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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:

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings
Erfan Maleki, Okan Ünal, Seyed Mahmoud Seyedi Sahebari, et al.
Journal of Marine Science and Engineering (2022) Vol. 10, Iss. 2, pp. 128-128
Open Access | Times Cited: 25

Showing 25 citing articles:

Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
Haijie Wang, Bo Li, Jian‐Guo Gong, et al.
Engineering Fracture Mechanics (2023) Vol. 284, pp. 109242-109242
Closed Access | Times Cited: 109
Machine learning based very-high-cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting
Tao Shi, Jingyu Sun, Jianghua Li, et al.
International Journal of Fatigue (2023) Vol. 171, pp. 107585-107585
Closed Access | Times Cited: 39
Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime
Yinfeng Jia, Rui Fu, Chao Ling, et al.
International Journal of Fatigue (2023) Vol. 172, pp. 107645-107645
Closed Access | Times Cited: 29
Novel GA-Based DNN Architecture for Identifying the Failure Mode with High Accuracy and Analyzing Its Effects on the System
Naeim Rezaeian, Regina Gurina, О. А. Салтыкова, et al.
Applied Sciences (2024) Vol. 14, Iss. 8, pp. 3354-3354
Open Access | Times Cited: 12
Prediction of Concrete Compressive Strength Using a Back-Propagation Neural Network Optimized by a Genetic Algorithm and Response Surface Analysis Considering the Appearance of Aggregates and Curing Conditions
Kazem Reza Kashyzadeh, Nima Amiri, Siamak Ghorbani, et al.
Buildings (2022) Vol. 12, Iss. 4, pp. 438-438
Open Access | Times Cited: 31
Neural network based fatigue lifetime prediction of metals subjected to block loading
Jelle Plets, Quinten Bouckaert, Bilal Ahmed, et al.
International Journal of Fatigue (2024) Vol. 183, pp. 108283-108283
Open Access | Times Cited: 5
Application of artificial neural networks throughout the entire life cycle of coatings: A comprehensive review
Zenglei Ning, Xia Zhao, Fan Liang, et al.
Progress in Organic Coatings (2024) Vol. 189, pp. 108279-108279
Closed Access | Times Cited: 4
A data-informed knowledge discovery framework to predict fatigue properties of additively manufactured Ti–6Al–4V, IN718 and AlSi10Mg alloys using fatigue databases
Samuel Onimpa Alfred, Mehdi Amiri
Progress in Additive Manufacturing (2025)
Open Access
Investigation of wear performance of R260 and R350HT rails after vibratory peening and ultrasonic nanocrystal surface modification
İbrahim Karademir, Vahap Neccaroglu, İsmail Aykut Karamanlı, et al.
Materials Science and Technology (2025)
Closed Access
Neural network approaches for real-time fatigue life estimation by Surrogating the rainflow counting method
Yu-cheng Guo, Liangxing Li, Jiabin Gui, et al.
International Journal of Fatigue (2025), pp. 108941-108941
Closed Access
An investigation of artificial neural network structure and its effects on the estimation of the low‐cycle fatigue parameters of various steels
Mehmet Alperen Soyer, Nail Tüzün, Özler Karakaş, et al.
Fatigue & Fracture of Engineering Materials & Structures (2023) Vol. 46, Iss. 8, pp. 2929-2948
Open Access | Times Cited: 12
A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
Azamatjon Kakhramon ugli Malikov, Younho Cho, Young H. Kim, et al.
Science Progress (2023) Vol. 106, Iss. 1
Open Access | Times Cited: 10
A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method
Chao Feng, Lianyong Xu, Lei Zhao, et al.
Advanced Engineering Materials (2023) Vol. 25, Iss. 9
Closed Access | Times Cited: 7
Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding
Meng Shi, Chang‐Ping Feng, Youlei Tu, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 18, pp. 22602-22615
Closed Access | Times Cited: 6
Failure analysis of loom crankshafts in textile industry by fretting-fatigue
José Wilmar Calderón-Hernández, Mauricio Pérez-Giraldo, Robison Buitrago‐Sierra, et al.
Engineering Failure Analysis (2023) Vol. 151, pp. 107414-107414
Closed Access | Times Cited: 6
Predictive modelling for coal abrasive index: Unveiling influential factors through Shallow and Deep Neural Networks
Moshood Onifade, Abiodun Ismail Lawal, Samson Bada, et al.
Fuel (2024) Vol. 374, pp. 132319-132319
Open Access | Times Cited: 1
High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network
Xiaolu Wei, Chenchong Wang, Zixi Jia, et al.
Journal of Materials Informatics (2022) Vol. 2, Iss. 3, pp. 9-9
Open Access | Times Cited: 7
Application of Probabilistic Approach to Investigate Influence of Details in Time History of Temperature Changes on the HCF Life of Integrated Bridge Steel Piles Installed on Water
Hamid Abdollahnia, Mohammad Hadi Alizadeh Elizei, Kazem Reza Kashyzadeh
Journal of Marine Science and Engineering (2022) Vol. 10, Iss. 12, pp. 1802-1802
Open Access | Times Cited: 3
Developing Artificial Neural Networks to Estimate the Fatigue Strength of Structural Steel Details Using the New European Database
Helen Bartsch, Justus Voelkel, Markus Feldmann
(2024)
Closed Access
Bibliography

Elsevier eBooks (2024), pp. 687-727
Closed Access
Predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy using backpropagation neural network optimized by the improved dung beetle algorithm
Zihao Gao, Changsheng Zhu, Yafeng Shu, et al.
Fatigue & Fracture of Engineering Materials & Structures (2024) Vol. 47, Iss. 11, pp. 3983-3999
Open Access
Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography
Sruthi Krishna, Krishnan Balasubramaniam
Journal of Physics D Applied Physics (2024) Vol. 58, Iss. 4, pp. 045306-045306
Closed Access
Developing artificial neural networks to estimate the fatigue strength of structural steel details using the new European database
Helen Bartsch, Justus Voelkel, Markus Feldmann
Steel Construction (2024)
Closed Access
Enhancing Friction Stir Welding in Fishing Boat Construction through Deep Learning-Based Optimization
Erfan Maleki, Okan Ünal, Seyed Mahmoud Seyedi Sahebari, et al.
Sustainable Marine Structures (2023) Vol. 5, Iss. 2, pp. 1-14
Open Access | Times Cited: 1
Failure Analysis of Marine Structure
Kazem Reza Kashyzadeh, Mahmoud Chizari
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 8, pp. 1526-1526
Open Access
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