Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network

Unstable chatter seriously reduces the quality of machined workpiece and machining efficiency. In order to improve productivity, on-line chatter detection has attracted much interest in the past decades. Nevertheless, traditional methods are inevitably flawed due to the manually extracted features. Deep learning methods possess outstanding feature learning and classification capabilities, but the generalisation and accuracy are severely affected by the labelling and training of data. To address this, this paper proposed a novel hybrid deep convolutional neural network method combining an Inception module and a Squeeze-and-Excitation ResNet block (SR-block), namely ISR-CNN. The Inception module can automatically extract multi-scale features of cutting force signal to enrich the feature map. The SR-block can assign weights to different feature channels, thus suppressing useless feature maps and improving the model accuracy. Meanwhile, the introduction of SR-block also reduces the risk of gradient disappearance and speeds up the training of network. The generalisation and accuracy of the model is guaranteed by combining the two modules without training with transition state data. Milling tests were carried out on a wedge-shaped workpiece using different cutting parameters and tool overhang lengths to verify the accuracy and generalisability of the proposed method. The results showed that the proposed method outperforms other methods by achieving classification accuracy of on the validation and test sets 100% and 97.8%, respectively. In comparison to existing methods, the proposed method can correctly identify each machining state, including the transition states. Furthermore, the proposed method identifies the onset of chatter earlier than other methods, which is beneficial for chatter suppression.