Research on Classification and Recognition of Micro Milling Tool Wear Based on Improved DenseNet

In order to detect the wear of micro-milling tools, proposing a deep learning neural network detection method based on improved DenseNet. Firstly, the Inception structure adds a multi-channel fusion of features. Secondly, the SE self-attention blocks are added. Finally, the whole network uses the pre-activation structure. The experiment results validate the effectiveness of the proposed model. The results show that the improved DenseNet can accurately identify and classify the 1 mm micro-milling tool wear state.