Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting
文献类型：期刊论文
| 作者 | Li, Jun3 ; Yang, Zhengmao3; Qian, Guian2,3; Berto, Filippo1; Qian GA(钱桂安); Yang ZM(杨正茂)
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| 刊名 | INTERNATIONAL JOURNAL OF FATIGUE
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| 出版日期 | 2022-05-01 |
| 卷号 | 158页码:9 |
| 关键词 | Very-high-cycle fatigue (VHCF) Machine learning Selective laser melting (SLM) Fatigue life prediction Monte Carlo simulation (MCs) |
| ISSN号 | 0142-1123 |
| DOI | 10.1016/j.ijfatigue.2022.106764 |
| 通讯作者 | Yang, Zhengmao(zmyang@imech.ac.cn) ; Qian, Guian(qianguian@imech.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | MECHANICAL-PROPERTIES ; MODEL ; BEHAVIOR ; INDUSTRY |
| 资助项目 | 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 |
| WOS研究方向 | Engineering ; Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000792830500007 |
| 资助机构 | 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 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/89336]  |
| 专题 | 宽域飞行工程科学与应用中心 力学研究所_非线性力学国家重点实验室 |
| 通讯作者 | Yang, Zhengmao; Qian, Guian |
| 作者单位 | 1.Norwegian Univ Sci & Technol NTNU, Dept Mech & Ind Engn, Richard Birkelands Vei 2b, N-7491 Trondheim, Norway 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Jun,Yang, Zhengmao,Qian, Guian,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. |
| APA | Li, Jun,Yang, Zhengmao,Qian, Guian,Berto, Filippo,钱桂安,&杨正茂.(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 | Li, Jun,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. |
入库方式： OAI收割
来源：力学研究所
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