An adaptive grinding chatter detection method considering the chatter frequency shift characteristic

At present the majority of the research works on machining chatter detection are based on energy level monitoring of certain chatter frequencies which are premised to be invariant. However, chatter frequencies are very complicated with multi-frequency/frequency band, time-variant characteristics and are also affected by many factors in the machining process. These characteristics make the traditional chatter detection methods vulnerable under the practical complex machining conditions. In this study, the complexity of grinding chatter frequency was investigated firstly. The influence from change of machining parameters and machine tool dynamic characteristics on grinding chatter frequencies were analyzed theoretically. Then a high accuracy and robust chatter detection method adaptable for dealing with complex machining conditions was proposed. Specifically, information fusion of feed motor current and vibration signal based on cross wavelet transform was utilized to extract chatter information adaptively and completely, whereas the harmonics of forced vibration and other noises were eliminated. To assess the grinding stability, two chatter indicators, i.e. normalized spectral entropy and logarithmic spectrum distance based on the cross wavelet transform spectrogram were devised. The efficacy of the proposed method was successfully verified through grinding operations in which both constant and variable machining were carried out. The results show that the developed method is capable of robustly detecting chatter in all the tested machining conditions and both the indicators have suitable properties to enable them for adaptive chatter detection. By comparison, spectrum distance has a monotonic relationship with the severity of chatter vibration, which makes it a comparatively better indicator.