Abstract
By the contrast with conventional methods, Acoustic Emission (AE) sensor possesses better performance for tool wear identifying. So, AE sensor is employed into identification of tool wear in this paper. Because of the diversity and time varying of AE, Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) are employed to analyze AE signal. EMD is suitable for analyzing non-stationary signal, and SVM possesses excellent classification capacity for small samples. According to these features, a method of identifying fault of tool wear based on EMD and SVM was presented. The characteristics of the tool under different conditions were extracted by EMD, and the tool wear was identified by SVM classifier. Experiment results show that the method based on EMD and SVM is suitable for identifying tool wear, and the rate of successfully identifying is 95%.
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