Random Forest and Principle Components Analysis Based on Health Assessment Methodology for Tool Wear

doi:10.3901/JME.2017.21.181

Abstract

Abstract: The cutting tool is a critical part of the CNC machine. Its performance directly affects the machining accuracy. A method to assess the wear status of the cutting tools is proposed based on the combination of random forest analysis and PCA to establish a nonlinear mapping relationship between the features of the spindle current signals and tool wear. The degrees of the tool wear are divided into several classifications. Experiments have been conducted by testing the tools of different machining conditions. Wavelet packet decomposition, time domain statistics and frequency domain analysis are performed on the signals for feature extraction. Then the random forest method is applied to evaluate and classify different tool status. Compared with the results from AdaBoost which is a common boost classification method, results show that the proposed model is more accurate and robust. The method can avoid the problem of imbalance samples. In addition, it can be realized on the build-in sensors of the industrial CNC machines so that there is no need to change the original structure design to avoid the potential interferences of the spindle's dynamic processing performance, which enables a wide industrial applications of the tool wear assessment for the CNC machines.

Key words: health assessment, performance degradation, principle components analysis, random forest, tool wear

CLC Number:

TH17

ZHAO Shuai, HUANG Yixiang, WANG Haoren, LIU Chengliang, LIU Xiao, LIANG Xinguang. Random Forest and Principle Components Analysis Based on Health Assessment Methodology for Tool Wear[J]. Journal of Mechanical Engineering, 2017, 53(21): 181-189.

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