Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing

Abstract

Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.