| 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 Author | Yin, Guoqiang(yinguoqiang@me.neu.edu.cn) |
| 2022-04-16 | |
| Source Publication | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
 |
| ISSN | 0268-3768 |
| Pages | 13 |
| Abstract | 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. |
| Keyword | AE signal Multi-information fusion model Grinding process Surface roughness |
| Funding Organization | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| DOI | 10.1007/s00170-022-09135-x |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[51771193] ; National Natural Science Foundation of China[52005092] ; Fundamental Research Funds for the Central Universities[N2103013] |
| WOS Research Area | Automation & Control Systems ; Engineering |
| WOS Subject | Automation & Control Systems ; Engineering, Manufacturing |
| WOS ID | WOS:000782874300002 |
| Publisher | SPRINGER LONDON LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/172801 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Yin, Guoqiang |
| Affiliation | 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 |
| 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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