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Research on intelligent tool condition monitoring based on data-driven: a review

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Abstract

The tool condition monitoring (TCM) can sense the real-time conditions of the tool to a large extent and warn the tool failure as early as possible. It can effectively improve processing efficiency, reduce production cost, and ensure production safety. With the rise of artificial intelligence technology, whether digital images obtained based on direct method or physical signals obtained through sensors by the indirect method can be regarded as valuable data. Using the artificial intelligence method to extract and identify the effective features in the data, mining the relationship between the tool wear or breakage and data is the key technology and difficulty of the intelligent tool condition monitoring. In this paper, the data representing tool wear or breakage characteristics are divided into image data and signal data. Moreover, the way to obtain high-quality data through image acquisition technology and multi-sensor fusion technology is discussed. Then the key principles and methods of feature extraction and decision making in TCM are studied. Finally, the future research direction is prospected based on the application of tool condition monitoring.

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Acknowledgments

The authors would like to acknowledge the Joint Guidance Project of Heilongjiang Provincial Natural Science Foundation (No. LH2021E083) in the production of this work. The authors are grateful to the anonymous reviewers for valuable comments and suggestions, which helped to improve this study.

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Authors and Affiliations

  1. College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Yaonan Cheng, Rui Guan, Yingbo Jin, Xiaoyu Gai, Mengda Lu & Ya Ding

  2. Harbin Vocational and Technical College, Harbin, China

    Rui Guan

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  1. Yaonan Cheng
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Correspondence to Yaonan Cheng.

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Yaonan Cheng received his Ph.D. from Harbin University of Science and Technology, China. He is currently a Professor at College of Mechanical and Power Engineering, Harbin University of Science and Technology. His current research focuses on metal cutting theory and tool technology, intelligent manufacturing technology and efficient machining technology for difficult-to-machine materials.

Rui Guan is a doctoral student of Harbin University of Science and Technology. She is also a teacher in Harbin Vocational and Technical College. Her main research focuses on intelligent monitoring technology for cutting tool wear or breakage in cutting difficult-to-machine materials, metal cutting principles and tools.

Yingbo Jin is a master student of Harbin University of Science and Technology. His main research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

Xiaoyu Gai is a doctoral student of Harbin University of Science and Technology, Harbin, China. His current research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

Mengda Lu is a master student of Harbin University of Science and Technology. His main research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

Ya Ding is a master student of Harbin University of Science and Technology. Her main research focuses on machining technology for difficult-to-machine materials, metal cutting principles and tools.

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Cheng, Y., Guan, R., Jin, Y. et al. Research on intelligent tool condition monitoring based on data-driven: a review. J Mech Sci Technol 37, 3721–3738 (2023). https://doi.org/10.1007/s12206-023-0637-9

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