中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 力学研究所
  3. 中国科学院力学研究所
  4. 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

文献类型：期刊论文

作者Huang QH(黄庆辉)3,4; Hu, Dianyin2; Wang, Rongqiao2; Sergeichev, Ivan1; Sun JY(孙经雨)4; Qian GA(钱桂安)4
刊名FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES
出版日期2025-02-02
页码19
关键词ensemble learning fatigue life interpolation multiple crack initiation short crack growth rate
ISSN号8756-758X
DOI10.1111/ffe.14573
通讯作者Qian, Guian(qianguian@imech.ac.cn)
英文摘要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.
分类号二类
WOS关键词METALLIC MATERIALS ; LIFE PREDICTION ; PROPAGATION ; CLOSURE
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]
WOS研究方向Engineering ; Materials Science
语种英语
WOS记录号WOS:001410730100001
资助机构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
其他责任者Qian, Guian
源URL[http://dspace.imech.ac.cn/handle/311007/98291]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Skolkovo Inst Sci & Technol, Ctr Mat Technol, Moscow, Russia
2.Beihang Univ, Res Inst Aeroengine, Beijing, Peoples R China;
3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing, Peoples R China;
4.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing, Peoples R China;
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.
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.

入库方式： OAI收割

来源：力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。