Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

Abstract

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency, so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status. Firstly, the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology, and the features related to the tool wear status are extracted in the time domain, frequency domain and time-frequency domain to form a feature sample matrix; secondly, the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types: slight wear, normal wear and sharp wear to construct a target Finally, the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status. The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%; and compared with BP model, CNN model and SVM model, the accuracy indexes are improved by 9.48%, 3.44% and 1.72% respectively, which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.