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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:

Tensile strength prediction in directed energy deposition through physics-informed machine learning and Shapley additive explanations
Clayton Cooper, Jianjing Zhang, Joshua Huang, et al.
Journal of Materials Processing Technology (2023) Vol. 315, pp. 117908-117908
Open Access | Times Cited: 18

Showing 18 citing articles:

Use of interpretable machine learning approaches for quantificationally understanding the performance of steel fiber-reinforced recycled aggregate concrete: From the perspective of compressive strength and splitting tensile strength
S. Y. Zhang, Wenguang Chen, Jinjun Xu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 137, pp. 109170-109170
Closed Access | Times Cited: 21
Bond stress estimation of profiled steel-concrete in steel reinforced concrete composite structures using ensemble machine learning approaches
Shuyuan Zhang, Jinjun Xu, Tao Lai, et al.
Engineering Structures (2023) Vol. 294, pp. 116725-116725
Closed Access | Times Cited: 22
A novel data-driven framework for enhancing the consistency of deposition contours and mechanical properties in metal additive manufacturing
Miao Yu, Lida Zhu, Zhichao Yang, et al.
Computers in Industry (2024) Vol. 163, pp. 104154-104154
Closed Access | Times Cited: 10
A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing)
Jeewanthi Ukwaththa, Sumudu Herath, D.P.P. Meddage
Materials Today Communications (2024) Vol. 41, pp. 110294-110294
Open Access | Times Cited: 9
Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design
Pengcheng Jiao, Zhong Lin Wang, Amir H. Alavi
Advanced Materials (2023) Vol. 36, Iss. 5
Open Access | Times Cited: 16
End-to-end supply chain resilience management using deep learning, survival analysis, and explainable artificial intelligence
Xingyu Li, Vasiliy Krivtsov, Chaoye Pan, et al.
International Journal of Production Research (2024), pp. 1-29
Closed Access | Times Cited: 6
Neural network models and shapley additive explanations for a beam-ring structure
Ying Sun, Luying Zhang, Minghui Yao, et al.
Chaos Solitons & Fractals (2024) Vol. 185, pp. 115114-115114
Closed Access | Times Cited: 4
A novel physically interpretable end-to-end network for stress monitoring in laser shock peening
Rui Qin, Zhifen Zhang, Jing Huang, et al.
Computers in Industry (2023) Vol. 155, pp. 104060-104060
Closed Access | Times Cited: 8
Visual coating inspection framework via self-labeling and multi-stage deep learning strategies
Changheon Han, Jiho Lee, Martin Byung‐Guk Jun, et al.
Journal of Intelligent Manufacturing (2024)
Open Access | Times Cited: 2
Multi-sensor fusion and machine learning-driven sequence-to-sequence translation for interpretable process signature prediction in machining
Clayton Cooper, Jianjing Zhang, Ihab Ragai, et al.
Journal of Manufacturing Systems (2024) Vol. 75, pp. 288-298
Open Access | Times Cited: 2
An interpretable framework for high-precision flatness prediction in strip cold rolling
Chengyan Ding, Jun-Cheng Ye, Jia-Wei Lei, et al.
Journal of Materials Processing Technology (2024) Vol. 329, pp. 118452-118452
Closed Access | Times Cited: 2
Hybrid Semiconductor Wafer Inspection Framework via Autonomous Data Annotation
Changheon Han, Heebum Chun, Jiho Lee, et al.
Journal of Manufacturing Science and Engineering (2024) Vol. 146, Iss. 7
Closed Access | Times Cited: 1
Error homogenization in physics-informed neural networks for modeling in manufacturing
Clayton Cooper, Jianjing Zhang, Robert X. Gao
Journal of Manufacturing Systems (2023) Vol. 71, pp. 298-308
Open Access | Times Cited: 2
Machine Learning Assisted Determination of the Interfacial Heat Transfer Coefficient for the Horizontal Continuous Casting of Copper Tubular Billets
Dayong Chen, Li Wang, Hong-Wu Song, et al.
(2024)
Closed 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
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
Intelligent Prediction and Analysis of Mechanical Properties of Magnesium Alloys Using Interpretable Machine Learning
Atwakyire Moses, Ying Gui, Marembo Micheal, et al.
(2023)
Closed Access | Times Cited: 1
Framework for Incorporating Machine Learning (ML) Driven Optimisation into Laser Materials Processing (LMP) Technologies for e-Mobility Applications towards Attaining Zero-Material Waste
Rehema Ndeda, Annelize Botes, Eyitayo Olatunde Olakanmi
Lasers in Manufacturing and Materials Processing (2023) Vol. 11, Iss. 1, pp. 77-108
Closed Access
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