Acoustic signal based tool wear monitoring system for belt grinding of superalloys

Abstract

Belt grinding is an efficient material removal and finishing machining process, which is usually applied in super alloy machining process and gives workpieces their final finish through the cutting ability of abrasive grains. However, the behavior of belt grinding process is highly dependent on the tool performance, and wear of grinding belt will not only reduce the grinding efficiency but also increase the cutting heat accumulation, causing burn of workpiece surface. This paper presents an acoustic signal based tool wear recognition method for robotic belt grinding process. To evaluate the wear conditions of grinding belt, time domain, frequency domain and energy distribution of grinding acoustic signal are analyzed through energy calculation, fast Fourier transform (FFT) and wavelet decomposition.