Tool Wear Estimation using Support Vector Machines in Ball-nose End Milling

This paper introduces a method to determine the tool wear by measured cutting force in Ball-nose End Milling. The features will be extracted from the measured cutting force with different flank wear. As the adaptive window width in wavelet transform is an advantage for analyzing and monitoring the rapid transient of small amplitude of cutting force signals when cutting engagement changes along the sculptured surface tool path, wavelet transform (WT) is more effective than FFT monitoring index for ball-nose end milling. In this research, cutting force signals will be analyzed in time-frequency domain to explore sensitive monitoring features in ball-nose end milling slope surfaces. As a supervised method, support vector machines (SVM) was developed for the classification problem to take advantage of prior knowledge of tool wear and construct a hyperplane as the decision surface. In this paper, SVM will be formulated into regression problem to estimate tool wear rather than decision maker.