Research and Application of Wear Prediction Method of NC Milling Cutter Based on Data-Driven

Abstract

As the basic equipment of the industry, the tool wear will affect the quality of the processed products and production efficiency, so how to use the tool condition monitoring information to accurately predict the residual life of the tool has a high application value. In the application background of industrial big data and industrial equipment fault prediction and health management, the predictive evaluation of milling cutter wear degree of CNC machine tool is carried out by using machine learning method, and then the tool life is accurately predicted. In the process of research and analysis, the milling cutter original data were preprocessed and feature extraction was carried out. A feature set screening method was adopted to screen out feature sets related to the degree of tool wear degradation. Finally, the remaining life of milling cutter was accurately predicted by machine learning model.