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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:

Application of machine learning methods on dynamic strength analysis for additive manufactured polypropylene-based composites
Ruijun Cai, Kui Wang, Wei Wen, et al.
Polymer Testing (2022) Vol. 110, pp. 107580-107580
Open Access | Times Cited: 44

Showing 26-50 of 44 citing articles:

System energy and band gap prediction of titanium dioxide based on machine learning
Shengbin Chen, Wenming Zhang, Rui Luo, et al.
Journal of Molecular Structure (2024) Vol. 1307, pp. 137934-137934
Closed Access | Times Cited: 2
Application of machine learning methods in the analysis of interface bonding strength for overmolded hybrid thermoset‐thermoplastic composites
Yulong Yin, Zhanyu Zhai, Yudong Ding
Polymer Composites (2024) Vol. 45, Iss. 9, pp. 8360-8373
Closed Access | Times Cited: 2
Investigating the Effect of Processing and Material Parameters of Alginate Dialdehyde-Gelatin (ADA-GEL)-Based Hydrogels on Stiffness by XGB Machine Learning Model
Duygu Ege, Aldo R. Boccaccını
Bioengineering (2024) Vol. 11, Iss. 5, pp. 415-415
Open Access | Times Cited: 2
Improving the Mechanical Performance of Biocomposite Plaster/ Washingtonia filifera : Optimization Comparison Between ANN and RSM Approaches
Ahmed Belaadi, Messaouda Boumaaza, Hassan Alshahrani, et al.
Journal of Natural Fibers (2023) Vol. 20, Iss. 1
Open Access | Times Cited: 6
Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models
Zhenhao Liao, Cheng Qiu, Jun Yang, et al.
Polymers (2022) Vol. 14, Iss. 15, pp. 3229-3229
Open Access | Times Cited: 9
Machine learning method for estimating the defect-related mechanical properties of additive manufactured alloys
Xun Wang, Lianyong Xu, Lei Zhao, et al.
Engineering Fracture Mechanics (2023) Vol. 291, pp. 109559-109559
Closed Access | Times Cited: 5
Investigation of effect of processing parameters of 3D printed NHS/EDC crosslinked carboxy methyl cellulose/gelatin hydrogels with machine learning techniques
Duygu Ege, Şule Arıcı
Materials Research Express (2024) Vol. 11, Iss. 4, pp. 045304-045304
Open Access | Times Cited: 1
Energy efficiency prediction model in fused deposition modeling based on Bayesian optimized random forest
Hong Bao, Yapeng Li, Qingdi Ke, et al.
Green Manufacturing Open (2024) Vol. 2, Iss. 3
Open Access | Times Cited: 1
Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation
Pouya Rajaee, Faramarz Ashenai Ghasemi, Amir Hossein Rabiee, et al.
Composites Part C Open Access (2024) Vol. 15, pp. 100535-100535
Open Access | Times Cited: 1
Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys
Zheng Shaojie, Xu Huang, Jijiang Hu, et al.
Reaction Chemistry & Engineering (2024) Vol. 9, Iss. 6, pp. 1354-1363
Open Access
Predicting the porosity of as-built additive manufactured samples based on machine learning method for small datasets
Qingyang Pi, Rui Li, Bin Han, et al.
Optics & Laser Technology (2024) Vol. 177, pp. 111203-111203
Closed Access
XGBoost machine learning assisted prediction of the mechanical and fracture properties of unvulcanized and dynamically vulcanized PP/EPDM reinforced with clay and halloysite nanoparticles
Pouya Rajaee, Amir Hossein Rabiee, Faramarz Ashenai Ghasemi, et al.
Polymer Composites (2024) Vol. 45, Iss. 16, pp. 14799-14815
Closed Access
Prediction of effective parameters for 3D printing of poly lactic acid-carbon fibre composites using intelligent frameworks based on mechanical response
Karthikeyan Marappan, M.P. Jenarthanan, K. Ghousiya Begum, et al.
Pigment & Resin Technology (2024)
Closed Access
A comprehensive review on application of machine intelligence in additive manufacturing
N. Ethiraj, T. Sivabalan, J. Sofia Vincent, et al.
Turkish Journal of Engineering (2024)
Open Access
Ensemble Learning Applications in Multiple Industries: A Review
Kuo-Yi Lin, Chancy Huang
Information Dynamics and Applications (2022) Vol. 1, Iss. 1, pp. 44-58
Open Access | Times Cited: 2
System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning
Shengbin Chen, Wenming Zhang, Rui Luo, et al.
(2023)
Closed Access
System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning
Shengbin Chen, Wenming Zhang, Rui Luo, et al.
(2023)
Closed Access
On‐Line Monitoring Device for Gas Phase Composition Based on Machine Learning Models and Its Application in the Gas Phase Copolymerization of Olefins
Xu Huang, Zheng Shaojie, Zhen Yao, et al.
Macromolecular Reaction Engineering (2023) Vol. 18, Iss. 1
Closed Access
Predicting the Porosity of As-Built Additive Manufactured Samples Based on Machine Learning Method for Small Datasets
Qingyang Pi, Rui Li, Bin Han, et al.
(2023)
Closed Access
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