Recognition of Tool Cutting State Under Least Squares Support Vector Machine

A method of cutting tool condition based on wavelet packet excellent time-frequency characteristics and least squares support vector machine (LS-SVM) high-quality learning and generalization ability with small samples is presented for a cutting tool state recognition system.Minimum entropy criterion was adopted to decompose best wavelet packet for extracting feature of acoustic emission signals,the feature vectors were constructed by the AE signals energy relative percentage of each band accounted for the total energy,which were brought in multi-class LS-SVM classifier,and the classification recognition of different cutting tool states was achieved.Results show that the multi-class LS-SVM classifier is an efficient method for accurately recognizing the cutting tool states of the test samples that contain feature vectors,when γ=10(penalty factor) and σ²=1(RBF kernel parameter) in the LS-SVM multi-classification algorithm with Gaussian kernel function.