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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:

Prediction of 316 stainless steel low-cycle fatigue life based on machine learning
Hongyan Duan, Mengjie Cao, Lin Liu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 12

Showing 12 citing articles:

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
A data-driven low-cycle fatigue life prediction model for nickel-based superalloys
Luopeng Xu, Rulun Zhang, Mengquan Hao, et al.
Computational Materials Science (2023) Vol. 229, pp. 112434-112434
Closed Access | Times Cited: 14
A deep learning approach for low-cycle fatigue life prediction under thermal–mechanical loading based on a novel neural network model
Yang Yang, Bo Zhang, Hao Wu, et al.
Engineering Fracture Mechanics (2024) Vol. 306, pp. 110239-110239
Closed Access | Times Cited: 4
A novel neural network model considering cyclic loading condition for low-cycle fatigue life prediction
Hongguang Zhou, Ziming Wang, Yunpeng Zhao, et al.
International Journal of Fatigue (2025), pp. 108943-108943
Closed Access
Orientation effects on the fracture behaviour of additively manufactured stainless steel 316L subjected to high cyclic fatigue
Mateusz Kopeć, Urvashi Gunputh, Wojciech Macek, et al.
Theoretical and Applied Fracture Mechanics (2024) Vol. 130, pp. 104287-104287
Closed Access | Times Cited: 1
Material informatics and impact of multicollinearity on regression model for fatigue strength of steel
Mrinal Kumar Adhikary, Archana Bora
International Journal of Fracture (2024) Vol. 246, Iss. 1, pp. 37-46
Closed Access | Times Cited: 1
A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning
Harsh Kumar Bhardwaj, Mukul Shukla
Fatigue & Fracture of Engineering Materials & Structures (2024)
Open Access | Times Cited: 1
Modeling of LCF Behaviour on AISI316L Steel Applying the Armstrong–Frederick Kinematic Hardening Model
Sushant Bhalchandra Pate, Gintautas Dundulis, Paulius Griškevičius
Materials (2024) Vol. 17, Iss. 14, pp. 3395-3395
Open Access | Times Cited: 1
Application and Feasibility Analysis of Knowledge-Based Machine Learning in Predicting Fatigue Performance of Stainless Steel
Jia Wang, DongKui Fan, C.S. Cai
(2024)
Closed Access
Fatigue Damage Evolution in SS316L Produced by Powder Bed Fusion in Different Orientations with Reused Powder Feedstock
Mateusz Kopeć, Urvashi Gunputh, Graham R. Williams, et al.
Experimental Mechanics (2024)
Open Access
Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel
Jia Wang, Desheng Fan, Chaocan Cai
Case Studies in Construction Materials (2024), pp. e04090-e04090
Open Access
A Deep Learning Approach for Low-Cycle Fatigue Life Prediction Under Thermal-Mechanical Loading Based on a Novel Neural Network Model
Yang Yang, Bo Zhang, Hao Wu, et al.
(2023)
Closed Access
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