Machine learning based very high cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting

doi:10.1016/j.ijfatigue.2023.107585

IMECH-IR > 非线性力学国家重点实验室

	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)
发表期刊	INTERNATIONAL JOURNAL OF FATIGUE
	2023-06
卷号	171 页码:107585
ISSN	0142-1123
摘要	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.
关键词	Very high cycle fatigue (VHCF) Machine learning (ML) Selective laser melting (SLM) Fatigue life prediction Interpolation
DOI	10.1016/j.ijfatigue.2023.107585
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000953337100001
WOS研究方向	Engineering, Mechanical ; Materials Science, Multidisciplinary
WOS类目	Engineering ; Materials Science
项目资助者	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]
论文分区	一类
力学所作者排名	1
RpAuthor	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.
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/91823
专题	非线性力学国家重点实验室
作者单位	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
推荐引用方式 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.
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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