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

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.