Early chatter detection in thin-walled workpiece milling process based on multi-synchrosqueezing transform and feature selection

The onset of chatter is extremely disadvantageous to machining process, which will induce poor surface quality, low processing efficiency and severe tool wear, especially in the milling of weakly rigid thin-walled workpieces, so it should be detected as early as possible to avoid further damage. To handle this issue, a multi-synchrosqueezing transform (MSST) based early chatter detection method as well as a novel feature selection algorithm is proposed in this paper to monitor the occurrence of chatter in thin-walled parts machining. MSST is firstly conducted on the sampled vibration signal to generate a time-frequency (TF) representation. The periodic components associated with spindle rotation are removed from the TF matrix and the chatterre-lated signal is recovered by multi-ridge detection. Then, features in time and frequency domains are extracted from the reconstructed chatter signal. A feature selection algorithm combining multi-feature distance evaluation technique (MFDET) and distance correlation analysis is subsequently presented to determine the optimal feature subset. Compared with other chatter indicators given in existing works, the selected compact feature set is more discriminative and has low redundancy. Furthermore, random forest (RF) is utilized to intelligently diagnose the machining stability. Several experiments under constant and variable conditions are performed to evaluate the performance of the proposed method in terms of early chatter detection. The experimental results demonstrate that the presented chatter monitoring method is highly efficient and robust, which can recognize the chatter onset timely and accurately for thin-walled parts milling under different machining conditions.