Recognition of chatter type based on improved neural network

Xiaozheng Xie; Yongpeng Xie; Rongzhen Zhao; Wuyin Jin; Yunping Yao

doi:10.1117/12.2010889

14 March 2013 Recognition of chatter type based on improved neural network

Xiaozheng Xie, Yongpeng Xie, Rongzhen Zhao, Wuyin Jin, Yunping Yao

Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87682K (2013) https://doi.org/10.1117/12.2010889
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore

Abstract

By studying chatter dynamic model, this paper discusses chatter phenomenon between metal cutting tool and workpiece during the cutting. From the point of energy, phase position difference of chatter mark, phase position difference of vibration mode, lagging phase position angle and change rate about cutting force relative to the cutting speed are respectively determined as characteristic parameter of regenerative, coupling vibration, lagging and fricative mode of chatter. With the four input parameters, multilayer feed forward neural network learning algorithm is used to diagnose the type of cutting chatter, and experiments show that this method is effective.It is essential to take appropriate measures on vibration suppression.

Citation Download Citation

Xiaozheng Xie, Yongpeng Xie, Rongzhen Zhao, Wuyin Jin, and Yunping Yao "Recognition of chatter type based on improved neural network", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87682K (14 March 2013); https://doi.org/10.1117/12.2010889

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