li, B. Li, Y. Zhang, B. Zhang, D. Liu et al., “Digital twin and artificial intelligence in machining: A bibliometric analysis”, Intelligent and Sustainable Manufacturing, vol. 2, no. 1, p. 10005, 2025. DOI: https://doi.org/10.70322/ism.2025.10005.
  10. Z. Zhao, S. Wang, Z. Wang, S. Wang, C. Ma and B. Yang, “Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining”, Journal of Intelligent Manufacturing, vol. 33, no. 4, pp. 943-952, 2022. DOI: https://doi.org/10.1007/s10845-020-01698-4.
    |
  11. M. Abed, A. Gameros, A. Mohammad and D. Axinte, “Swift feedback and immediate error control using a lightweight simulation approach – A case study of the digital-twin-in-the-loop for machining thin-wall structures”, Journal of Manufacturing Systems, vol. 71, pp. 309-322, 2023. DOI: https://doi.org/10.1016/j.jmsy.2023.09.016.
    |
  12. S. Natarajan, M. Thangamuthu, S. Gnanasekaran and J. Rakkiyannan, “Digital Twin-Driven Tool Condition Monitoring for the Milling Process”, Sensors, vol. 23, no. 12, p. 5431, 2023. DOI: https://doi.org/10.3390/s23125431.
    | |

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Published

2025-09-26

How to Cite

[1]
M. Vakulenko and S. Sapon, “Digital Twins as a Tool for Improving the Efficiency of the Milling Process”, Mech. Adv. Technol., vol. 9, no. 3(106), pp. 326–341, Sep. 2025.

Issue

Vol. 9 No. 3(106) (2025)

Section

Advanced Mechanical Engineering and Manufacturing Technologies

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Copyright (c) 2025 Максим Вакуленко, Сергій Сапон

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