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Tool wear intelligent monitoring techniques in cutting: a review
Yaonan Cheng,Xiaoyu Gai,Rui Guan,Yingbo Jin,Mengda Lu,Ya Ding 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.1
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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Research on intelligent tool condition monitoring based on data-driven: a review
Yaonan Cheng,Rui Guan,Yingbo Jin,Xiaoyu Gai,Mengda Lu,Ya Ding 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7
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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