Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning

doi:10.1111/ffe.14573

IMECH-IR > 非线性力学国家重点实验室

	Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning
	Huang QH(黄庆辉)1,2 ; Hu, Dianyin 3; Wang, Rongqiao 3; Sergeichev, Ivan 4; Sun JY(孙经雨)1; Qian GA(钱桂安)1
通讯作者	Qian, Guian([email protected])
发表期刊	FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES
	2025-02-02
页码	19
ISSN	8756-758X
摘要	In situ fatigue crack propagation experiment was conducted on laser cladding with coaxial powder feeding (LCPF) K477 under various stress ratios and temperatures. Multiple crack initiation sites were observed by using in situ scanning electron microscopy (SEM). The fatigue short crack growth rate was measured, and the impacts of temperature and stress ratio on this growth rate were analyzed. Based on these experiments, the experimental data were expanded, and three ensemble learning algorithms, that is, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were employed to establish a fatigue short crack growth rate model controlled by multiple parameters. It is indicated that the RF model performs the best, achieving a coefficient of determination (R2) of up to 0.88. The fatigue life predicted by the machine learning (ML) method agrees well with the experimental one.
关键词	ensemble learning fatigue life interpolation multiple crack initiation short crack growth rate
DOI	10.1111/ffe.14573
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:001410730100001
关键词[WOS]	METALLIC MATERIALS ; LIFE PREDICTION ; PROPAGATION ; CLOSURE
WOS研究方向	Engineering ; Materials Science
WOS类目	Engineering, Mechanical ; Materials Science, Multidisciplinary
资助项目	National Natural Science Foundation of China ; International Partnership Program for Grand Challenges of Chinese Academy of Sciences[025GJHZ2023092GC] ; Science Center for Gas Turbine Project[P2022-B-III-008-001] ; National Science and Technology Major Project[J2019-IV-0009-0077] ; National Science and Technology Major Project[Y2022-IV-0002-0019] ; [12072345] ; [11932020]
项目资助者	National Natural Science Foundation of China ; International Partnership Program for Grand Challenges of Chinese Academy of Sciences ; Science Center for Gas Turbine Project ; National Science and Technology Major Project
论文分区	二类
力学所作者排名	1
RpAuthor	Qian, Guian
引用统计
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/98292
专题	非线性力学国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing, Peoples R China; 3.Beihang Univ, Res Inst Aeroengine, Beijing, Peoples R China; 4.Skolkovo Inst Sci & Technol, Ctr Mat Technol, Moscow, Russia
推荐引用方式 GB/T 7714	Huang QH,Hu, Dianyin,Wang, Rongqiao,et al. Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning[J]. FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES,2025:19.Rp_Au:Qian, Guian
APA	黄庆辉,Hu, Dianyin,Wang, Rongqiao,Sergeichev, Ivan,孙经雨,&钱桂安.(2025).Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning.FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES,19.
MLA	黄庆辉,et al."Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning".FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES (2025):19.