A Tool Wear Prediction Network Fusing Visual-Acoustic Cross-Attention and Interval Guidance

To address the limitations of single-modality feature characterization in tool wear monitoring, as well as the susceptibility of deep regression models to local optima and their poor robustness under complex working conditions, this paper proposes a tool wear prediction method based on a multimodal attention mechanism and interval guidance. First, a Region of Interest (ROI) algorithm removes visual background redundancy, constructing an adaptive ROI-based visual stream feature model to achieve spatial alignment across image dimensions. Concurrently, the Gramian Angular Field (GAF) is introduced to encode one-dimensional acoustic emission signals into two-dimensional time-frequency feature maps. Subsequently, utilizing the EfficientNetV2 backbone network, Bi-Directional Cross-Attention (BCA) is employed to facilitate deep interaction between visual and acoustic features, ensuring cross-modal semantic alignment. Second, interval classification is introduced as an auxiliary constraint to guide the model toward precise localization within the fine-grained regression space, while a priori constraints are applied to this feature space. To mitigate task weight uncertainty, a Stable Kendall Loss is utilized to adaptively balance the weights among the global regression, interval classification, and structured regression tasks. Finally, the proposed model is experimentally validated using the MATWI dataset. Experimental results demonstrate a classification accuracy of 94.58% and a minimum mean absolute error (MAE) of 6.6183 μm. Compared to state-of-the-art prediction methods and official benchmarks, the proposed model achieves superior performance across all evaluation metrics. Notably, it reduces prediction errors by approximately 65% relative to the benchmark model, fully validating its high accuracy and robust performance under complex working conditions.