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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:

Fatigue life prediction of aluminum alloy via knowledge-based machine learning
Zhengheng Lian, Minjie Li, Wencong Lu
International Journal of Fatigue (2022) Vol. 157, pp. 106716-106716
Closed Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

Small data machine learning in materials science
Pengcheng Xu, Xiaobo Ji, Minjie Li, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 288
Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
Haijie Wang, Bo Li, Jian‐Guo Gong, et al.
Engineering Fracture Mechanics (2023) Vol. 284, pp. 109242-109242
Closed Access | Times Cited: 115
Material machine learning for alloys: Applications, challenges and perspectives
Xiujuan Liu, Pengcheng Xu, Juanjuan Zhao, et al.
Journal of Alloys and Compounds (2022) Vol. 921, pp. 165984-165984
Closed Access | Times Cited: 90
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 fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on physics-informed neural network
Linwei Dang, Xiaofan He, Dingcheng Tang, et al.
International Journal of Structural Integrity (2025)
Closed Access | Times Cited: 2
Fatigue life analysis of high-strength bolts based on machine learning method and SHapley Additive exPlanations (SHAP) approach
Shujia Zhang, Honggang Lei, Zichun Zhou, et al.
Structures (2023) Vol. 51, pp. 275-287
Closed Access | Times Cited: 34
Physics-informed machine learning and its structural integrity applications: state of the art
Shun‐Peng Zhu, Lanyi Wang, Changqi Luo, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 381, Iss. 2260
Closed Access | Times Cited: 31
High cycle fatigue life prediction of titanium alloys based on a novel deep learning approach
Siyao Zhu, Yue Zhang, Beichen Zhu, et al.
International Journal of Fatigue (2024) Vol. 182, pp. 108206-108206
Closed Access | Times Cited: 16
Fatigue life prediction of the FCC-based multi-principal element alloys via domain knowledge-based machine learning
Xiao Lu, Gang Wang, Weimin Long, et al.
Engineering Fracture Mechanics (2024) Vol. 296, pp. 109860-109860
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
High-cycle and very-high-cycle fatigue life prediction in additive manufacturing using hybrid physics-informed neural networks
Isaac Abiria, Chan Wang, Qicheng Zhang, et al.
Engineering Fracture Mechanics (2025), pp. 111026-111026
Closed Access | Times Cited: 1
Machine learning-based prediction of hydrogen-assisted fatigue crack growth rate in Cr–Mo steel
Jiangchuan Hu, Kai Ma, Zhenquan Zhang, et al.
International Journal of Hydrogen Energy (2025) Vol. 122, pp. 1-11
Closed Access | Times Cited: 1
High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network
Xiaolu Wei, Chi Zhang, Siyu Han, et al.
International Journal of Fatigue (2022) Vol. 163, pp. 107050-107050
Closed Access | Times Cited: 36
A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys
Junyu Guo, Xueping Zan, Lin Wang, et al.
Engineering Fracture Mechanics (2023) Vol. 293, pp. 109714-109714
Closed Access | Times Cited: 21
Machine learning-assisted discovery of Cr, Al-containing high-entropy alloys for high oxidation resistance
Ziqiang Dong, Ankang Sun, Yang Shuang, et al.
Corrosion Science (2023) Vol. 220, pp. 111222-111222
Closed Access | Times Cited: 20
A multiaxial low-cycle fatigue prediction method under irregular loading by ANN model with knowledge-based features
Tianguo Zhou, Xingyue Sun, Xu Chen
International Journal of Fatigue (2023) Vol. 176, pp. 107868-107868
Closed Access | Times Cited: 20
Defect-related strain-controlled high-temperature fatigue behavior in additive manufacturing Hastelloy X assisted with ultrasonic micro-forging treatment
Xun Wang, Lianyong Xu, Lei Zhao, et al.
International Journal of Fatigue (2023) Vol. 172, pp. 107607-107607
Closed Access | Times Cited: 18
Phase-field simulation and machine learning of low-field magneto-elastocaloric effect in a multiferroic composite
Wei Tang, Shizheng Wen, Huilong Hou, et al.
International Journal of Mechanical Sciences (2024) Vol. 275, pp. 109316-109316
Closed Access | Times Cited: 8
Prediction of low-cycle fatigue properties of additive manufactured IN718 by crystal plasticity modelling incorporating effects from crystallographic orientations and defects
Naiyuan Xi, Zhiyang Ni, Pengfei Liu, et al.
Virtual and Physical Prototyping (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 7
Interpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behaviour
Shuwei Zhou, Bing Yang, Shoune Xiao, et al.
Metals and Materials International (2024) Vol. 30, Iss. 7, pp. 1944-1964
Closed Access | Times Cited: 6
A novel generalization ability-enhanced approach for corrosion fatigue life prediction of marine welded structures
Chao Feng, Molin Su, Lianyong Xu, et al.
International Journal of Fatigue (2022) Vol. 166, pp. 107222-107222
Closed Access | Times Cited: 27
A multi-algorithm integration machine learning approach for high cycle fatigue prediction of a titanium alloy in aero-engine
Siyao Zhu, Yue Zhang, Xin Chen, et al.
Engineering Fracture Mechanics (2023) Vol. 289, pp. 109485-109485
Closed Access | Times Cited: 16
On the integration of domain knowledge and branching neural network for fatigue life prediction with small samples
Lei Gan, Hao Wu, Zheng Zhong
International Journal of Fatigue (2023) Vol. 172, pp. 107648-107648
Closed Access | Times Cited: 15
Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions
Mohammad Azadi, Mahmood Matin
Frattura ed Integrità Strutturale (2024) Vol. 18, Iss. 68, pp. 357-370
Open Access | Times Cited: 6
Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature
Mengwei Wu, Wei Yong, Cunqin Fu, et al.
International Journal of Minerals Metallurgy and Materials (2024) Vol. 31, Iss. 4, pp. 773-785
Closed Access | Times Cited: 5
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