Research on tool wear prediction based on the random forest optimized by NGO algorithm

Abstract

Tool wear, which affects both machining quality and manufacturing efficiency, is a major factor in restricting the development of machining intelligence during the machining process. The present study introduces a novel approach that makes use of the northern goshawk optimization (NGO) algorithm for optimizing the random forest (RF) to predict tool wear. First, an analysis of the vibration signal during the cutting process is conducted in both the time-domain and frequency-domain. Next, the S-transform is introduced to analyze the signal in time-frequency domain. Then, the minimal redundancy maximal relevance (mRMR) algorithm is used to screen the sensitive features of tool wear. Finally, milling experiments demonstrate the feasibility of the proposed method and can improve prediction accuracy. Compared with the evaluation indicators of RF, RF optimized by Genetic Algorithm (GA) and RF model prediction results by Gray Wolf Optimization (GWO), this method has higher prediction accuracy and better model fitting accuracy. The results of the study provide technical and theoretical support for the implementation of online intelligent tool wear monitoring, meanwhile for the advancement of cutting automation and the implementation of intelligent tool control.