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.
基金
support of National Natural Science Foundation of China(No.U2241245)
support of National Natural Science Foundation of China(No.91960202)
National Key Laboratory Foundation of Science and Technology on Materials under Shock and Impact(No.6142902220301)
Natural Science Foundation of Shenyang(No.23-503-6-05)
support of Opening Project of National Key Laboratory of Shock Wave and Detonation Physics(No.2022JCJQLB05702)
Aeronautical Science Foundation of China(No.2022Z053092001)
support of Shanghai Engineering Research Center of High-Performance Medical Device Materials(No.20DZ2255500).
作者简介
Corresponding authors:H.Wang,E-mail addresses:haowang7@usst.edu.cn;Corresponding authors:A.J.Huang,E-mail addresses:aijun.huang@monash.edu;Corresponding authors:L.-C.Zhang,E-mail addresses:l.zhang@ecu.edu.au;Corresponding authors:D.L.Chen,E-mail addresses:dchen@torontomu.ca。
