| Machine learning based very high cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting | |
Shi T(时涛); Sun JY(孙经雨); Li JH(李江华); Qian GA(钱桂安) ; Hong YS(洪友士)
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| Source Publication | INTERNATIONAL JOURNAL OF FATIGUE
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| 2023-06 | |
| Volume | 171Pages:107585 |
| ISSN | 0142-1123 |
| Abstract | Few machine learning models are applied to investigate the influence of defect features on very high cycle fa tigue performance of additively manufactured alloys and these models usually suffer from data scarcity. Inter polation methods are run to enlarge dataset size and machine learning models are established to investigate the synergic influence of layer thickness, stress ratio, stress amplitude, defect size, shape and location on fatigue life of selective laser melted AlSi10Mg. Results show that the increases in defect distance to surface, circularity, and layer thickness favor higher fatigue life; however, the increases in stress amplitude, stress ratio, and defect size decrease fatigue life. |
| Keyword | Very high cycle fatigue (VHCF) Machine learning (ML) Selective laser melting (SLM) Fatigue life prediction Interpolation |
| DOI | 10.1016/j.ijfatigue.2023.107585 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000953337100001 |
| WOS Research Area | Engineering, Mechanical ; Materials Science, Multidisciplinary |
| WOS Subject | Engineering ; Materials Science |
| Funding Organization | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics [11988102] ; National Natural Science Foundation of China [11932020, 12072345] ; National Science and Technology Major Project [J2019 VI 0012 0126] ; Science Center for Gas Turbine Project [P2022 B III 008 001] |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Qian, GA (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China. ; Qian, GA (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/91823 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Shi T,Sun JY,Li JH,et al. Machine learning based very high cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting[J]. INTERNATIONAL JOURNAL OF FATIGUE,2023,171:107585.Rp_Au:Qian, GA (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China., Qian, GA (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China. |
| APA | 时涛,孙经雨,李江华,钱桂安,&洪友士.(2023).Machine learning based very high cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting.INTERNATIONAL JOURNAL OF FATIGUE,171,107585. |
| MLA | 时涛,et al."Machine learning based very high cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting".INTERNATIONAL JOURNAL OF FATIGUE 171(2023):107585. |
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| Jp2023Fa124.pdf（17637KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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