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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:

Physics-guided machine learning frameworks for fatigue life prediction of AM materials
Lanyi Wang, Shun‐Peng Zhu, Changqi Luo, et al.
International Journal of Fatigue (2023) Vol. 172, pp. 107658-107658
Closed Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Defect sensitivity and fatigue design: Deterministic and probabilistic aspects in additively manufactured metallic materials
Xiaopeng Niu, Chao He, Shun‐Peng Zhu, et al.
Progress in Materials Science (2024) Vol. 144, pp. 101290-101290
Closed Access | Times Cited: 38
Fatigue performance of metal additive manufacturing: a comprehensive overview
Hamidreza Javidrad, Bahattin Koç, Hakan Bayraktar, et al.
Virtual and Physical Prototyping (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 21
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: 17
Predicting fatigue slip and fatigue life of FRP rebar-concrete bonds using tree-based and theory-informed learning models
Yiliyaer Tuerxunmaimaiti, Xiao-Ling Zhao, Daxu Zhang, et al.
International Journal of Fatigue (2025) Vol. 193, pp. 108816-108816
Closed Access | Times Cited: 2
Multi-fidelity physics-informed machine learning framework for fatigue life prediction of additive manufactured materials
Lanyi Wang, Shun‐Peng Zhu, Borui Wu, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 439, pp. 117924-117924
Closed Access | Times Cited: 2
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
A tensile properties-related fatigue strength predicted machine learning framework for alloys used in aerospace
Jiangbo Fan, Zhangwei Wang, Changqi Liu, et al.
Engineering Fracture Mechanics (2024) Vol. 301, pp. 110057-110057
Closed Access | Times Cited: 11
Critical damage events of 3D printed AlSi10Mg alloy via in situ synchrotron X-ray tomography
Zhengkai Wu, Shengchuan Wu, Jamie J. Kruzic, et al.
Acta Materialia (2024) Vol. 282, pp. 120464-120464
Closed Access | Times Cited: 11
A physics-informed neural network approach for predicting fatigue life of SLM 316L stainless steel based on defect features
Feng Feng, Tao Zhu, Bing Yang, et al.
International Journal of Fatigue (2024) Vol. 188, pp. 108486-108486
Closed Access | Times Cited: 10
Critical physics informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects
Yanan Hu, Yufeng She, Shengchuan Wu, et al.
International Journal of Mechanical Sciences (2024), pp. 109730-109730
Closed Access | Times Cited: 9
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
Multiaxial damage parameter evaluation by neural network-based symbolic regression
Weiwen Cao, Xingyue Sun, Yajing Li, et al.
Engineering Fracture Mechanics (2025) Vol. 315, pp. 110809-110809
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
Probabilistic fatigue life prediction in additive manufacturing materials with a physics-informed neural network framework
Feng Feng, Tao Zhu, Bing Yang, et al.
Expert Systems with Applications (2025), pp. 127098-127098
Closed Access | Times Cited: 1
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
Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materials
Lanyi Wang, Shun‐Peng Zhu, 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: 23
Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling
Zhixin Zhan, Xiaofan He, Dingcheng Tang, et al.
Fatigue & Fracture of Engineering Materials & Structures (2023) Vol. 46, Iss. 12, pp. 4425-4464
Closed Access | Times Cited: 22
A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation
Alessandro Tognan, Andrea Patané, Luca Laurenti, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 418, pp. 116521-116521
Open Access | Times Cited: 20
Stored energy density solution for TSV-Cu structure deformation under thermal cyclic loading based on PINN
Hongjiang Qian, Jiebin Shen, Zhiyong Huang, et al.
International Journal of Plasticity (2024) Vol. 179, pp. 104046-104046
Closed Access | Times Cited: 8
Fatigue life prediction driven by mesoscopic defect data
Chao Wang, Yali Yang, Hao Chen, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107773-107773
Closed Access | Times Cited: 7
Pore-induced fatigue failure: A prior progressive fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on machine learning
Linwei Dang, Xiaofan He, Dingcheng Tang, et al.
Theoretical and Applied Fracture Mechanics (2024) Vol. 130, pp. 104276-104276
Closed Access | Times Cited: 7
Residual stress computation in direct metal deposition using integrated artificial neural networks and finite element analysis
Farshid Hajializadeh, Ayhan Ince
Materials Today Communications (2024) Vol. 38, pp. 108471-108471
Open Access | Times Cited: 6
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
Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates
Liaojun Yao, Jiexiong Wang, Mingyue Chuai, et al.
Composites Part A Applied Science and Manufacturing (2024), pp. 108474-108474
Closed Access | Times Cited: 6
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