Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination

Abstract

Chatter is a common state in the end milling, which has important influence on machining quality. Early chatter detection is a prerequisite for taking effective measures to avoid chatter. However, there are still many difficulties in the feature extraction of chatter detection. In this article, a novel online chatter detection method in end milling process is proposed based on wavelet packet transform (WPT) and support vector machine recursive feature elimination (SVM-RFE). The measured vibration signal in the machining process was preprocessed by WPT. The original feature set of chatter composed of ten time-domain and four frequency-domain feature parameters was obtained via calculating the reconstructed signal. Then feature weights are computed by SVM-RFE, and the obtained feature ranking list was to indicate their different importance in chatter. The optimal feature subset was selected according to the prediction accuracy. The proposed method is described and applied to incipient chatter over conventional methods in identifying the transition from a stable to unstable state. Some milling tests were conducted and the experiment results was shown that the impulse factor and onestep autocorrelation function were the sensitive chatter features.