Identification and three-dimensional absorption of time-varying potential chatter during robotic milling

Abstract

• A new concept of "potential chatter" is proposed. • A modulation-inspired potential chatter identification framework is proposed. • TO-AVA is designed to handle three-dimensional potential chatter. Robotic milling constitutes an important component of robotized intelligent manufacturing, gaining increasing popularity for subtractive manufacturing of large components. Extensive efforts have been devoted to the analysis and suppression of robot chatter to enhance milling efficiency and quality. However, the dynamic characteristics of robots are highly pose-dependent, leading to time-varying low-frequency chatter. Meanwhile, the low-frequency chatter is continuously influenced by the action of vibration suppression devices, making it challenging to consistently track and suppress time-varying chatter. To address this, this paper proposes a new concept, the potential chatter mode, to more accurately describe the target mode that requires attention in online chatter suppression. Inspired by the modulation mechanism between modal vibrations and spindle rotation during robotic milling, a potential chatter mode identification framework is developed. By investigating the distribution pattern of vibration spectra under the modulation mechanism, and integrating filtering, demodulation, signal decomposition, and vibration energy evaluation, it achieves the online identification of the time-varying frequency of potential chatter. Furthermore, the potential chatter exhibits a three-dimensional time-varying direction, whereas the existing suppression devices are generally designed to operate in one or two directions. This paper develops a novel three-dimensional orthogonal adaptive vibration absorber (TO-AVA) based on magnetorheological elastomers (MRE). By incorporating a parallel negative stiffness mechanism and parameter design, the TO-AVA can handle the three-dimensional time-varying direction of potential chatter. Validation experiments of robotic milling are conducted, which involves various process parameters and time-varying potential chatter across different directions, frequencies, and states. The results demonstrate that the developed framework can accurately identify time-varying potential chatter and effectively suppress it using the TO-AVA.