Research On Monitoring Methods Of Tool Condition Based On Ensemble Empirical Mode Decomposition And Support Vector Machine

In this paper, we have a series of work for the method research for the wear state of cutting tool, which is been used in machining largely.Firstly, the mechanism of tool damage、the process of tool wearing and the tool blunt standard are been analyzed. Then setting up the experimental platform of multi-sensor for the monitoring of tool wear condition, and collecting turning vibration and cutting force data under different conditions.Secondly, author analysis the principle of the method of empirical mode decomposition and the decomposition process. Towards its shortage of modal aliasing, it used the improved overall empirical mode decomposition method. Then trying to use the method for the analysis of the vibration data of tool wear. It extracted the percentage value of the energy of each IMF component after decomposition as the feature, and discussed the characteristic’s repeatability and differences in detail.In addition, it introduced the basic idea of the statistical knowledge and the theoretical characteristics of support vector machine. Support vector machine is based on structural risk minimization principle. Then, it makes the energy percentage values of vibration data extracted from ensemble empirical mode decomposition and the mean of cutting force as training samples and testing samples of support vector machine model, In this way, it established a support vector machine classifier model. The test samples tell the correct of this model for classification and identification of feature value that the energy percentage values of vibration data extracted from ensemble empirical mode decomposition and the mean of cutting force.Finally, though comparing of BP and RBF neural network with support vector machine classifier in tool wear identification found that support vector machine showed good superiority in recognition accuracy, training time and reliance on the model structure. This paper combined EEMD decomposition method and support vector machine for tool wear monitoring, and achieved the anticipated results. Enriched the research methods of the tool wear condition monitoring, and also provided a theoretical basis for the further realization of online monitoring, In addition, it compared the characteristics of neural network and support vector machine in classification and recognition applications for providing a theoretical reference for the subsequent selecting of recognition model.