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Metalworking fluids—Mechanisms and performance

Process Monitoring in Grinding

Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring

A novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed. The grinding signals are collected by an acoustic emission (AE) sensor. A preprocessing method is presented to identify the grinding period signals from raw AE signals. Root mean square and variance of each decomposition level are designated as the feature vector using discrete wavelet decomposition. Various grinding experiments were performed on a surface grinder to validate the proposed classification system. The results indicate that the proposed monitoring system could achieve a classification accuracy of 99.39% with a cut depth of 10 μm, and 100% with a cut depth of 20 μm. Finally, several factors that may affect the classification results were discussed as well.

W.P. Dong

Papers

Raw Acoustic Emission Signal Analysis of Grinding Process