Adaptive removal of time-varying harmonics for chatter detection in thin-walled turning

Abstract

Chatter is a kind of undesired vibration with multiple adverse effects in machining operations, and online detection of chatter is crucial for chatter avoidance or suppression. However, it is observed that time-varying harmonic components are abundant in turning of thin-walled parts. The emergence of harmonics alters conventional frequency distribution patterns of the measured signal. In particular, it is a very challenging task to detect chatter in an early stage when the signal spectrum is dominated by the harmonics. This paper firstly investigates theoretically the relationship between the chatter frequency and the natural frequency based on a well-accepted chatter model of turning. The chatter frequency is found to vary far more slowly than the natural frequency during the thin-walled turning. Based on this finding, the adaptive signal predictor is proposed as a preprocessor for chatter detection, which can alleviate harmonics interference and noise with no prior knowledge of the workpiece dynamics. Furthermore, an improved adaptive filter algorithm is developed to enhance the performance of time-varying harmonics removal. Finally, simulation and experimental results validate the effectiveness of the proposed approach in harmonics removal and noise reduction for chatter detection in thin-walled turning.