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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:

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
Fasikaw Kibrete, Tomasz Trzepieciński, Hailu Shimels Gebremedhen, et al.
Journal of Composites Science (2023) Vol. 7, Iss. 9, pp. 364-364
Open Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Mohamed Abdellatief, Leong Sing Wong, Norashidah Md Din, et al.
Materials Today Communications (2024) Vol. 40, pp. 110022-110022
Closed Access | Times Cited: 28
Applications of artificial intelligence/machine learning to high-performance composites
Yifeng Wang, Wang Kan, Chuck Zhang
Composites Part B Engineering (2024) Vol. 285, pp. 111740-111740
Closed Access | Times Cited: 22
Transformers in Material Science: Roles, Challenges, and Future Scope
Nitin Rane
SSRN Electronic Journal (2023)
Closed Access | Times Cited: 27
Machine learning approach to evaluating impact behavior in fabric-laminated composite materials
Shivashankar Hiremath, Yu Zhang, Lu Zhang, et al.
Results in Engineering (2024) Vol. 23, pp. 102576-102576
Open Access | Times Cited: 12
A comprehensive review on fillers and mechanical properties of 3D printed polymer composites
Nishtha Arora, Sachin Dua, Vivek Kumar Singh, et al.
Materials Today Communications (2024) Vol. 40, pp. 109617-109617
Closed Access | Times Cited: 11
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen, Khemraj Shukla, Saaketh Desai, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 10
Identification of Lighting Strike Damage and Prediction of Residual Strength of Carbon Fiber-Reinforced Polymer Laminates Using a Machine Learning Approach
Ruochen Dong, Yin Fan, J. J. Bian, et al.
Polymers (2025) Vol. 17, Iss. 2, pp. 180-180
Open Access | Times Cited: 1
Classification of a nanocomposite using a combination between Recurrent Neural Network based on Transformer and Bayesian Network for testing the conductivity property
Wejden Gazehi, Rania Loukil, Mongi Besbes
Expert Systems with Applications (2025) Vol. 270, pp. 126518-126518
Closed Access | Times Cited: 1
Experimental and computational approaches to optimizing the development of NFs reinforced polymer composite: A review of optimization strategies
Olajesu Favor Olanrewaju, Justus Uchenna Anaele, Sodiq Abiodun Kareem
Sustainable materials and technologies (2025), pp. e01259-e01259
Closed Access | Times Cited: 1
Assessing the significance of the particle size of Ganga sand Sone sand and bentonite mixtures for hydraulic containment liners integrated with machine learning-based UCS predictions
Rajiv Kumar, Divesh Ranjan Kumar, Sunita Kumari, et al.
Construction and Building Materials (2025) Vol. 465, pp. 140236-140236
Closed Access | Times Cited: 1
Machine Learning Predictions for the Comparative Mechanical Analysis of Composite Laminates with Various Fibers
Baha Eddine Ben Brayek, Sirine Sayed, Viorel Mînzu, et al.
Processes (2025) Vol. 13, Iss. 3, pp. 602-602
Open Access | Times Cited: 1
Towards data-efficient mechanical design of bicontinuous composites using generative AI
Milad Masrouri, Zhao Qin
Theoretical and Applied Mechanics Letters (2024) Vol. 14, Iss. 1, pp. 100492-100492
Open Access | Times Cited: 8
A Review of Machine Learning for Progressive Damage Modelling of Fiber-Reinforced Composites
Jimbay Loh, Kirk Ming Yeoh, Karthikayen Raju, et al.
Applied Composite Materials (2024)
Closed Access | Times Cited: 7
Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites
Kimberley Rooney, Yu Dong, A.K. Basak, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 10, pp. 416-416
Open Access | Times Cited: 7
ADVANCED ENSEMBLE MACHINE LEARNING PREDICTION TO ENHANCE THE ACCURACY OF ABRASIVE WATERJET MACHINING FOR BIOCOMPOSITES
Gopi Periyappillai, S. Sathiyamurthy, S. Saravanakumar
Materials Chemistry and Physics (2024), pp. 130175-130175
Closed Access | Times Cited: 7
Explainable artificial intelligence framework for FRP composites design
Mostafa Yossef, Mohamed Noureldin, Aghyad Alqabbany
Composite Structures (2024) Vol. 341, pp. 118190-118190
Open Access | Times Cited: 6
Self-consistency Reinforced minimal Gated Recurrent Unit for surrogate modeling of history-dependent non-linear problems: Application to history-dependent homogenized response of heterogeneous materials
Ling Wu, Ludovic Noels
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 424, pp. 116881-116881
Closed Access | Times Cited: 5
Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites
Jayanta Bhusan Deb, Jihua Gou, Haonan Song, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 3, pp. 96-96
Open Access | Times Cited: 5
Exploring the Future of Polyhydroxyalkanoate Composites with Organic Fillers: A Review of Challenges and Opportunities
Abhishek Thakur, Marta Musioł, Khadar Duale, et al.
Polymers (2024) Vol. 16, Iss. 13, pp. 1768-1768
Open Access | Times Cited: 5
A Review of AI for optimization of 3D Printing of Sustainable Polymers and Composites
Malik Hassan, Manjusri Misra, Graham W. Taylor, et al.
Composites Part C Open Access (2024), pp. 100513-100513
Open Access | Times Cited: 5
Deep learning identifies transversely isotropic material properties using kinematics fields
Nikzad Motamedi, Hazem Wannous, Vincent Magnier
International Journal of Mechanical Sciences (2024) Vol. 283, pp. 109672-109672
Closed Access | Times Cited: 4
Experimental Study on Mechanical Performance of Single-Side Bonded Carbon Fibre-Reinforced Plywood for Wood-Based Structures
Krzysztof Szwajka, Joanna Zielińska-Szwajka, Tomasz Trzepieciński, et al.
Materials (2025) Vol. 18, Iss. 1, pp. 207-207
Open Access
Advancements of machine learning techniques in fiber-filled polymer composites: a review
R. Alagulakshmi, R. Ramalakshmi, V. Arumugaprabu, et al.
Polymer Bulletin (2025)
Closed Access
Ensemble machine learning for predicting and enhancing tribological performance of Al5083 alloy with HEA reinforcement
S. Kumaravel, P.M. Suresh
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2025)
Closed Access
Development of machine learning models for material classification and prediction of mechanical properties of FDM 3D printing outputs
Suhyun Kim, Ji-Hye Park, Ji Young Park, et al.
Journal of Mechanical Science and Technology (2025)
Closed Access
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