| Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors | |
Guo, Yiyun; Rui SS(芮少石); Xu, Wei ; Sun CQ(孙成奇)
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| Corresponding Author | Sun, Chengqi([email protected]) |
| Source Publication | MATERIALS
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| 2023 | |
| Volume | 16Issue:1Pages:13 |
| Abstract | 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. |
| Keyword | machine learning nickel-based superalloy fatigue strength prediction temperature stress ratio |
| DOI | 10.3390/ma16010046 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000909946300001 |
| WOS Keyword | HIGH-CYCLE FATIGUE ; ARTIFICIAL NEURAL-NETWORK ; STRESS RATIO ; TITANIUM-ALLOY ; BEHAVIOR ; PROPAGATION ; GROWTH ; STEEL ; LIFE ; GAME |
| WOS Research Area | Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics |
| WOS Subject | Chemistry, Physical ; Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering ; Physics, Applied ; Physics, Condensed Matter |
| Funding Project | 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] |
| Funding Organization | 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 |
| Classification | 二类/Q1 |
| Ranking | 1 |
| Contributor | Sun, Chengqi |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/91434 |
| Collection | 非线性力学国家重点实验室 |
| Recommended Citation GB/T 7714 | Guo, Yiyun,Rui SS,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.Rp_Au:Sun, Chengqi |
| APA | Guo, Yiyun,芮少石,Xu, Wei,&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. |
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| Jp2023A025.pdf（9664KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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