Intelligent tool wear monitoring approach in milling of titanium alloys

Tool wear exerts a critical influence on machining stability and workpiece quality, making its accurate, intelligent monitoring indispensable for preventing tool failure and ensuring product consistency. Although direct assessment via wear imagery is possible, it requires interrupting the machining process and thus is impractical for real‐time production. A more viable solution is to leverage in‐process signals—such as vibration—to enable continuous monitoring. Here, we present a Signal processing method that Beluga whale optimization‐Successive variational mode decomposition (BWO‐SVMD) for noise suppression, followed by the S‐transform to produce high‐resolution time–frequency representations. Based on these denoised spectrograms, we develop an intelligent monitoring model that integrates a multi‐scale convolutional neural network (MSCNN), long short‐term memory (LSTM) units, and a channel–spatial attention mechanism. Experimental results demonstrate that our model achieves 96.25 % classification accuracy, a Kappa coefficient of 0.9686, and a total computation time of 320.64 s. Compared with CNN‐LSTM‐Attention, MSCNN‐Attention, and MSCNN‐LSTM baselines, it improves average accuracy by 1.89 %, 8.02 %, and 6.67 % and Kappa by 0.0732, 0.1374, and 0.2009, respectively. Although training time increases by 10.2 %–14.2 %, the substantial gains in predictive performance justify the additional computational cost.