Tool Wear Condition Monitoring and Remaining Life Prediction under Complex Working Conditions：A Review

doi:10.3901/JME.2025.19.299

Abstract

Abstract: Manufacturers are now faced with the challenge of responding quickly to diverse markets and uncertain demands. Since there are more and more processing tasks for small batches of personalized products, the traditional tool condition monitoring technology and tool management mode is no longer applicable to the manufacturing environment with frequent changes in working conditions. Existing literature review on tool condition monitoring and remaining life prediction mostly focuses on a single fixed working condition. The latest achievements on tool condition monitoring and remaining life prediction under complex working conditions at home and abroad are systematically analyzed and summarized to bridge above limitations, and the further research direction is put forward. According to the progress of domestic and foreign scholars in recent years, the advances of investigation on sensor-based indirect tool condition monitoring method are firstly discussed in detail. The advance on applications of the signal processing and feature extraction, monitoring model selection under complex working conditions are reviewed from the perspective of single-sensor monitoring and multi-sensor joint monitoring respectively. Next, the current status of the application of the transfer learning method in complex working conditions tool wear condition monitoring technology are summarized; Then, the development status of remaining life prediction under complex working conditions is summarized from wear degradation model, data-driven model and hybrid model; Finally, the focus and development trend of tool wear monitoring and residual life prediction under complex working conditions in the future are generalized. It has certain theoretical reference and enlightenment for future research work.

Key words: complex working conditions, tool wear, tool condition monitoring, remaining useful life

CLC Number:

TG506

WANG Min, CHE Changjia, GAO Xiangsheng, ZAN Tao, GAO Peng, ZHANG Yunfei. Tool Wear Condition Monitoring and Remaining Life Prediction under Complex Working Conditions：A Review[J]. Journal of Mechanical Engineering, 2025, 61(19): 299-326.

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