Tool wear condition monitoring method based on Relevance Vector Machine

During the machining process, the tool wear state is closely related to the quality of the workpiece, which will directly affect the performance of the equipment. Not timely replacement of tools will lead to increased processing costs, low workpiece surface quality, and even damage to processing equipment. Therefore, research on tool wear monitoring is necessary for the tool processing industry. By analyzing the relationship between tool wear and sensor signals to determine the required acquisition signal. Aiming at the problem that the original sensor data cannot be directly used in the machining process, the signal processing technology is used to preprocess the original signal, remove the invalid signal collected during the cutting process, and use the filtering method to eliminate the singular points in the original signal. The time domain and frequency domain features of the data are extracted. Firstly, the features are optimized by the extreme random tree(ET), and the tool wear is taken as the target vector. The Pearson correlation coefficient(PCC) between the target vector and the filtered features is calculated, and the features with solid correlation with the target vector are selected. The results show that the relevance vector machine(RVM) model proposed in the research can effectively monitor tool wear.