Tool wear intelligent monitoring techniques in cutting: a review

• Published in: Journal of mechanical science and technology Vol. 37; no. 1; pp. 289 - 303
• Main Authors: Cheng, Yaonan, Gai, Xiaoyu, Guan, Rui, Jin, Yingbo, Lu, Mengda, Ding, Ya
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.01.2023; Springer Nature B.V; 대한기계학회
• Subjects: Control; Cutting wear; Decision making; Dynamical Systems; Engineering; Feature extraction; Industrial and Production Engineering; Intelligent manufacturing systems; Machining; Mechanical Engineering; Monitoring; Original Article; Pattern recognition; Tool wear; Vibration; 기계공학; Intelligent monitoring; Multisource-correlation sensor signal; Feature extraction; Pattern recognition; Tool wear
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-022-1229-9

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.