Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review

doi:10.1016/j.jmst.2024.01.086

IMR OpenIR

	Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review
	Wang, H.1; Gao, S. L.1; Wang, B. T.1; Ma, Y. T.1; Guo, Z. J.1; Zhang, K.1; Yang, Y.1; Yue, X. Z.1; Hou, J.1; Huang, H. J.1; Xu, G. P.1; Li, S. J.2; Feng, A. H.3; Teng, C. Y.4; Huang, A. J.5; Zhang, L. -C.6; Chen, D. L.7
通讯作者	Wang, H.(haowang7@usst.edu.cn) ; Huang, A. J.(aijun.huang@monash.edu) ; Zhang, L. -C.(l.zhang@ecu.edu.au) ; Chen, D. L.(dchen@torontomu.ca)
	2024-11-01
发表期刊	JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY
ISSN	1005-0302
卷号	198 页码:111-136
摘要	Additive manufacturing features rapid production of complicated shapes and has been widely employed in biomedical, aeronautical and aerospace applications. However, additive manufactured parts generally exhibit deteriorated fatigue resistance due to the presence of random defects and anisotropy, and the prediction of fatigue properties remains challenging. In this paper, recent advances in fatigue life prediction of additive manufactured metallic alloys via machine learning models are reviewed. Based on artificial neural network, support vector machine, random forest, etc., a number of models on various systems were proposed to reveal the relationships between fatigue life/strength and defect/microstructure/parameters. Despite the success, the predictability of the models is limited by the amount and quality of data. Moreover, the supervision of physical models is pivotal, and machine learning models can be well enhanced with appropriate physical knowledge. Lastly, future challenges and directions for the fatigue property prediction of additive manufactured parts are discussed. (c) 2024 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
关键词	Fatigue Additive manufacturing Metallic alloys Machine learning
资助者	National Natural Science Foundation of China ; National Key Laboratory Foundation of Science and Technology on Materials under Shock and Impact ; Natural Science Foundation of Shenyang ; Opening Project of National Key Laboratory of Shock Wave and Detonation Physics ; Aeronautical Science Foundation of China ; Shanghai Engineering Research Center of High-Performance Medical Device Materials
DOI	10.1016/j.jmst.2024.01.086
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U2241245] ; National Natural Science Foundation of China[91960202] ; National Key Laboratory Foundation of Science and Technology on Materials under Shock and Impact[6142902220301] ; Natural Science Foundation of Shenyang[23-503-6-05] ; Opening Project of National Key Laboratory of Shock Wave and Detonation Physics[2022JCJQLB05702] ; Aeronautical Science Foundation of China[2022Z053092001] ; Shanghai Engineering Research Center of High-Performance Medical Device Materials[20DZ2255500]
WOS研究方向	Materials Science ; Metallurgy & Metallurgical Engineering
WOS类目	Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
WOS记录号	WOS:001237816100001
出版者	JOURNAL MATER SCI TECHNOL
引用统计	被引频次：34[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/321006/186611
专题	中国科学院金属研究所
通讯作者	Wang, H.; Huang, A. J.; Zhang, L. -C.; Chen, D. L.
作者单位	1.Univ Shanghai Sci & Technol, Interdisciplinary Ctr Addit Mfg ICAM, Sch Mat & Chem, Shanghai 200093, Peoples R China 2.Chinese Acad Sci, Shi Changxu Innovat Ctr Adv Mat, Inst Met Res, Shenyang 110016, Peoples R China 3.Tongji Univ, Sch Mat Sci & Engn, Shanghai 201804, Peoples R China 4.AVIC Aeropolytechnol Estab, Beijing 100028, Peoples R China 5.Monash Univ, Dept Mat Sci & Engn, Clayton, Vic 3800, Australia 6.Edith Cowan Univ, Sch Engn, Perth, WA 6027, Australia 7.Toronto Metropolitan Univ, Ryerson Univ, Dept Mech & Ind Engn, Toronto, ON M5B 2K3, Canada
推荐引用方式 GB/T 7714	Wang, H.,Gao, S. L.,Wang, B. T.,et al. Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review[J]. JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY,2024,198:111-136.
APA	Wang, H..,Gao, S. L..,Wang, B. T..,Ma, Y. T..,Guo, Z. J..,...&Chen, D. L..(2024).Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review.JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY,198,111-136.
MLA	Wang, H.,et al."Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review".JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY 198(2024):111-136.