Experimental Study on Entropy Features in Machining Vibrations of A Thin-walled Tubular Workpiece





In machining processes, chatter vibrations are always regarded as one of the major limitations for production quality and efficiency. Accurate and timely monitoring of chatter is helpful to maintain stable machining operations. At present, most chatter monitoring methods are based on the energy level at specified chatter frequencies or frequency bands. However, the spectral features of chatter could change during machining operations due to complexity and time-varying dynamics of the physical machining process. The purpose of this paper is to investigate the time-varying chatter features in turning of thin-walled tubular workpieces from the perspective of entropy. The airborne acoustics was selected as the source of information for machining condition monitoring. First, corresponding to the distinguishing surface topographies relevant to machining conditions, the features of the sound signal emitted during turning of the thin-walled cylindrical workpieces were extracted using the spectral analysis and wavelet packet transform, respectively. It was shown that the dominant vibration frequency as well as the energy distribution could shift with the transition of the machining status. After that, two relative entropy indicators based on the spectrum and the wavelet packet energy were constructed to identify chattering events in turning of the thin-walled tubes. The experimental results demonstrate that the proposed indicators could accurately reflect the transition of machining conditions with high sensitivity and robustness in comparison with the traditional FFT-based methods. The achievement of this study lays the foundations of the online chatter monitoring and control technique for turning of the thin-walled tubular workpieces.


Metrics Loading ...




How to Cite

Lu, K., Wang, X., Chen, X., Pang, X., & Gu, F. (2023). Experimental Study on Entropy Features in Machining Vibrations of A Thin-walled Tubular Workpiece. Journal of Dynamics, Monitoring and Diagnostics. https://doi.org/10.37965/jdmd.2023.155



Regular Articles