The prediction model and experimental research of grinding surface roughness based on AE signal

doi:10.1007/s00170-022-09135-x

IMR OpenIR

	The prediction model and experimental research of grinding surface roughness based on AE signal
	Yin, Guoqiang 1; Wang, Jiahui 1; Guan, Yunyun 1; Wang, Dong 2; Sun, Yao 1
通讯作者	Yin, Guoqiang(yinguoqiang@me.neu.edu.cn)
	2022-04-16
发表期刊	INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
ISSN	0268-3768
页码	13
摘要	This paper is based on the investigation of the relationship between the processing parameters and the characteristic parameters of acoustic emission signal (AE signal) including RMS value, ringing count, and signal spectrum during the grinding of several difficult-to-machine metallic materials; the variation of AE signal characteristic parameters and spectrum with the parameters of grinding depth a(p), grinding wheel velocity v(s), and feed velocity v(w) was analyzed, then the corresponding relationship between acoustic emission signal characteristic parameters and machining surface roughness was given. On this basis, the multi-information fusion algorithm based on BP neural network was used to reasonably fuse various characteristic parameters of AE signals, then predict and recognize the surface roughness of grinding workpieces. Finally, the established model was optimized by using genetic algorithm, which significantly improved the prediction accuracy and provided a reliable prediction model for the grinding of difficult-to-machine alloys, providing a feasible method for predicting surface roughness for practical production.
关键词	AE signal Multi-information fusion model Grinding process Surface roughness
资助者	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
DOI	10.1007/s00170-022-09135-x
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[51771193] ; National Natural Science Foundation of China[52005092] ; Fundamental Research Funds for the Central Universities[N2103013]
WOS研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Manufacturing
WOS记录号	WOS:000782874300002
出版者	SPRINGER LONDON LTD
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/172801
专题	中国科学院金属研究所
通讯作者	Yin, Guoqiang
作者单位	1.Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Yin, Guoqiang,Wang, Jiahui,Guan, Yunyun,et al. The prediction model and experimental research of grinding surface roughness based on AE signal[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2022:13.
APA	Yin, Guoqiang,Wang, Jiahui,Guan, Yunyun,Wang, Dong,&Sun, Yao.(2022).The prediction model and experimental research of grinding surface roughness based on AE signal.INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,13.
MLA	Yin, Guoqiang,et al."The prediction model and experimental research of grinding surface roughness based on AE signal".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022):13.