ArticleSensors (Basel, Switzerland)2025
Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Research on the Timing of Replacing Worn Milling Cutters by Using Wear Transition Percentage Constructed Based on Spindle Current Clutter Signals.Sensors (Basel, Switzerland) · 2025Article
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5 authors.
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Abstract
Accurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learning strategy in a single source domain. They cannot fully utilize the sensor data distribution information of multiple cutting parameters, hindering recognition performance improvement. Thus, this paper proposes a wear-state recognition method for variable cutting parameters based on multi-source unsupervised domain adaptation. First, non-stationary Transformer encoders extract non-stationary common features; then, sliced Wasserstein distance-based domain-specific feature distribution alignment and classifier output alignment scale down the domain shift and make multi-domain distribution synchronous alignment less complex. Finally, the milling experiments with variable cutting parameters are conducted to validate the recognition performance of the proposed method.
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