State Recognition of Tool Wear Based on Improved Empirical Mode Decomposition and Least Squares Support Vector Machine

End effect, stop standard and artificial components of empirical mode decomposition (EMD) are improved in this paper, and the contrast test of simulation signal verifies the feasibility of the improved method. Acoustic emission signals were collected in the cutting process and the improved empirical mode decomposition method was used to decompose them into a series of intrinsic mode function components. Artificial components that were produced in the decomposition were then removed according to correlation coefficients of the IMF components and original signal, and normalized energy of the IMF components was extracted as characteristic vector. The characteristic vector was divided into two groups: one group was used to train the least squares support vector machine and the other was used to identify the tool wear state. The experiment result shows that it can effectively characterize tool wear states.