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Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors

文献类型：期刊论文

作者Guo, Yiyun2,3; Rui, Shao-Shi3; Xu, Wei1; Sun, Chengqi2,3; Sun CQ(孙成奇)
刊名MATERIALS
出版日期2023
卷号16期号:1页码:13
关键词machine learning nickel-based superalloy fatigue strength prediction temperature stress ratio
DOI10.3390/ma16010046
通讯作者Sun, Chengqi(scq@lnm.imech.ac.cn)
英文摘要The accurate prediction of fatigue performance is of great engineering significance for the safe and reliable service of components. However, due to the complexity of influencing factors on fatigue behavior and the incomplete understanding of the fatigue failure mechanism, it is difficult to correlate well the influence of various factors on fatigue performance. Machine learning could be used to deal with the association or influence of complex factors due to its good nonlinear approximation and multi-variable learning ability. In this paper, the gradient boosting regression tree model, the long short-term memory model and the polynomial regression model with ridge regularization in machine learning are used to predict the fatigue strength of a nickel-based superalloy GH4169 under different temperatures, stress ratios and fatigue life in the literature. By dividing different training and testing sets, the influence of the composition of data in the training set on the predictive ability of the machine learning method is investigated. The results indicate that the machine learning method shows great potential in the fatigue strength prediction through learning and training limited data, which could provide a new means for the prediction of fatigue performance incorporating complex influencing factors. However, the predicted results are closely related to the data in the training set. More abundant data in the training set is necessary to achieve a better predictive capability of the machine learning model. For example, it is hard to give good predictions for the anomalous data if the anomalous data are absent in the training set.
WOS关键词HIGH-CYCLE FATIGUE ; ARTIFICIAL NEURAL-NETWORK ; STRESS RATIO ; TITANIUM-ALLOY ; BEHAVIOR ; PROPAGATION ; GROWTH ; STEEL ; LIFE ; GAME
资助项目National Natural Science Foundation of the China Basic Science Center for Multiscale Problems in Nonlinear Mechanics[11988102] ; Youth Fund of National Natural Science Foundation of China[12202446] ; Opening Fund of the Key Laboratory of Aero-engine Thermal Environment and Structure, Ministry of Industry and Information Technology[CEPE2022004]
WOS研究方向Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics
语种英语
WOS记录号WOS:000909946300001
资助机构National Natural Science Foundation of the China Basic Science Center for Multiscale Problems in Nonlinear Mechanics ; Youth Fund of National Natural Science Foundation of China ; Opening Fund of the Key Laboratory of Aero-engine Thermal Environment and Structure, Ministry of Industry and Information Technology
源URL[http://dspace.imech.ac.cn/handle/311007/91433]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Sun, Chengqi
作者单位1.Beijing Inst Aeronaut Mat, Beijing Key Lab Aeronaut Mat Testing & Evaluat, Beijing 100095, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Yiyun,Rui, Shao-Shi,Xu, Wei,et al. Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors[J]. MATERIALS,2023,16(1):13.
APA Guo, Yiyun,Rui, Shao-Shi,Xu, Wei,Sun, Chengqi,&孙成奇.(2023).Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors.MATERIALS,16(1),13.
MLA Guo, Yiyun,et al."Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors".MATERIALS 16.1(2023):13.

入库方式： OAI收割

来源：力学研究所

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