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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 Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
David Merayo, Álvaro Rodríguez-Prieto, Ana María Camacho
Materials (2020) Vol. 13, Iss. 22, pp. 5227-5227
Open Access | Times Cited: 56

Showing 1-25 of 56 citing articles:

Machine learning aided nanoindentation: A review of the current state and future perspectives
E.S. Puchi-Cabrera, Edoardo Rossi, Giuseppe Sansonetti, et al.
Current Opinion in Solid State and Materials Science (2023) Vol. 27, Iss. 4, pp. 101091-101091
Open Access | Times Cited: 34
Machine learning modeling for the prediction of plastic properties in metallic glasses
Nicolás Amigó, Simón Palominos, Felipe J. Valencia
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 20
Data-driven stress and strain curves of the unidirectional composites by deep neural networks with principal component analysis and selective-data augmentation
Kyung Hwan Kim, Myeong‐Seok Go, Jae Hyuk Lim, et al.
Composite Structures (2023) Vol. 313, pp. 116902-116902
Closed Access | Times Cited: 20
Correlation between scratch hardness and plastic parameters of metallic materials: A comparative study of machine learning and empirical solution
Peng Teng, Tingwei Sun, Yuan‐Xin Li, et al.
Journal of materials research/Pratt's guide to venture capital sources (2025)
Closed Access
Comparative Analysis of Response Surface Methodology and Artificial Neural Network on the Wear Properties of Surface Composite Fabricated by Friction Stir Processing
Lakshay Tyagi, Ravi Butola, Luckshaya Kem, et al.
Journal of Bio- and Tribo-Corrosion (2021) Vol. 7, Iss. 2
Closed Access | Times Cited: 32
Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts
Sivarao Subramonian, K. Kadirgama, Abdulkareem Sh. Mahdi Al‐Obaidi, et al.
Engineering Technology & Applied Science Research (2023) Vol. 13, Iss. 5, pp. 11677-11684
Open Access | Times Cited: 12
Application of Artificial Intelligence at All Stages of Bone Tissue Engineering
E. R. Kolomenskaya, Vera V. Butova, Artem Poltavskiy, et al.
Biomedicines (2023) Vol. 12, Iss. 1, pp. 76-76
Open Access | Times Cited: 11
Manufacturing process encoding through natural language processing for prediction of material properties
Ana P. O. Costa, Mariana R. R. Seabra, J De, et al.
Computational Materials Science (2024) Vol. 237, pp. 112896-112896
Open Access | Times Cited: 3
Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis
Zhongyuan Che, Chong Peng
Mathematics (2024) Vol. 12, Iss. 8, pp. 1153-1153
Open Access | Times Cited: 3
Sustainable Revolution: AI-Driven Enhancements for Composite Polymer Processing and Optimization in Intelligent Food Packaging
Sana Yakoubi
Food and Bioprocess Technology (2024)
Closed Access | Times Cited: 3
Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique
Ali Khalfallah, Amine Khalfallah, Zohra Benzarti
Coatings (2024) Vol. 14, Iss. 6, pp. 683-683
Open Access | Times Cited: 3
Effect of temperature and relative humidity on hydrolytic degradation of additively manufactured PLA: characterization and artificial neural network modeling
Suha Lee, Jung-Wook Wee
Polymer Degradation and Stability (2024), pp. 111055-111055
Closed Access | Times Cited: 3
A feasibility study of machine learning-assisted alloy design using wrought aluminum alloys as an example
Yasaman J. Soofi, Md Asad Rahman, Yijia Gu, et al.
Computational Materials Science (2022) Vol. 215, pp. 111783-111783
Closed Access | Times Cited: 14
The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test
Jakub Gajewski, Przemysław Golewski, Tomasz Sadowski
Materials (2021) Vol. 14, Iss. 2, pp. 419-419
Open Access | Times Cited: 19
Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering
W. Sitek, J. Trzaska
Metals (2021) Vol. 11, Iss. 11, pp. 1832-1832
Open Access | Times Cited: 19
Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning
David Merayo, Álvaro Rodríguez-Prieto, Ana María Camacho
Metals (2021) Vol. 11, Iss. 8, pp. 1289-1289
Open Access | Times Cited: 18
Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming
Tomaž Pepelnjak, Luka Sevšek, Ognjan Lužanin, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3707-3707
Open Access | Times Cited: 13
Estimation and prediction of the air–water interfacial tension in conventional and peptide surface-active agents by random Forest regression
Fabián Ricardo, Paola Ruiz Puentes, Luis H. Reyes, et al.
Chemical Engineering Science (2022) Vol. 265, pp. 118208-118208
Open Access | Times Cited: 13
A systematic study of interface properties and fracture behavior of graphene/aluminum: Insights from a first-principles study
Jingtao Huang, Yong Liu, Zhonghong Lai, et al.
Vacuum (2022) Vol. 204, pp. 111346-111346
Closed Access | Times Cited: 11
An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery
Yasaman J. Soofi, Yijia Gu, Jinling Liu
Computational Materials Science (2023) Vol. 226, pp. 112248-112248
Closed Access | Times Cited: 6
Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
Stephania Kossman, Maxence Bigerelle
Materials (2021) Vol. 14, Iss. 22, pp. 7027-7027
Open Access | Times Cited: 13
Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties
Bulat N. Galimzyanov, Maria A. Doronina, А. В. Мокшин
Metals (2023) Vol. 13, Iss. 4, pp. 812-812
Open Access | Times Cited: 4
A novel machine-learning schemes to predict heat transfer coefficient during condensation of CO2 in porous media
Rami Al-Jarrah, Mohammad Tarawneh
Journal of Thermal Analysis and Calorimetry (2023) Vol. 148, Iss. 14, pp. 7281-7298
Closed Access | Times Cited: 4
Determining Homogenization Parameters and Predicting 5182-Sc-Zr Alloy Properties by Artificial Neural Networks
Jingxiao Li, Dongfang Du, Xiaofang Yang, et al.
Materials (2023) Vol. 16, Iss. 15, pp. 5315-5315
Open Access | Times Cited: 4
A DPSO-BP NN modeling for predicting mechanical property: a case of 6181H18 aluminum alloy
Jianping Zhang, Chenghua Hu, Hongzhu Yan
Applied Physics A (2024) Vol. 130, Iss. 4
Closed Access | Times Cited: 1
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