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:

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: 12

Showing 12 citing articles:

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: 19

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: 7

Fatigue life prediction of cold expansion hole using physics-enhanced data-driven method
Jianxing Mao, Zhi-Fan Xian, Xin Wang, et al.
International Journal of Fatigue (2024) Vol. 190, pp. 108634-108634
Closed Access | Times Cited: 5

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

Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy
Haijie Wang, Bo Li, Liming Lei, et al.
Virtual and Physical Prototyping (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 4

Creep‐Fatigue Life Prediction of 316H Stainless Steel through Physics‐Informed Data‐Driven Models
Lianyong Xu, Huiqiang Jia, Lei Zhao, et al.
Advanced Engineering Materials (2025)
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

Fatigue life prediction of rough Hastelloy X specimens fabricated using Laser Powder Bed Fusion
Ritam Pal, Brandon Kemerling, Daniel Ryan, et al.
Additive manufacturing (2024), pp. 104450-104450
Closed Access | Times Cited: 1

Promoting Synergies to Improve Manufacturing Efficiency in Industrial Material Processing: A Systematic Review of Industry 4.0 and AI
Md. Sazol Ahmmed, Sriram Praneeth Isanaka, Frank Liou
Machines (2024) Vol. 12, Iss. 10, pp. 681-681
Open Access | Times Cited: 1

Meshing theory of point-contact conical-envelope cylindrical worm-face worm gear drive
Shibo Mu, Xingwei Sun, Zhixu Dong, et al.
Mechanism and Machine Theory (2024) Vol. 205, pp. 105870-105870
Closed Access | Times Cited: 1

A review on physics-informed machine learning for process-structure-property modeling in additive manufacturing
Meysam Faegh, Suyog Ghungrad, João Pedro Oliveira, et al.
Journal of Manufacturing Processes (2024) Vol. 133, pp. 524-555
Open Access

Fatigue Life Prediction and Reliability Assessment of CFRP Adhesively Bonded Joints in Offshore Wind Turbine Blade Applications: A Physics‐Informed Data‐Driven Approach
Zhenjiang Shao, Zheng Liu, Yuhao Zhang, et al.
Quality and Reliability Engineering International (2024)
Closed Access

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