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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:

Low-cycle fatigue life prediction of austenitic stainless steel alloys: A data-driven approach with identification of key features
Harsh Kumar Bhardwaj, Mukul Shukla
International Journal of Fatigue (2024) Vol. 187, pp. 108454-108454
Open Access | Times Cited: 9

Showing 9 citing articles:

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
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
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
Low Cycle Fatigue Response and Cyclic Life Prediction Model of Ultra‐Pure 26Cr2Ni4MoV Steel Under Strain‐ and Stress‐Controlled Loading
Bin Li, Xiaodi Wang, Hongfei Yu, et al.
Fatigue & Fracture of Engineering Materials & Structures (2025)
Closed Access
Microstructural feature-based physics-informed neural network for creep residual life prediction of P91 steel
Zhi Liu, Zhou Zheng, Peng Zhao, et al.
Engineering Fracture Mechanics (2025), pp. 110989-110989
Closed Access
A cGAN-based fatigue life prediction of 316 austenitic stainless steel in high-temperature and high-pressure water environments
Lvfeng Jiang, Yanan Hu, Hui Li, et al.
International Journal of Fatigue (2024), pp. 108633-108633
Closed Access | Times Cited: 2
A Hybrid Framework for Characterizing and Benchmarking Fatigue S‐N Curves in Aluminum Alloys by Integrating Empirical and Data‐Driven Approaches
Hamed Esmaeili, Maryam Avateffazeli, M. Haghshenas, et al.
Fatigue & Fracture of Engineering Materials & Structures (2024)
Open Access
A microstructure sensitive machine learning-based approach for predicting fatigue life of additively manufactured parts
Prateek Kishore, Aratrick Mondal, Aayush Trivedi, et al.
International Journal of Fatigue (2024), pp. 108724-108724
Closed Access
Enhanced fatigue crack growth rate prediction in alloy steels using particle swarm optimized neural network
Harsh Kumar Bhardwaj, Mukul Shukla
Theoretical and Applied Fracture Mechanics (2024), pp. 104826-104826
Closed Access
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