Abstract
Tool wear condition monitoring technology is one of the main parts of advanced manufacturing technology and is a hot research direction in recent years. A method based on the characteristics of acoustic emission signal and the advantages of wavelet packets decomposition theory in the non-stationary signal feature extraction is proposed for tool wear state monitoring with monitor the change of acoustic emission signal feature vector. In this paper, through the method, firstly, acoustic emission signal were decomposed into 4 layers with wavelet packet analysis, secondly, the frequency band energy of the have been decomposed signal were extracted, thirdly, the frequency band energy that are sensitive to tool wear were selected as feature vector, and then the corresponding relation between feature vector and tool wear was established , finally, the state of the tool wear can be distinguished according to the change of feature vector. The results show that this method can be feasibility used to monitor tool wear state in high speed milling.
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