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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:

Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting
Germán Barrionuevo, Jorge Ramos‐Grez, Magdalena Walczak, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 113, Iss. 1-2, pp. 419-433
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Research and application of machine learning for additive manufacturing
Jian Qin, Fu Hu, Ying Liu, et al.
Additive manufacturing (2022) Vol. 52, pp. 102691-102691
Open Access | Times Cited: 240
A new lightweight deep neural network for surface scratch detection
Wei Li, Liangchi Zhang, Chuhan Wu, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 123, Iss. 5-6, pp. 1999-2015
Open Access | Times Cited: 87
Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review
Fengyang He, Lei Yuan, Haochen Mu, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 82, pp. 102525-102525
Closed Access | Times Cited: 65
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: 39
Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning: The state-of-the-art and research challenges
Peng Wang, Yiran Yang, Narges Shayesteh Moghaddam
Journal of Manufacturing Processes (2021) Vol. 73, pp. 961-984
Closed Access | Times Cited: 81
Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting
Iván La Fé-Perdomo, Jorge Ramos‐Grez, Ignacio Jeria, et al.
Journal of Manufacturing Processes (2022) Vol. 80, pp. 666-682
Closed Access | Times Cited: 39
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
A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion
Kun Li, Ruijin Ma, Qin Yu, et al.
Journal of Materials Processing Technology (2023) Vol. 318, pp. 118032-118032
Closed Access | Times Cited: 37
Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions
Feipeng Wang, Wing‐Keung Wong, Zheng Wang, et al.
Resources Policy (2023) Vol. 85, pp. 103747-103747
Closed Access | Times Cited: 33
Microhardness and wear resistance in materials manufactured by laser powder bed fusion: Machine learning approach for property prediction
Germán Barrionuevo, Magdalena Walczak, Jorge Ramos‐Grez, et al.
CIRP journal of manufacturing science and technology (2023) Vol. 43, pp. 106-114
Closed Access | Times Cited: 25
A state-of-the-art review on metal additive manufacturing: milestones, trends, challenges and perspectives
Pushkal Badoniya, Manu Srivastava, Prashant K. Jain, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2024) Vol. 46, Iss. 6
Closed Access | Times Cited: 14
Machine learning-supported manufacturing: a review and directions for future research
Baris Ördek, Yuri Borgianni, Éric Coatanéa
Production & Manufacturing Research (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 13
Machine learning for advancing laser powder bed fusion of stainless steel
Walaa Abd‐Elaziem, Sally Elkatatny, Tamer A. Sebaey, et al.
Journal of Materials Research and Technology (2024) Vol. 30, pp. 4986-5016
Open Access | Times Cited: 9
Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning
Jianjian Zhu, Zhongqing Su, Qingqing Wang, et al.
Computers in Industry (2024) Vol. 156, pp. 104066-104066
Closed Access | Times Cited: 8
Influence of the Processing Parameters on the Microstructure and Mechanical Properties of 316L Stainless Steel Fabricated by Laser Powder Bed Fusion
Germán Barrionuevo, Jorge Ramos‐Grez, Xavier Sánchez-Sánchez, et al.
Journal of Manufacturing and Materials Processing (2024) Vol. 8, Iss. 1, pp. 35-35
Open Access | Times Cited: 8
Optimization of Process Parameters in Laser Powder Bed Fusion of SS 316L Parts Using Artificial Neural Networks
Sumanth Theeda, Shweta Hanmant Jagdale, Bharath Bhushan Ravichander, et al.
Metals (2023) Vol. 13, Iss. 5, pp. 842-842
Open Access | Times Cited: 16
Density prediction for selective laser melting fabricated of CuCrZr alloy using hybrid Gaussian boosted regression
Guangzhao Yang, Mingxuan Cao, Yixun Cai, et al.
Journal of Laser Applications (2025) Vol. 37, Iss. 1
Closed Access
Performance evaluation of machine learning techniques in surface roughness prediction for 3D printed micro-lattice structures
B. Veera Siva Reddy, Ameer Malik Shaik, C. Chandrasekhara Sastry, et al.
Journal of Manufacturing Processes (2025) Vol. 137, pp. 320-341
Closed Access
Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples
Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, et al.
PLoS ONE (2025) Vol. 20, Iss. 3, pp. e0316600-e0316600
Open Access
A machine learning approach for the prediction of melting efficiency in wire arc additive manufacturing
Germán Barrionuevo, Pedro M. Sequeira-Almeida, Sergio Ríos, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 5-6, pp. 3123-3133
Closed Access | Times Cited: 26
Laser Additive Manufacturing of High-Strength Aluminum Alloys: Challenges and Strategies
Som Dixit, Shunyu Liu
Journal of Manufacturing and Materials Processing (2022) Vol. 6, Iss. 6, pp. 156-156
Open Access | Times Cited: 26
An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects
Pranab Jyoti Das, Dilwar Hussain Mazumder
Artificial Intelligence Review (2023) Vol. 56, Iss. 9, pp. 9809-9836
Open Access | Times Cited: 15
Revolutionizing 3D concrete printing: Leveraging RF model for precise printability and rheological prediction
Songyuan Geng, Mei Liu, Boyuan Cheng, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109127-109127
Closed Access | Times Cited: 4
Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys
Germán Barrionuevo, Iván La Fé-Perdomo, Jorge Ramos‐Grez
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
Effects of powder compression and laser re-melting on the microstructure and mechanical properties of additively manufactured parts in laser-powder bed fusion
Muhannad Ahmed Obeidi, Alex Conway, Andre Mussatto, et al.
Results in Materials (2022) Vol. 13, pp. 100264-100264
Open Access | Times Cited: 20
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