A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering. (September 2021) - Dun, Yichao; Zhu, Lida; Yan, Boling; Wang, Shuhao
Highlights: Chatter detection in milling of thin-walled workpiece. Nonlinear compression of experimental measured signals based on auto-encoding. Density-distance metric based hybrid clustering method to detect chatter. Comparison between other methods, on massive experimental measured signals. Abstract: Chatter is a typical fault in milling, which is a self-excited vibration. Chatter can be diagnosed by using several methods, such as Delay Differential Equation (DDE) modelling methods, Artificial Neural Network (ANN) supervised learning methods, etc. These methods are effective. But they have some limitations, such as susceptible to measurement errors, require all data to be labeled, etc. In this paper, an unsupervised method to diagnose the chatter stability in milling according to massive unlabeled measured dynamic signals is proposed. The proposed method is not susceptible to measurement errors, don't require labels, and is robust. The dynamic signals are acquired from several milling trials. In the proposed method, the measured signals are compressed by using auto-encoding based method. Then, a hybrid clustering method based on both density metric and distance metric is used to cluster the compressed signals. The proposed method achieves a detection accuracy of 95.6033% on the experimental measured dynamic signals.