Chatter detection in milling based on singular spectrum analysis

Chatter is a frequently encountered problem in metal cutting field which reduces the machining efficiency and surface quality. Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based on singular spectrum analysis (SSA) is proposed. SSA is applied to process the cutting force signal and extract the feature that is closely related to the machining state. The singular value spectrum obtained by SSA is used to describe the energy distribution of the principal modes in the signal. On the basis of frequency domain chatter theory, singular value entropy (SVE) is adopted to evaluate the variation of energy distribution in the signal and the milling chatter is detected accordingly. Milling experiments under different cutting conditions are performed out to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can accurately identify the onset of chatter. This method is simple in operation and fast in calculation, which makes it have great potential for online chatter detection.