EEMD-based online milling chatter detection by fractal dimension and power spectral entropy

Abstract

Chatter is a kind of self-excited unstable vibration during machining process, which always leads to multiple negative effects such as poor surface quality, dimension accuracy error, excessive noise, and tool wear. For purposes of monitoring the processing state of milling process and detecting chatter timely, a novel online chatter detection method was proposed. In the proposed method, the acceleration signals acquired by sensor were decomposed into a series of intrinsic mode functions (IMFs) by the adaptive analysis method named ensemble empirical mode decomposition (EEMD), and the IMFs which contain the feature information of milling process were selected as the analyzed signals. The two indicators power spectral entropy and fractal dimension which is obtained by morphological covering method are introduced to detect the chatter features. Then, both the frequency characteristic and morphological feature of the extracted signals can be reflected by the two indicators. To verify the approach, milling experiments were performed; the experiment results show that the proposed method can detect chatter timely and effectively, which is important in the aspect of improving the milling quality. And finally, in order to detect milling chatter timely, an online milling chatter monitoring system was developed.