An Early Chatter Detection Method Based on Multivariate Variational Mode Decomposition and Chatter Correlation Factor

Abstract

Machining chatter is a self-excited vibration between the tool and the workpiece that affects surface quality and productivity. In this article, we propose a novel approach for early detection of machining chatter based on multivariate variational mode decomposition (MVMD) and chatter correlation factor (CCF). The proposed approach is featured by early chatter detection, and it is not susceptible to machining parameters. In order to obtain effective intrinsic mode functions (IMFs) and avoid mode mixing problem in MVMD, an automatic selection method of MVMD's parameters based on the difference of correlation coefficients (DCC) is developed. By using MVMD, multichannel signals are decomposed into multiple IMF groups with mode-alignment to extract common frequency components across all channel signals. Because multichannel signals are highly correlated at the chatter frequency when chatter occurs, the proposed CCF derived by correlation coefficients between the selected chatter-sensitive IMFs is sensitive to early chatter and can simultaneously measure the level of chatter. A support vector machine chatter identification model is obtained based on the CCF. The effectiveness of the proposed method is demonstrated by simulation and experimental findings.