Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 67, Issue 12, Pages 10844-10855Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2962465
Keywords
Chatter identification; k-nearest neighbor (kNN); machine learning
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Funding
- National Natural Science Foundation of China [11772244]
- National Science Fund for Excellent Young Scholars [51922084]
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Chatter is a kind of self-excited vibration which will destroy the manufacturing process badly. The detection or identification of chatter is attracting considerable interest for several years. In this article, a chatter identification method called reinforced k-nearest neighbors is proposed to realize both chatter identification and model self-learning. We conducted large amounts of experiments on a computer numerical control milling machine with different types of sensors in high-speed milling processes, where chatter occurs frequently. Signals from different sensors are compared and features are extracted by statistical methods. Then, a dimensional reduction method t-distributed stochastic neighbor embedding is used for extracting sensitive information and visualization. Finally, the proposed reinforced k-nearest neighbors is used for chatter identification under different cutting conditions and the experiment results show the effectiveness of the proposed method.
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