Abstract
Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on a priori information about the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index–based technique is developed for the estimation of the instantaneous chatter frequency to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.
Highlights
Chatter is a kind of unexpected self-excited vibration occurring in almost all machining processes, which limits productivity, damages the machined surface, and shortens the tool life [1]
Since the chatter is characterized by the progressive variation of the energy distribution, when chatter occurs, the decreasing randomness in vibration responses can be captured by energy entropy, a generalization of Shannon's entropy in the energy domain
To make such an index more sensitive to transitions of the cutting state, the energy entropy should be computed based on single signal modes that are obtained by signal decomposition techniques
Summary
Chatter is a kind of unexpected self-excited vibration occurring in almost all machining processes, which limits productivity, damages the machined surface, and shortens the tool life [1]. With the development of automation, the flexibility of the machine tool leads to diverse working conditions in milling processes. It is impossible to completely avoid chatter. Researchers devote their efforts to chatter prediction before milling. They focus on the stability lobe chart based on the identified dynamic model of the milling system [2]. Chatter may still occur when machining under stable conditions indicated by theoretical prediction, because the lobe chart is sensitive to the system parameters that are erratic in engineering
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