Vibration recognition for peripheral milling thin-walled workpieces using sample entropy and energy entropy

Due to the poor rigidity of thin-walled parts, vibration (chatter) is extremely easy to occur during the cutting process, affecting the precision, surface quality, and efficiency of part processing. The chatter in milling thin-walled workpieces becomes a more complex, nonlinear, and unstable signal as the dynamics of thin-walled workpieces change with time and position. To realize chatter detection, a method using ensemble empirical mode decomposition (EEMD) and nonlinear dimensionless indicators is proposed in this paper. Firstly, the EEMD is adopted to decompose the raw signal because it is suited for nonlinear and nonstationary signal. Subsequently, the correlation analysis is used to obtain chatter-related intrinsic mode function (IMF) components. When chatter occurs in the milling, time series complexity is changed and energy is transferred to the chatter bands. Therefore, the nonlinear sample entropy (SE) and energy entropy (EE) of IMFs can be extracted as two indicators. Then, principal component analysis (PCA) is adopted to further reduce the feature vector dimension. After that, an improved support vector machine (SVM) is developed to identify the chatter. Among them, genetic algorithm (GA) and grid explore (GE) are used to explore the best parameters of the SVM. In addition, off-line chatter prediction is employed to determine the cutting status under different machining parameters used in the experiments. At last, the cutting force signals are performed to verify the proposed method. The results show the proposed method using SE and EE can effectively detect the chatter, which provides an option for chatter detection.