A novel chatter detection method in micro-milling process using wavelet packet entropy

Chatter has always been a detrimental problem restricting the improvement of surface quality and processing efficiency in machining processes. Chatter detection is an essential approach to prevent these harmful effects, which aims to identify chatter occurrence at the early stage. Energy entropic methods explain chatter from randomness perspective of energy distribution and are often applied to detect chatter in machining processes. However, the energy of the milling system at the chatter frequency increase slowly at the onset of chatter, which lead to that energy entropy is sometimes challenging to identify weak chatter. In this paper, a new method based on combination of wavelet packet energy entropy and energy ratio is proposed to monitor chatter. By detecting the energy ratio of chatter frequency band, it is feasible to set a threshold to represent the different milling states and can accurately detect weak chatter. The proposed approach is assessed by using preliminary numerical simulation and several milling experiments, and the results demonstrate that the proposed method can effectively detect chatter.