Recognition of chatter type based on improved neural network

doi:10.1117/12.2010889

IR > 机电工程学院

	Recognition of chatter type based on improved neural network
	Xie, Xiaozheng 1; Xie, Yongpeng 2; Zhao, Rongzhen 1; Jin, Wuyin1 ; Yao, Yunping1
	2013
会议名称	4th International Conference on Graphic and Image Processing, ICGIP 2012
会议录名称	Proceedings of SPIE - The International Society for Optical Engineering
卷号	8768
会议日期	October 6, 2012 - October 7, 2012
会议地点	Singapore, Singapore
出版者	SPIE
摘要	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. © 2013 SPIE.
关键词	Cutting tools Feedforward neural networks Image processing Multilayer neural networks BP neural networks Chatter Coupling vibration Input parameter Multilayer feedforward neural networks Recognition Type Vibration suppression
DOI	10.1117/12.2010889
收录类别	EI
语种	英语
EI入藏号	20132916518044
EI主题词	Metal cutting
ISSN	0277786X
来源库	Compendex
分类代码	603.2 Machine Tool Accessories - 604.1 Metal Cutting
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/117760
专题	机电工程学院
作者单位	1.Key Laboratory of Digital Manufacturing Technology and Application, Ministry of Education, Lanzhou University of Technology, Lanzhou, 730050, China; 2.Lanzhou Petrochemical Company Sewage Treatment Plant, Lanzhou , 730060, China
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Xie, Xiaozheng,Xie, Yongpeng,Zhao, Rongzhen,et al. Recognition of chatter type based on improved neural network[C]:SPIE,2013.