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Tool wear intelligent monitoring techniques in cutting: a review

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Abstract

Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry.

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Acknowledgments

The authors would like to acknowledge the financial support of National Natural Science Foundation of China (No. 52175394), Natural Science Foundation of Heilongjiang Province (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, Xiaoyu Gai, Rui Guan, Yingbo Jin, Mengda Lu & Ya Ding

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  1. Yaonan Cheng
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  2. Xiaoyu Gai
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  3. Rui Guan
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  4. Yingbo Jin
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  5. Mengda Lu
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  6. Ya Ding
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Correspondence to Yaonan Cheng.

Additional information

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.

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.

Rui Guan is a doctoral student of Harbin University of Science and Technology. Her main research focuses on intelligent monitoring technology for 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.

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., Gai, X., Guan, R. et al. Tool wear intelligent monitoring techniques in cutting: a review. J Mech Sci Technol 37, 289–303 (2023). https://doi.org/10.1007/s12206-022-1229-9

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