Abstract
In milling processing, the wear state of the tool has an essential influence on the processing quality. The machining process is not continuous in the cycloid milling process, and the signal of the empty tool part increases the difficulty of identifying the tool wear state. At present, most of the researchers use experimental data and cut out the signal of the empty tool part in the signal by data processing. However, it will affect the original signal to a certain extent and destroy the confidential information in the original signal. A novel method using variational auto-encoder (VAE) for tool wear status identification is proposed. Due to VAE has structural characteristics that reduce the dimensionality of high-dimensional data to lower dimensionality. This requires VAE to find and learn the significant features, which are hidden in the complex raw data. In this paper, the signals of the empty tool do not need to be cut out; the effective value of the three-phase current signals obtained in the real processing is converted into the form of three-dimensional color images. VAE is applied to extract features from the image samples and then realize the classification of different wear states of the tool. A large number of comparison experiments are conducted, and the result shows that the presented method has a better recognition performance for the actual processing data. It is more suitable for the recognition of tool wear status in the actual milling process.