In order to solve the problem of low accuracy of tool wear detection due to the poor quality of generated data under small sample problems, a deep learning model based on data enhancement and feature fusion is proposed. Firstly, in order to solve the problem that there is no quality evaluation standard in the training process of the traditional generative adversarial network (GAN), the K nearest neighbor algorithm is proposed to test the data generated by the GAN model for the second time. The improved GAN model can be automatically trained to get the optimal model according to the second test...
