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

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

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Additive manufacturing features rapid production of complicated shapes and has been widely em-ployed 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 de-fect/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 learn-ing models can be well enhanced with appropriate physical knowledge.Lastly,future challenges and di-rections for the fatigue property prediction of additive manufactured parts are discussed.

FatigueAdditive manufacturingMetallic alloysMachine learning

H.Wang、S.L.Gao、B.T.Wang、Y.T.Ma、Z.J.Guo、K.Zhang、Y.Yang、X.Z.Yue、J.Hou、H.J.Huang、G.P.Xu、S.J.Li、A.H.Feng、C.Y.Teng、A.J.Huang、L.-C.Zhang、D.L.Chen

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Interdisciplinary Centre for Additive Manufacturing(ICAM),School of Materials and Chemistry,University of Shanghai for Science and Technology,Shanghai 200093,China

Shi-changxu Innovation Center for Advanced Materials,Institute of Metal Research,Chinese Academy of Sciences,Shenyang 110016,China

School of Materials Science and Engineering,Tongji University,Shanghai 201804,China

AVIC Aero-Polytechnology Establishment,Beijing 100028,China

Department of Material Science and Engineering,Monash University,Clayton,VIC 3800,Australia

School of Engineering,Edith Cowan University,Perth,WA 6027,Australia

Department of Mechanical and Industrial Engineering,Toronto Metropolitan University(formerly Ryerson University),Toronto,Ontario M5B 2K3,Canada

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2024

10.1016/j.jmst.2024.01.086

材料科学技术(英文版)
中国金属学会 中国材料研究学会 中国科学院金属研究所

材料科学技术(英文版)

CSTPCD
影响因子：0.657
ISSN：1005-0302
年,卷(期)：2024.198(31)