Tool wear identification and prediction method based on stack sparse self-coding network

Abstract

In the process of metal cutting, the effective monitoring of tool wear is of great significance to ensure the machining quality of parts. Aiming at the problem of tool wear monitoring, a tool wear recognition and prediction method based on stack sparse self-coding network is proposed. This method can simplify the establishment process of monitoring model, monitor the tool wear according to different task requirements, and guide the tool replacement in the actual cutting process. Firstly, unsupervised K-means clustering is used to divide the tool wear stage, and the feature set is marked. Secondly, the parameters of stack sparse self-coding network layer are determined by trial, and the sensitive features that can reflect the tool wear process are obtained. Finally, the tool wear identification model of stack sparse self-encoder and the tool wear prediction model of BP neural network are established respectively, and the smoothing correction method is used to further improve the prediction accuracy. The experimental results show that the established tool wear identification and prediction model can accurately monitor the tool wear state and wear amount, and has a certain reference value for efficient tool change in the actual metal cutting process.