2007 年 1 巻 4 号 p. 760-770
Vibration signals of tool wear are proved to be non-stationary ones. They usually carry the dynamic information of tool wear and are very useful for tool wear condition recognition. The wavelet analysis is especially suitable for non-stationary signal processing and the artificial neural network is a very good tool for signal identification. In this paper, a new efficient tool wear monitoring method based on the wavelet packet transform and the artificial neural network was presented. 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. Wavelet pretreatment distills feature information from original signal as input vectors of decision net in order to reduce input data dimension, optimize the net construction, compute complexity and decrease the decision the errors. The theoretical background of wavelet packet transform and grey relational degree analysis optimization radial based function network (RBFN) was given. The effect of cutting parameters 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 RBFN was established. The results showed that RBFN can be successfully used to recognize and analyze tool wear conditions.
