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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 compressive mechanical properties of three-dimensional mesoscopic aluminium foam based on deep learning method
Weimin Zhuang, Enming Wang, Hailun Zhang
Mechanics of Materials (2023) Vol. 182, pp. 104684-104684
Closed Access | Times Cited: 18

Showing 18 citing articles:

Fabrication, Processing, Properties, and Applications of Closed-Cell Aluminum Foams: A Review
Wensheng Fu, Yanxiang Li
Materials (2024) Vol. 17, Iss. 3, pp. 560-560
Open Access | Times Cited: 9
Construction and deformation behavior of metal foam based on a 3D-Voronoi model with real pore structure
Mengzhen Cao, Tianwei Qiu, Baixing Deng, et al.
Materials & Design (2024) Vol. 238, pp. 112729-112729
Open Access | Times Cited: 7
Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks
Mohammad Rezasefat, James D. Hogan
Machine Learning Science and Technology (2024) Vol. 5, Iss. 1, pp. 015038-015038
Open Access | Times Cited: 7
Prediction of microstructural-dependent mechanical properties, progressive damage, and stress distribution from X-ray computed tomography scans using a deep learning workflow
Mohammad Rezasefat, Haoyang Li, James D. Hogan
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 424, pp. 116878-116878
Open Access | Times Cited: 6
Effect of Chemical Foaming Process Parameters on the Performance of Epoxy Foam and Parameter Optimization Strategies
Junzhen Chen, Long Cheng, Yuxia Hu, et al.
Advanced Engineering Materials (2025)
Closed Access
Mesoscale study on the mechanical properties and energy absorption characteristics of aluminum foam-filled CFRP tubes under axial compression
Weimin Zhuang, Enming Wang, Ditong Zhang, et al.
Mechanics of Advanced Materials and Structures (2023) Vol. 31, Iss. 28, pp. 10330-10346
Closed Access | Times Cited: 10
High temperature and mesostructure effect on aluminum foam compression responses
S.Q. Xiao, Zeang Zhao, Shengyu Duan, et al.
International Journal of Mechanical Sciences (2024) Vol. 275, pp. 109344-109344
Closed Access | Times Cited: 3
Stochastic reconstruction and performance prediction of cathode microstructures based on deep learning
Xinwei Yang, Chunwang He, Le Yang, et al.
Journal of Power Sources (2024) Vol. 603, pp. 234410-234410
Closed Access | Times Cited: 2
Self-constructed strategy-based reinforcement LSTM approach for fiber-reinforced polymer non-linear degradation performance analysis
Zhicen Song, Yunwen Feng, Cheng Lu, et al.
Composites Science and Technology (2023) Vol. 248, pp. 110414-110414
Closed Access | Times Cited: 5
Comparative life cycle assessment and multi-criteria decision analysis of coffee capsules made with conventional and innovative materials
Maria Pia Desole, Annamaria Gisario, M. Barletta
Sustainable Production and Consumption (2024) Vol. 48, pp. 99-122
Open Access | Times Cited: 1
Prediction of the compressive mechanical properties and reverse structural design of two-dimensional mesoscopic aluminum foam based on deep learning methods
Weimin Zhuang, Enming Wang, Hailun Zhang
Journal of Materials Science (2024) Vol. 59, Iss. 25, pp. 11416-11439
Closed Access | Times Cited: 1
High temperature in-situ 3D monitor of microstructure evolution and heat transfer performance of metal foam
S.Q. Xiao, Tianhua Wen, Zhaoliang Qu, et al.
Applied Thermal Engineering (2024) Vol. 259, pp. 124864-124864
Closed Access | Times Cited: 1
Microstructural characterization of bimodal composite metal foams under compression with machine learning
Tamás Bubonyi, Péter Barkóczy, Alexandra Kemény, et al.
Composites Part A Applied Science and Manufacturing (2024) Vol. 185, pp. 108292-108292
Open Access
Unveiling Enhanced Properties via Microstructural Evolution in Stir‐Cast Al6061 Composite Reinforced with AlCrFeNiTi High‐Entropy Alloy Particles.
Anand Sekhar R, Rakesh Pillai R, N. Firoz, et al.
Advanced Engineering Materials (2024) Vol. 26, Iss. 16
Closed Access
Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics
Mohammad Rezasefat, James D. Hogan
Deleted Journal (2024) Vol. 4, pp. 100011-100011
Open Access
Virtual rapid prototyping of materials with deep learning: spatiotemporal stress fields prediction in ceramics employing convolutional neural networks and transfer learning
Mohammad Rezasefat, James D. Hogan
Virtual and Physical Prototyping (2024) Vol. 19, Iss. 1
Open Access
Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled Super learner machine-learning algorithms
Atwakyire Moses, Ying Gui, B.H. Chen, et al.
Mechanics of Materials (2024), pp. 105168-105168
Closed Access
Preparation, testing, and design of a radar‐absorption polymethacrylimide foam
Shijun Song, Jie Yuan, Rongxia Duan, et al.
Journal of Polymer Science (2023) Vol. 62, Iss. 7, pp. 1350-1360
Closed Access | Times Cited: 1
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