A fault evolution knowledge-driven adversarial meta-learning method for few-shot tool state recognition under variable working conditions

The health status of cutting tools is vital to ensuring workpiece quality and operational safety. While most existing research on tool condition monitoring focuses on progressive wear, such as estimating wear volume or remaining useful life, the more critical and abrupt failure mode of tool breakage has received less attention. In real-world manufacturing, stringent safety protocols lead to a severe scarcity of breakage data, presenting a typical few-shot learning challenge for tool state recognition (TSR). To tackle this issue, we propose a novel fault evolution knowledge-driven adversarial meta-learning (FEK-AML) method for few-shot TSR, where fault evolution knowledge is creatively formulated and integrated into the proposed adversarial meta-learning method, resulting in a physics-guided learning framework. Specifically, a feature extraction network is first trained using domain adversarial training to learn domain-invariant features while capturing the fault evolution knowledge. Subsequently, a metric-based meta-learning network is designed to transfer this knowledge for effective TSR under few-shot conditions. Milling experiments are performed on cutting tools in healthy, worn, and broken states under various working conditions. A series of TSR tasks is constructed, with only one fault sample per class available in the target domain. Comparative results show that FEK-AML effectively mines fault evolution knowledge and outperforms existing approaches in recognizing tool states under extremely limited data conditions, confirming its potential for reliable deployment in CNC monitoring systems to achieve accurate and robust TSR. Copyright © 2026. Published by Elsevier Ltd.