IMR OpenIR
The prediction model and experimental research of grinding surface roughness based on AE signal
Yin, Guoqiang1; Wang, Jiahui1; Guan, Yunyun1; Wang, Dong2; Sun, Yao1
Corresponding AuthorYin, Guoqiang(yinguoqiang@me.neu.edu.cn)
2022-04-16
Source PublicationINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
ISSN0268-3768
Pages13
AbstractThis 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.
KeywordAE signal Multi-information fusion model Grinding process Surface roughness
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
DOI10.1007/s00170-022-09135-x
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[51771193] ; National Natural Science Foundation of China[52005092] ; Fundamental Research Funds for the Central Universities[N2103013]
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Manufacturing
WOS IDWOS:000782874300002
PublisherSPRINGER LONDON LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/172801
Collection中国科学院金属研究所
Corresponding AuthorYin, Guoqiang
Affiliation1.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
Recommended Citation
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.
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