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| Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting | |
Li J(李俊)1; Yang ZM(杨正茂)1 ; Qian GA(钱桂安)1,2; Berto F3
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| Corresponding Author | Yang, Zhengmao([email protected]) ; Qian, Guian([email protected]) |
| Source Publication | INTERNATIONAL JOURNAL OF FATIGUE
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| 2022-05-01 | |
| Volume | 158Pages:9 |
| ISSN | 0142-1123 |
| Abstract | Few machine learning (ML) models were applied for very-high-cycle fatigue (VHCF) analysis and these methods encounter limitations in data sparsity and overfitting. The present work aims to overcome data sparsity and propose an easy-to-use and nonredundant ML model for VHCF analysis. Monte Carlo simulation (MCs) is run to enlarge dataset size and a ML method is proposed to investigate the synergic influence of defect size, depth, location and build orientation on Ti-6Al-4V. The coefficient factor that indicates the percentage variation between the predicted and experimental fatigue lives can reach up to 0.98, meaning that the model demonstrates good prediction accuracy. |
| Keyword | Very-high-cycle fatigue (VHCF) Machine learning Selective laser melting (SLM) Fatigue life prediction Monte Carlo simulation (MCs) |
| DOI | 10.1016/j.ijfatigue.2022.106764 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000792830500007 |
| WOS Keyword | MECHANICAL-PROPERTIES ; MODEL ; BEHAVIOR ; INDUSTRY |
| WOS Research Area | Engineering ; Materials Science |
| WOS Subject | Engineering, Mechanical ; Materials Science, Multidisciplinary |
| Funding Project | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; National Natural Science Foundation of China[11872364] ; National Natural Science Foundation of China[11932020] ; National Natural Science Foundation of China[12072345] ; National Science and Technology Major Project[J2019-VI-0012-0126] ; CAS Pioneer Hundred Talents Program |
| Funding Organization | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; National Natural Science Foundation of China ; National Science and Technology Major Project ; CAS Pioneer Hundred Talents Program |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Yang, Zhengmao ; Qian, Guian |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/89337 |
| Collection | 宽域飞行工程科学与应用中心 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China; 3.Norwegian Univ Sci & Technol NTNU, Dept Mech & Ind Engn, Richard Birkelands Vei 2b, N-7491 Trondheim, Norway |
| Recommended Citation GB/T 7714 | Li J,Yang ZM,Qian GA,et al. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting[J]. INTERNATIONAL JOURNAL OF FATIGUE,2022,158:9.Rp_Au:Yang, Zhengmao, Qian, Guian |
| APA | 李俊,杨正茂,钱桂安,&Berto F.(2022).Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting.INTERNATIONAL JOURNAL OF FATIGUE,158,9. |
| MLA | 李俊,et al."Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting".INTERNATIONAL JOURNAL OF FATIGUE 158(2022):9. |
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| Jp2022FA406_2022_Mac（3392KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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