A frequency band energy analysis of vibration signals for tool condition monitoring

The cutting vibration signals in different milling wear conditions are decomposed and reconstructed by using the wavelet packet transform and feature extraction with conditions information is obtained by using proper and scientific methods to extract from signals. The effect of cutting parameter is on energy parameters was taken into account while extracting energy parameters of tool wear characteristic vector, this made the extracted parameters of characteristic vector be more sensitive to tool wear and the sensitivity to cutting parameters be the minimum. The recognition method for tool wear condition was studied through artificial neural network and wavelet packet analysis, and relevant RBF network was established. The results showed that RBFN can be successfully used to recognize and analyze tool wear conditions.