| A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate | |
Lei XD(雷旭东)1,2 ; Wu XQ(吴先前)1; Zhang Z(张珍)1,2; Xiao KL(肖凯璐)1,2; Wang YW(王一伟)1,2; Huang CG(黄晨光)1,2,3
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| Corresponding Author | Wu, X. Q.([email protected]) |
| Source Publication | SCIENTIFIC REPORTS
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| 2021-03-22 | |
| Volume | 11Issue:1Pages:10 |
| ISSN | 2045-2322 |
| Abstract | It has been a vital issue to ensure both the accuracy and efficiency of computational models for analyzing the ballistic impact response of fiber-reinforced composite plates (FRCP). In this paper, a machine learning (ML) model is established in an effort to bridge the ballistic impact protective performance and the characteristics of microstructure for unidirectional FRCP (UD-FRCP), where the microstructure of the UD-FRCP is characterized by the two-point correlation function. The results showed that the ML model, after trained by 175 cases, could reasonably predict the ballistic impact energy absorption of the UD-FRCP with a maximum error of 13%, indicating that the model can ensure both computational accuracy and efficiency. Besides, the model's critical parameter sensitivities are investigated, and three typical ML algorithms are analyzed, showing that the gradient boosting regression algorithm has the highest accuracy among these algorithms for the ballistic impact problem of UD-FRCP. The study proposes an effective solution for the traditional difficulty of the ballistic impact simulation of composites with both high efficiency and accuracy. |
| DOI | 10.1038/s41598-021-85963-3 |
| Indexed By | SCI |
| Language | 英语 |
| WOS ID | WOS:000634963000022 |
| WOS Research Area | Science & Technology - Other Topics |
| WOS Subject | Multidisciplinary Sciences |
| Funding Project | National Natural Science Foundation of China[11672315] ; National Natural Science Foundation of China[11772347] ; Science Challenge Project[TZ2018001] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB22040302] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB22040303] |
| Funding Organization | National Natural Science Foundation of China ; Science Challenge Project ; Strategic Priority Research Program of Chinese Academy of Sciences |
| Classification | 二类/Q1 |
| Ranking | 1 |
| Contributor | Wu, X. Q. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/86429 |
| Collection | 流固耦合系统力学重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China |
| Recommended Citation GB/T 7714 | Lei XD,Wu XQ,Zhang Z,et al. A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate[J]. SCIENTIFIC REPORTS,2021,11,1,:10.Rp_Au:Wu, X. Q. |
| APA | 雷旭东,吴先前,张珍,肖凯璐,王一伟,&黄晨光.(2021).A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate.SCIENTIFIC REPORTS,11(1),10. |
| MLA | 雷旭东,et al."A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate".SCIENTIFIC REPORTS 11.1(2021):10. |
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| File Name/Size | DocType | Version | Access | License | ||
| Jp2021F183.pdf（1965KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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