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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:

Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions
Zhuo Wang, Wenhua Yang, Qingyang Liu, et al.
Journal of Manufacturing Processes (2022) Vol. 77, pp. 13-31
Closed Access | Times Cited: 63

Showing 26-50 of 63 citing articles:

Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing
Deepak Akhare, Tengfei Luo, Jianxun Wang
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 406, pp. 115902-115902
Open Access | Times Cited: 11
A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing
Mukesh Chandra, K. E. K. Vimal, Sonu Rajak
International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) Vol. 18, Iss. 9, pp. 6625-6638
Closed Access | Times Cited: 11
Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning
Masahiro Kusano, Makoto Watanabe
Integrating materials and manufacturing innovation (2024) Vol. 13, Iss. 1, pp. 288-304
Open Access | Times Cited: 4
A review of the energy storage system as a part of power system: Modelling, simulation and prospect
Shanxiang Mao, Junru Chen, Muyang Liu
Electric Power Systems Research (2024) Vol. 233, pp. 110448-110448
Closed Access | Times Cited: 4
Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques
Mandar Golvaskar, Sammy A. Ojo, K. Manigandan
Recycling (2024) Vol. 9, Iss. 3, pp. 43-43
Open Access | Times Cited: 4
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Amirul Islam Saimon, Emmanuel Yangue, Xiaowei Yue, et al.
IISE Transactions (2024), pp. 1-44
Open Access | Times Cited: 4
Unveiling Processing–Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
Mahsa Amiri, Zahra Zanjani Foumani, Penghui Cao, et al.
Materials & Design (2025), pp. 113705-113705
Open Access
Multi-objective robust parameter optimization using the extended and weighted k-means (EWK-means) clustering in laser powder bed fusion (LPBF)
Adrian Matias Chung Baek, Eunju Park, Minkyu Seong, et al.
Expert Systems with Applications (2023) Vol. 236, pp. 121349-121349
Closed Access | Times Cited: 10
Architecture-Driven Physics-Informed Deep Learning for Temperature Prediction in Laser Powder Bed Fusion Additive Manufacturing With Limited Data
Suyog Ghungrad, Meysam Faegh, Benjamin Gould, et al.
Journal of Manufacturing Science and Engineering (2023) Vol. 145, Iss. 8
Closed Access | Times Cited: 9
Robust parameter design for 3D printing process using stochastic computer model
Chunfeng Ding, Jianjun Wang, Yiliu Tu, et al.
Simulation Modelling Practice and Theory (2024) Vol. 132, pp. 102896-102896
Closed Access | Times Cited: 3
A digital twin for smart manufacturing of structural composites by liquid moulding
Joaquín Fernández-León, Keayvan Keramati, Luis Baumela, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 130, Iss. 9-10, pp. 4679-4697
Closed Access | Times Cited: 3
Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process
Deepak Akhare, Zeping Chen, Richard Gulotty, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 3
Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit
Kaixiong Hu, Kaikai Guo, Weidong Li, et al.
Advances in Manufacturing (2024)
Closed Access | Times Cited: 3
Minireview on Porous Media and Microstructure Reconstruction Using Machine Learning Techniques: Recent Advances and Outlook
Hossein Mirzaee, Serveh Kamrava, Pejman Tahmasebi
Energy & Fuels (2023) Vol. 37, Iss. 20, pp. 15348-15372
Closed Access | Times Cited: 7
What can machine learning help with microstructure-informed materials modeling and design?
Xiang-Long Peng, Mozhdeh Fathidoost, Binbin Lin, et al.
MRS Bulletin (2024)
Open Access | Times Cited: 2
Time-dependent deep learning predictions of 3D electrode particle-resolved microstructure effect on voltage discharge curves
Wenhua Yang, X.X. Yao, Zhuo Wang, et al.
Journal of Power Sources (2023) Vol. 579, pp. 233087-233087
Closed Access | Times Cited: 5
Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, et al.
Computers in Industry (2023) Vol. 154, pp. 104037-104037
Closed Access | Times Cited: 5
Data-driven modeling of process-structure-property relationships in metal additive manufacturing
Zhaoyang Hu, Wentao Yan
Deleted Journal (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 1
Understanding the Effects of Manufacturing Attributes on Damage Tolerance of Additively Manufactured Parts and Exploring Synergy Among Process‐Structure‐Properties. A Comprehensive Review
Zia Uddin, Muhammad Muteeb Butt, Varma Kvvssn, et al.
Engineering Reports (2024) Vol. 6, Iss. 11
Open Access | Times Cited: 1
A review on physics-informed machine learning for process-structure-property modeling in additive manufacturing
Meysam Faegh, Suyog Ghungrad, João Pedro Oliveira, et al.
Journal of Manufacturing Processes (2024) Vol. 133, pp. 524-555
Open Access | Times Cited: 1
A Review of the Latest Developments in the Field of Additive Manufacturing Techniques for Nuclear Reactors
Hui Li, Xiaolong Fu, Liping Zhang, et al.
Crystals (2022) Vol. 12, Iss. 7, pp. 918-918
Open Access | Times Cited: 5
Deep learning for anomaly detection in wire-arc additive manufacturing
Mukesh Chandra, Abhinav Kumar, Sumit K. Sharma, et al.
Welding International (2023) Vol. 37, Iss. 8, pp. 457-467
Closed Access | Times Cited: 2
Challenges in modelling thermo-optical performance of 3D-printed facades: a cross-domain review
Valeria Piccioni, Matthias Leschok, Esther Borkowski, et al.
Building Simulation Conference proceedings (2023) Vol. 18
Open Access | Times Cited: 2
Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing
Suyog Ghungrad, Azadeh Haghighi
Manufacturing Letters (2024) Vol. 41, pp. 133-142
Closed Access
Modelling Process Monitoring Data in Laser Powder Bed Fusion: A Pragmatic Route to Additive Manufacturing Quality Assurance
Luke N. Carter, Victor M. Villapún, James W. Andrews, et al.
Additive Manufacturing Letters (2024), pp. 100252-100252
Open Access

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