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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:

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing
Dayalan Gunasegaram, Anthony B. Murphy, Amanda S. Barnard, et al.
Additive manufacturing (2021) Vol. 46, pp. 102089-102089
Open Access | Times Cited: 107

Showing 1-25 of 107 citing articles:

The 2022 Plasma Roadmap: low temperature plasma science and technology
Igor Adamovich, Sumit Agarwal, Eduardo Ahedo, et al.
Journal of Physics D Applied Physics (2022) Vol. 55, Iss. 37, pp. 373001-373001
Open Access | Times Cited: 323
Digital twin in healthcare: Recent updates and challenges
Tianze Sun, Xiwang He, Zhonghai Li
Digital Health (2023) Vol. 9
Open Access | Times Cited: 194
Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
Yanwen Xu, Sara Kohtz, Jessica Boakye, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108900-108900
Open Access | Times Cited: 183
Industrial digital twins at the nexus of NextG wireless networks and computational intelligence: A survey
Shah Zeb, Aamir Mahmood, Syed Ali Hassan, et al.
Journal of Network and Computer Applications (2022) Vol. 200, pp. 103309-103309
Open Access | Times Cited: 89
A literature review and design methodology for digital twins in the era of zero defect manufacturing
Foivos Psarommatis, Gökan May
International Journal of Production Research (2022) Vol. 61, Iss. 16, pp. 5723-5743
Open Access | Times Cited: 86
Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution
Mohammad Parsazadeh, Shashank Sharma, Narendra B. Dahotre
Progress in Materials Science (2023) Vol. 135, pp. 101102-101102
Closed Access | Times Cited: 66
Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects
M. Rahman, Tanveer Saleh, Muhammad P. Jahan, et al.
Micromachines (2023) Vol. 14, Iss. 3, pp. 508-508
Open Access | Times Cited: 51
Big data, machine learning, and digital twin assisted additive manufacturing: A review
Liuchao Jin, Xiaoya Zhai, Kang Wang, et al.
Materials & Design (2024) Vol. 244, pp. 113086-113086
Open Access | Times Cited: 45
Virtual manufacturing in Industry 4.0: A review
Mohsen Soori, Behrooz Arezoo, Roza Dastres
Data Science and Management (2023) Vol. 7, Iss. 1, pp. 47-63
Open Access | Times Cited: 44
Recent innovations in laser additive manufacturing of titanium alloys
Jinlong Su, Fulin Jiang, Jie Teng, et al.
International Journal of Extreme Manufacturing (2024) Vol. 6, Iss. 3, pp. 032001-032001
Open Access | Times Cited: 34
Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing
Dayalan Gunasegaram, Amanda S. Barnard, Manyalibo J. Matthews, et al.
Additive manufacturing (2024) Vol. 81, pp. 104013-104013
Open Access | Times Cited: 31
Online distortion simulation using generative machine learning models: A step toward digital twin of metallic additive manufacturing
Haochen Mu, Fengyang He, Lei Yuan, et al.
Journal of Industrial Information Integration (2024) Vol. 38, pp. 100563-100563
Open Access | Times Cited: 25
Digital twins in additive manufacturing: a state-of-the-art review
Tao Shen, Bo Li
The International Journal of Advanced Manufacturing Technology (2024) Vol. 131, Iss. 1, pp. 63-92
Closed Access | Times Cited: 23
An optimization-centric review on integrating artificial intelligence and digital twin technologies in manufacturing
Vispi Karkaria, Ying-Kuan Tsai, Yi-Ping Chen, et al.
Engineering Optimization (2025), pp. 1-47
Open Access | Times Cited: 3
Towards Zero Defect and Zero Waste Manufacturing by Implementing Non-Destructive Inspection Technologies
Joan Lario, Javier Mateos, Foivos Psarommatis, et al.
Journal of Manufacturing and Materials Processing (2025) Vol. 9, Iss. 2, pp. 29-29
Open Access | Times Cited: 3
Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data
Reza Yavari, Alex Riensche, Emine Tekerek, et al.
Materials & Design (2021) Vol. 211, pp. 110167-110167
Open Access | Times Cited: 65
A digital twin hierarchy for metal additive manufacturing
Arden Phua, C.H.J. Davies, Gary W. Delaney
Computers in Industry (2022) Vol. 140, pp. 103667-103667
Closed Access | Times Cited: 65
The case for digital twins in metal additive manufacturing
Dayalan Gunasegaram, Anthony B. Murphy, Manyalibo J. Matthews, et al.
Journal of Physics Materials (2021) Vol. 4, Iss. 4, pp. 040401-040401
Open Access | Times Cited: 63
Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing
Sandeep Suresh Babu, Abdel‐Hamid I. Mourad, Khalifa H. Harib, et al.
Virtual and Physical Prototyping (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 44
Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review
Shashank Sharma, Sameehan S. Joshi, Mangesh V. Pantawane, et al.
International Materials Reviews (2023) Vol. 68, Iss. 7, pp. 943-1009
Closed Access | Times Cited: 38
Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed Deep Learning Model
Mingzhi Zhao, Huiliang Wei, Yiming Mao, et al.
Engineering (2023) Vol. 23, pp. 181-195
Open Access | Times Cited: 33
Modeling of hierarchical solidification microstructures in metal additive manufacturing: Challenges and opportunities
Supriyo Ghosh, J. Zollinger, Miha Založnik, et al.
Additive manufacturing (2023) Vol. 78, pp. 103845-103845
Closed Access | Times Cited: 24
Metal AM process-structure-property relational linkages using Gaussian process surrogates
Robert N. Saunders, Kirubel Teferra, Alaa Elwany, et al.
Additive manufacturing (2023) Vol. 62, pp. 103398-103398
Open Access | Times Cited: 23
A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes
Usman Tariq, Ranjit Joy, Sung-Heng Wu, et al.
Rapid Prototyping Journal (2023) Vol. 29, Iss. 10, pp. 2061-2097
Closed Access | Times Cited: 23
Leveraging digital twin into dynamic production scheduling: A review
Nada Ouahabi, Ahmed Chebak, Oulaïd Kamach, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 89, pp. 102778-102778
Closed Access | Times Cited: 14
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