DFS-TSPML: Distribution Feature Screening and Three-Stage Physical Information Meta-Learning for Multi-Condition Tool Wear Monitoring

Le résumé fourni par la source

Under varying conditions, the stage evolution of tool wear exhibits disparate characteristics, with pronounced differences in wear rate and transition points across stages. This variability renders real-time, high-precision measurement and unified monitoring of tool wear exceptionally challenging. To address this issue, a data-distribution-based feature screening and three-stage physics-informed meta-learning are proposed in this paper for multi-condition tool wear unified monitoring. First, high-dimensional data acquired during the cutting process are exploited to construct a time–frequency feature matrix. Information criteria are employed to identify the distribution of tool wear increments. And features whose exhibit maximal multidimensional similarity to both the wear state and its distribution are selected via FSI. Then, a novel three-stage wear physical model is embedded into the monitoring model. These physics-based constraints, together with the measured data, jointly restrict the solution space. An improved meta-optimizer is subsequently adopted to distill the domain-invariant representations shared across multi-conditions. Finally, the monitoring model is rapidly adapted to a new condition with only a handful of samples, enabling real-time and accurate tool wear monitoring. A multi-condition wear experiment conducted on indexable CNC milling inserts demonstrated that, compared with state-of-the-art methods, the proposed method exhibits markedly higher precision, stability, and adaptability in new conditions.

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