When machining low stiffness workpieces,chatter is an important factor affecting surface quality,machining efficiency and tool life. A novel method for extracting chatter features has been proposed to more accurately identify milling machining status. The raw signal is preprocessed using comb filters and empirical mode decomposition. The intrinsic mode function ( IMF) is reconstructed based on the Pearson correlation coefficient,and the optimal parameters of variational mode decomposition (VMD) are obtained using the grey wolf optimization algorithm (GWO). To obtain frequency bands with rich chatter information,the energy entropy characteristics of each sub frequency band are calculated. Multi-scale permuta-tion entropy ( MPE) and multi-scale fuzzy entropy ( MFE) are calculated as chatter characteristics. Based on the range of entropy values,the optimal scale features in each processing state is selected. The analysis results indicate that the proposed method can effectively extract sensitive chatter features based on the optimal scale.
关键词
颤振监测/灰狼优化/VMD/多尺度排列熵/多尺度模糊熵
Key words
milling chatter detection/grey wolf optimization/VMD/multiscale permutation entropy/multiscale fuzzy entropy
维普
万方数据