Milling chatter recognition based on dynamic and wavelet packet decomposition

Abstract

In metal milling, especially in the machining of low-stiffness workpieces, chatter is a key factor affecting many aspects such as surface quality, machining efficiency, and tool life. In order to avoid chatter, a milling chatter identification method based on dynamic wavelet packet decomposition (WPD) is proposed from the perspective of signal processing. The dynamic characteristics of the system are obtained by a hammer test. Based on the principle that the chatter frequency will reach a peak value near the natural frequency of the system, the original milling force signal is decomposed by WPD, and the sub-signals containing rich chatter information are selected for signal reconstruction. After numerical analysis and spectrum comparison, the reconstruction scheme is proved to be robust. Then, the time–frequency domain image of the reconstructed signal and the Hilbert spectrum feature are compared and analyzed to identify the chatter. Finally, the validity and reliability of the proposed method for chatter recognition are verified by experiments.