Analysis of Tool Wear Condition Monitoring Based on Digital Twin Technology
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Keywords

Digital twin technology
Tool wear
Condition monitoring
Machining
Predictive maintenance

DOI

10.26689/jera.v9i4.11441

Submitted : 2025-07-08
Accepted : 2025-07-23
Published : 2025-08-07

Abstract

This paper focuses on the key issues of tool wear condition monitoring in the field of machining, and deeply discusses the application of digital twin technology in this aspect. This paper expounds the principle and architecture of digital twin technology, analyzes its specific methods in tool wear data acquisition, modeling, simulation, and real-time monitoring, and shows the significant advantages of this technology in improving the accuracy of tool wear monitoring and realizing predictive maintenance. At the same time, the challenges faced by digital twin technology in tool wear condition monitoring are discussed, and the corresponding development direction is put forward, aiming to provide theoretical reference and practical guidance for optimizing tool management by digital twin technology in the machining industry.

References

Chen Y, Wang Y, Qiao M, et al., 2024, Review on Key Technologies and Typical Applications of Digital Twin in Manufacturing. Aeronautical Manufacturing Technology, 67(11): 24–45.

Bao X, 2023, Online Monitoring and Early Warning Technology for Tool Wear of CNC Machine Tools. Farm Machinery Using & Maintenance, (11): 61–65.

You M, 2023, Application of Digital Twin Technology in Precision Tool Intelligent Factory. Science Technology and Innovation, (20): 9–13.

Ding M, Liu X, Yue C, et al., 2023, Tool Design, Fabrication and Control Technology for Intelligent Manufacturing Process. Journal of Mechanical Engineering, 59(19): 429–459.

Sun Y, Ye J, Hu J, 2023, Machining Process Optimization and Application of Digital Twin Technology. China Machinery, (27): 36–39.

Qi H, Li X, Tao Q, et al., 2024, Research Progress of Mechanical Process System Driven by Digital Twin. Acta Aeronauticae Astronauticae Sinica, 45(21): 32–64.

Zhang C, Zhou T, Hu T, et al., 2023, Construction Method of Digital Twin Model for Cutting Tools under Variable Working Conditions. Computer Integrated Manufacturing Systems, 29(06): 1852–1866.

Fang X, Zhang J, Cheng D, et al., 2023, Quality Control Method for Processing Key Parts of Marine Diesel Engine Driven by Digital Twin. Machinery Design & Manufacture, (03): 46–52.

Ye W, Guo B, Deng Z, et al., 2023, Research Progress and Development Trend of Key Technology of Intelligent Tool. Journal of Mechanical Engineering, 59(23): 265–282.

Song Q, Peng Y, Wang R, et al., 2023, Tool Wear State Recognition Method for Thin-wall Milling Driven by Digital Twin. Aeronautical Manufacturing Technology, 66(03): 46–52 + 60.

Wang X, Bai Q, Wang P, et al., 2021, Micro Milling Digital Twin Modeling Technology Research Progress. Aviation Manufacturing Technology, (20): 56–64.

Li C, Sun X, Hou X, et al., 2022, Online Tool Wear Monitoring Method for CNC Milling Driven by Digital Twin. China Mechanical Engineering, 33(01): 78–87.

Meng B, Li M, Liu X, et al., 2021, Research Progress on Architecture and Key Technologies of Machine Tool Intelligent Control System. Journal of Mechanical Engineering, 57(09): 147–166.

Xie N, Kou R, Liu X, 2021, Research on Digital Twin System of Tool Condition Monitoring Based on Cloud Computing. Machinery Manufacturing, 59(03): 78–82 + 92.

Sun H, Pan J, Zhang J, et al., 2019, Tool Digital Twin Model for Cutting Process. Computer Integrated Manufacturing Systems, 25(06): 1474–1480.