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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Data-driven approach to very high cycle fatigue life prediction
Yu-Ke Liu, Jia-Le Fan, Gang Zhu, et al.
Engineering Fracture Mechanics (2023) Vol. 292, pp. 109630-109630
Closed Access | Times Cited: 25

Showing 25 citing articles:

Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels
Lvfeng Jiang, Yanan Hu, Yuxuan Liu, et al.
International Journal of Fatigue (2024) Vol. 182, pp. 108187-108187
Closed Access | Times Cited: 28
A holistic review on fatigue properties of additively manufactured metals
Min Yi, Wei Tang, Yiqi Zhu, et al.
Journal of Materials Processing Technology (2024) Vol. 329, pp. 118425-118425
Open Access | Times Cited: 20
Advancing Fatigue Life Prediction with Machine Learning: A review
Atef Hamada, Shaimaa Elyamny, Walaa Abd‐Elaziem, et al.
Materials Today Communications (2025), pp. 111525-111525
Closed Access | Times Cited: 2
Machine Learning-Based predictions of crack growth rates in an aeronautical aluminum alloy
Yuval Freed
Theoretical and Applied Fracture Mechanics (2024) Vol. 130, pp. 104278-104278
Closed Access | Times Cited: 13
Crack propagation simulation and overload fatigue life prediction via enhanced physics-informed neural networks
Zhiying Chen, Yanwei Dai, Yinghua Liu
International Journal of Fatigue (2024) Vol. 186, pp. 108382-108382
Closed Access | Times Cited: 11
A novel machine-learning based framework for calibrating micromechanical fracture model of ultra-low cycle fatigue in steel structures
Mingming Yu, Xu Xie, Zhiyuan Fang, et al.
Engineering Fracture Mechanics (2024) Vol. 306, pp. 110200-110200
Closed Access | Times Cited: 8
Study of fatigue crack propagation on modified CT specimens under variable amplitude loadings using machine learning
B. Santos, V. Infante, T. Barros, et al.
International Journal of Fatigue (2024) Vol. 184, pp. 108332-108332
Open Access | Times Cited: 7
Physics-informed neural network for creep-fatigue life prediction of Inconel 617 and interpretation of influencing factors
Shanglin Zhang, Lanyi Wang, Shun‐Peng Zhu, et al.
Materials & Design (2024) Vol. 245, pp. 113267-113267
Open Access | Times Cited: 6
Physics-informed machine learning framework for creep-fatigue life prediction of a Ni-based superalloy using ensemble learning
Xi Deng, Shun‐Peng Zhu, Shanglin Zhang, et al.
Materials Today Communications (2024) Vol. 41, pp. 110260-110260
Closed Access | Times Cited: 6
A generalized machine learning framework to estimate fatigue life across materials with minimal data
Dharun Vadugappatty Srinivasan, Morteza Moradi, Panagiotis Komninos, et al.
Materials & Design (2024), pp. 113355-113355
Open Access | Times Cited: 6
A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy
Hang Li, Guanze Sun, Tian Zhao, et al.
Fatigue & Fracture of Engineering Materials & Structures (2024)
Closed Access | Times Cited: 4
A machine learning-driven prediction of lower-bound buckling design load for cylindrical shells under localized axial compression
Xinyi Lin, Peng Jiao, Huangyang Xu, et al.
Thin-Walled Structures (2025) Vol. 209, pp. 112960-112960
Closed Access
Evaluation of Frequency Effects on Fatigue Life at High Test Frequencies for SAE 1045 Steel Based on Thermography and Electrical Resistance Measurements
Jonas Anton Ziman, Fabian Weber, J.J. Koziol, et al.
Applied Sciences (2025) Vol. 15, Iss. 3, pp. 1022-1022
Open Access
Machine learning model for predicting the influence of crystallographic orientation on thermomechanical fatigue of Ni-base superalloys
Rohan Acharya, Alexander N. Caputo, Richard W. Neu
International Journal of Fatigue (2025), pp. 108832-108832
Closed Access
A Novel Physical Neural Network Based on Transformer Framework for Multiaxial Fatigue Life Prediction
Rui Pan, Jianxiong Gao, Yiping Yuan, et al.
Fatigue & Fracture of Engineering Materials & Structures (2025)
Open Access
Uncertainty-based fatigue life of vehicle subframe via improved bootstrap method under load extrapolation
W.Q. Li, Xintian Liu, Weihao Su
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering (2025)
Closed Access
Thermomechanical Fatigue Behavior and Lifetime Prediction of Nickel-Based Single Crystal Alloys Under Varying Stress Conditions
Yi Tu, Jundong Wang, Zhixun Wen, et al.
Journal of Alloys and Compounds (2025), pp. 180202-180202
Closed Access
A general physics-informed neural network framework for fatigue life prediction of metallic materials
Shuwei Zhou, Manuel Henrich, Zhichao Wei, et al.
Engineering Fracture Mechanics (2025), pp. 111136-111136
Closed Access
What Is Necessary for Digital Transformation of Large Manufacturing Companies? A Necessary Condition Analysis
Ziye Zhang, Meiying Wu, Jiajie Yin
Sustainability (2024) Vol. 16, Iss. 9, pp. 3837-3837
Open Access | Times Cited: 3
Creep–fatigue life prediction of a titanium alloy deep-sea submersible using a continuum damage mechanics-informed BP neural network model
Yuhao Guo, Shichao Wang, Gang Liu
Ocean Engineering (2024) Vol. 311, pp. 118826-118826
Closed Access | Times Cited: 2
Data-driven prediction method for flexural performance of ECC composite sandwich panels
Feng Xiong, Yu Bian, Ye Liu, et al.
Structures (2024) Vol. 70, pp. 107524-107524
Closed Access | Times Cited: 2
Coupling physics in artificial neural network to predict the fatigue behavior of corroded steel wire
Yi Fan, Huan Lei, Qingfang Lv, et al.
International Journal of Fatigue (2024), pp. 108669-108669
Closed Access | Times Cited: 1
Macroscopically modeling fatigue life of additively manufactured metals: Pore-defect informed phase-field model
Wei Tang, Lingfeng Wang, Shen Sun, et al.
Journal of the Mechanics and Physics of Solids (2024), pp. 106008-106008
Closed Access | Times Cited: 1
Crack Propagation Simulation and Overload Fatigue Life Prediction Via Enhanced Physics-Informed Neural Networks
Zhiying Chen, Yanwei Dai, Yinghua Liu
(2024)
Closed Access
Neural Network Based Short Crack Growth Rate Prediction on Ti-6al-4v Titanium Alloy Under High Cycle Fatigue
Zhengyu Shen, K.S. Tan, Guanlin Lv, et al.
(2024)
Closed Access
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