Timely online chatter detection in end milling process

Chatter is one of the most unexpected and uncontrollable phenomenon during the milling operation. It is very important to develop an effective monitoring method to identify the chatter as soon as possible, while existing methods still cannot detect it before the workpiece has been damaged. This paper proposes an energy aggregation characteristic-based Hilbert-Huang transform method for online chatter detection. The measured vibration signal is firstly decomposed into a series of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition. Feature IMFs are then selected according to the majority energy rule. Subsequently Hilbert spectral analysis is applied on these feature IMFs to calculate the Hilbert time/frequency spectrum. Two indicators are proposed to quantify the spectrum and thresholds are automatically calculated using Gaussian mixed model. Milling experiments prove the proposed method to be effective in protecting the workpiece from severe chatter damage within acceptable time complexity.