Chatter Detection in Robotic Milling Using Entropy Features

Abstract

Chatter detection in robotic milling is a difficult issue due to the complex dynamic behavior of robots. In this paper, a novel approach to detecting chatter in the robotic milling process is proposed. The method of improved complete ensemble empirical mode decomposition with adaptive noise is introduced for decomposing the milling vibration signals into a series of intrinsic mode functions (IMFs). The effective IMFs are chosen according to the correlation between the original signals and each IMF. Signal reconstruction is conducted using the selected IMFs. The weighted refined composite multiscale dispersion entropy is extracted from the reconstructed signals in order to characterize the chatter states. Then, a classification model is established for chatter detection. Experimental results prove that the proposed method is feasible for chatter detection in the robotic milling process under different robot configurations and machining parameters.