Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach

doi:10.1016/j.msea.2020.140693

IMR OpenIR

	Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach
	Luo, Y. W.1; Zhang, B.1; Feng, X.1; Song, Z. M.2; Qi, X. B.3; Li, C. P.3; Chen, G. F.3; Zhang, G. P.2
通讯作者	Zhang, B.(zhangb@atm.neu.edu.cn) ; Zhang, G. P.(gpzhang@imr.ac.cn)
	2021-01-20
发表期刊	MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
ISSN	0921-5093
卷号	802 页码:11
摘要	Fatigue life scattering and prediction of Inconel 718 fabricated by selective laser melting were investigated using miniature specimen tests combined with statistical method and machine learning algorithms. The relationship between pore features and fatigue life of the selective laser melting-fabricated specimens was analyzed statistically. The results show that the increase in the size and/or the number of the pores in the specimens, and/or the decrease in the distance from a pore center to the specimen surface degraded the fatigue life. The machine learning and statistical analysis results reveal that the fatigue life are most closely related to the location of the pores compared with the size and the number of pores in the specimens. The finding may provide a potential way to get high-throughput statistical data helping in evaluating defect-dominated scattering and prediction of fatigue life of additive manufactured metallic parts using miniature specimen testing assisted by the machine learning approach.
关键词	Selective laser melting Pore feature Fatigue life Statistical analysis Machine learning
资助者	National Natural Science Foundation of China (NSFC) ; project of 'Manufacturing the swirl nozzles of the high pressure turbine by selective laser melting' ; Fundamental Research Project of Shenyang National Laboratory for Materials Science
DOI	10.1016/j.msea.2020.140693
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[51771207] ; National Natural Science Foundation of China (NSFC)[51671050] ; National Natural Science Foundation of China (NSFC)[51971060] ; project of 'Manufacturing the swirl nozzles of the high pressure turbine by selective laser melting' ; Fundamental Research Project of Shenyang National Laboratory for Materials Science[L2019R18]
WOS研究方向	Science & Technology - Other Topics ; Materials Science ; Metallurgy & Metallurgical Engineering
WOS类目	Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
WOS记录号	WOS:000613412700003
出版者	ELSEVIER SCIENCE SA
引用统计	被引频次：60[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/159127
专题	中国科学院金属研究所
通讯作者	Zhang, B.; Zhang, G. P.
作者单位	1.Northeastern Univ, Sch Mat Sci & Engn, Minist Educ, Key Lab Anisotropy & Texture Mat, 3-11 Wenhua Rd, Shenyang 110819, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Peoples R China 3.Corp Technol Siemens Ltd China, Mat & Mfg Qualificat Grp, Beijing 100102, Peoples R China
推荐引用方式 GB/T 7714	Luo, Y. W.,Zhang, B.,Feng, X.,et al. Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach[J]. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2021,802:11.
APA	Luo, Y. W..,Zhang, B..,Feng, X..,Song, Z. M..,Qi, X. B..,...&Zhang, G. P..(2021).Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach.MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,802,11.
MLA	Luo, Y. W.,et al."Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach".MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 802(2021):11.