Milling Chatter Prediction Based on the Information Entropy and Support Vector Machine

This paper proposes a method based on information entropy and support vector machine predict chatter in milling, it uses multi-scale permutation entropy and wavelet packet energy as the milling chatter premonition features, we select parameters of these identifying features by experimental analysis, and predict chatter using the SVM which use these two identifying features as its input. The results show that this method can effectively predict the occurrence of milling chatter, correct rate is 95.8%.