Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens

doi:10.1016/j.jmst.2018.12.011

IMR OpenIR

	Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens
	Wan, H. Y.1,2; Chen, G. F.3; Li, C. P.3; Qi, X. B.3,4; Zhang, G. P.1
通讯作者	Zhang, G. P.(gpzhang@imr.ac.cn)
	2019-06-01
发表期刊	JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY
ISSN	1005-0302
卷号	35 期号:6 页码:1137-1146
摘要	This overview firstly introduces the state-of-the-art research progress in length scale-related fatigue performance of conventionally-fabricated metals evaluated by miniature specimens. Some key factors for size effects sensitive to microstructures including the specimen thickness, grain size and a ratio between them are highlighted to summarize some general rules for size effects. Then, ongoing research progress and new challenges in evaluating the fatigue performance of additive manufactured parts controlled by location-specific defects, microstructure heterogeneities as well as mechanical anisotropy using miniature specimen testing technique are discussed and addressed. Finally, a potential roadmap to establish a data-driven evaluation platform based on a large number of miniature specimen-based experiment data, theoretical computations and the 'big data' analysis with machine learning is proposed. It is expected that this overview would provide a novel strategy for the realistic evaluation and fast qualification of fatigue properties of additive manufactured parts we have been facing to. (C) 2019 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
关键词	Additive manufacturing Miniature specimen Fatigue Size effect Location-specific Data analysis
资助者	National Natural Science Foundation of China (NSFC)
DOI	10.1016/j.jmst.2018.12.011
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)[51571199]
WOS研究方向	Materials Science ; Metallurgy & Metallurgical Engineering
WOS类目	Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
WOS记录号	WOS:000464017000023
出版者	JOURNAL MATER SCI TECHNOL
引用统计	被引频次：61[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/321006/132831
专题	中国科学院金属研究所
通讯作者	Zhang, G. P.
作者单位	1.Chinese Acad Sci, Shenyang Natl Lab Mat Sci, Inst Met Res, 72 Wenhua Rd, Shenyang 110016, Liaoning, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Liaoning, Peoples R China 3.Siemens Ltd, Mat & Mfg Qualificat Grp, Corp Technol, Beijing 100102, Peoples R China 4.Tsinghua Univ, State Key Lab Tribol, Beijing 100084, Peoples R China
推荐引用方式 GB/T 7714	Wan, H. Y.,Chen, G. F.,Li, C. P.,et al. Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens[J]. JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY,2019,35(6):1137-1146.
APA	Wan, H. Y.,Chen, G. F.,Li, C. P.,Qi, X. B.,&Zhang, G. P..(2019).Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens.JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY,35(6),1137-1146.
MLA	Wan, H. Y.,et al."Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens".JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY 35.6(2019):1137-1146.