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A knowledge migration method based on multi-axis state coupling and multimodal data fusion for CNC process pattern recognition
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DOI：10.1088/2631-8695/ae1dcb.png)
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Abstract
En 中文
To address the challenges of extracting steady-state machining intervals in complex Computer numerical control (CNC) cutting, the high complexity of multimodal data fusion for machine-tool condition characterization, and the difficulty of deploying external sensors on structurally diverse CNC machines, this paper proposes a knowledge-transfer method that mines coupled multi-axis state information, fuses multimodal data to extract CNC machining features, and establishes a knowledge-distillation framework to guide cross-modal feature migration for CNC process-mode recognition. First, a multi-axis synchronous acquisition platform is employed to concurrently capture vibration and current signals under coordinated multi-axis machining, enabling the revelation of inter-axis state synergies across different process modes and the reliable extraction of steady-state machining intervals in complex cutting. Second, a Vibration-Guided Cross-Attention Mechanism (VGCAM) module is introduced together with a Self-Distilled Spatial Attention Mechanism (SDSAM). These components suppress redundancy in multimodal data while injecting high-frequency vibration cues into the current stream, thereby enhancing cross-modal feature representation and improving the efficiency of multimodal fusion. Finally, a multi-stage knowledge-distillation framework is constructed in which a teacher network extracts and fuses multimodal features; the fused representations and process knowledge are then transferred to a single-modal student network. The distilled student model achieves substantially fewer parameters and lower inference latency, and offers greater deployment convenience than external vibration sensors and similar add-on instrumentation. The proposed method is validated on a CNC milling testbed using nylon and aluminum workpieces. Results show that the student network, when supplied only with current root-mean-square (RMS) features, achieves high process-mode recognition accuracy while significantly reducing network parameters and inference latency, demonstrating the feasibility of on-site industrial deployment.
Keywords:
complex cutting
CNC process-segment identification
attention mechanism
multi-level distillation
knowledge transfer
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