Chatter detection in milling of thin-walled parts using multi-channel feature fusion and temporal attention-based network

Chatter often occurs in the milling of thin-walled parts due to their high flexibility, which can seriously impair machining accuracy, surface quality, and production efficiency. In this paper, a chatter detection method in the milling of thin-walled parts based on deep learning is proposed. Multi-channel signal features in the time, frequency, and time–frequency domains are employed and their chatter sensitivities are evaluated. A novel temporal attention-based network is constructed. The self-attention mechanism, channel attention mechanism, and attentive statistics pooling are applied to capture the intra-feature correlation, assign the weights of different features, and calculate the variations and weights of features at multiple moments, respectively. The mutual information of distinct features is integrated into the channel attention mechanism to enhance the function of suppressing useless features and enhancing useful ones. The additive angular margin loss function is applied to strengthen intra-class aggregation and inter-class separation. Milling experiments on straight-wall parts are performed, and the classification performance of this model is evaluated in three scenarios. Afterward, milling experiments on curved thin-walled parts are conducted to verify the generalization performance. The results show that the proposed method can accurately detect chatter under varying machining conditions.