Unsupervised Tool State Classification Based on Multi-Modal Fusion

As a critical process in the manufacturing of core components for equipment, milling directly influences workpiece quality and production efficiency through the process of tool wear. However, current monitoring methods face limitations in extracting comprehensive wear features from multi-source signals, while data-driven approaches are constrained by scarce wear samples and imbalanced stage distribution in practical applications. To address these challenges, this paper presents an unsupervised tool wear state classification method based on multi-modal feature fusion. The proposed approach establishes a dual-channel feature extraction model that processes vibration signals and workpiece surface texture images separately, enabling complementary-information integration through a fusion strategy. An autoencoder architecture performs unsupervised dimensionality reduction and reconstruction of the fused high-dimensional features, extracting low-dimensional representations strongly correlated with wear states. By optimizing feature discriminability through reconstruction error minimization, the method effectively mitigates the impact of limited labeled data on model performance. Experimental validation using cemented carbide milling cutters demonstrates that the proposed method achieves a classification accuracy of 93.33% under unsupervised conditions, significantly outperforming conventional unsupervised approaches. This research offers a viable approach for intelligent perception and precise analysis of cutting processes, presenting practical value for advancing equipment condition monitoring, process optimization, and predictive maintenance within intelligent manufacturing systems.