Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace
Wenke Hao, Li Tan, Xiaoguang Yang, et al.
International Journal of Fatigue (2023) Vol. 170, pp. 107536-107536
Closed Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation
Zeng Meng, Q. Q. Qian, Mengqiang Xu, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 414, pp. 116172-116172
Closed Access | Times Cited: 127
Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring
Yuandi Wu, Brett Sicard, S. Andrew Gadsden
Expert Systems with Applications (2024) Vol. 255, pp. 124678-124678
Open Access | Times Cited: 29
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
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: 32
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
Machine learning-driven 3D printing: A review
Xijun Zhang, Dianming Chu, Xinyue Zhao, et al.
Applied Materials Today (2024) Vol. 39, pp. 102306-102306
Closed Access | Times Cited: 17
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
Data-driven fatigue life prediction of small-deep holes in a nickel-based superalloy after a cold expansion process
Chao-Zong Tang, Hongwei Li, Kai-Shang Li, et al.
International Journal of Fatigue (2024) Vol. 181, pp. 108159-108159
Closed Access | Times Cited: 13
Neural network integrated with symbolic regression for multiaxial fatigue life prediction
Peng Zhang, Keke Tang, Anbin Wang, et al.
International Journal of Fatigue (2024) Vol. 188, pp. 108535-108535
Closed Access | Times Cited: 11
A physics-informed neural network framework for laminated composite plates under bending
Weixi Wang, Huu‐Tai Thai
Thin-Walled Structures (2025), pp. 113014-113014
Open Access | Times Cited: 1
Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligence
Teng Wang, Yanfeng Li, Taoyong Li, et al.
Journal of Intelligent Manufacturing (2025)
Closed Access | Times Cited: 1
A review on full-, zero-, and partial-knowledge based predictive models for industrial applications
Stefano Zampini, Guido Parodi, Luca Oneto, et al.
Information Fusion (2025), pp. 102996-102996
Open Access | Times Cited: 1
An ensemble deep learning network based on 2D convolutional neural network and 1D LSTM with self-attention for bearing fault diagnosis
Liying Wang, Weiguo Zhao
Applied Soft Computing (2025), pp. 112889-112889
Closed Access | Times Cited: 1
Physics-constrained deep learning approach for solving inverse problems in composite laminated plates
Yang Li, Detao Wan, Zhe Wang, et al.
Composite Structures (2024) Vol. 348, pp. 118514-118514
Closed Access | Times Cited: 7
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
A machine learning based approach with an augmented dataset for fatigue life prediction of additively manufactured Ti-6Al-4V samples
Jan Horňas, Jiří Běhal, Petr Homola, et al.
Engineering Fracture Mechanics (2023) Vol. 293, pp. 109709-109709
Open 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
Manufacturing of ultra-thin large titanium alloy tube using the novel hot gas pressure-bending process
Kunning Fu, Ziwei Zhao, Heli Peng, et al.
Journal of Materials Processing Technology (2024) Vol. 326, pp. 118358-118358
Closed Access | Times Cited: 5
Estimation of mode I quasi-static fracture of notched aluminum–lithium AW2099-T83 alloy using local approaches and machine learning
Muhammed Al Helal, Abullateef Almutairi, Sulaiman Almudayris, et al.
Engineering Failure Analysis (2024) Vol. 163, pp. 108496-108496
Closed Access | Times Cited: 5
Nonlocal multiaxial fatigue model based on artificial neural networks for predicting fretting fatigue life of dovetail joints
Wang Zhao, Sihai Luo, Xiaoqing Liang, et al.
International Journal of Fatigue (2024) Vol. 189, pp. 108546-108546
Closed Access | Times Cited: 5
Recent advances in machine learning for defects detection and prediction in laser cladding process
Xiaochao Ji, R.S. Chen, Chris Xiaoxuan Lu, et al.
Next Materials (2024) Vol. 7, pp. 100404-100404
Closed Access | Times Cited: 5
Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network
Haijie Wang, Bo Li, Liming Lei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109852-109852
Closed Access | Times Cited: 13
Data-Driven, Physics-Based, or Both: Fatigue Prediction of Structural Adhesive Joints by Artificial Intelligence
Pedro Fernandes, Giovanni Corsetti Silva, Diogo B. Pitz, et al.
Applied Mechanics (2023) Vol. 4, Iss. 1, pp. 334-355
Open Access | Times Cited: 12
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
Data-driven machine learning for alloy research: Recent applications and prospects
Xueyun Gao, Haiyan Wang, Huijie Tan, et al.
Materials Today Communications (2023) Vol. 36, pp. 106697-106697
Closed Access | Times Cited: 10
Page 1 - Next Page

Scroll to top