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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:

Developing a data-driven system for grinding process parameter optimization using machine learning and metaheuristic algorithms
Gyeongho Kim, S W Park, Jae Gyeong Choi, et al.
CIRP journal of manufacturing science and technology (2024) Vol. 51, pp. 20-35
Closed Access | Times Cited: 11

Showing 11 citing articles:

An intelligent prediction paradigm for milling tool parameters design based on multi-task tabular data deep transfer learning integrating physical knowledge
Caihua Hao, Weiye Li, Xinyong Mao, et al.
Journal of Manufacturing Processes (2025) Vol. 134, pp. 998-1020
Closed Access
Integrated Multi-strategy Sand Cat Swarm Optimization for Path Planning Applications
Yourui Huang, Quanzeng Liu, Tao Han, et al.
Intelligent Systems with Applications (2025), pp. 200486-200486
Open Access
Adaptive process parameters decision-making in robotic grinding based on meta-reinforcement learning
Jie Pan, Fan Chen, Dan Han, et al.
Journal of Manufacturing Processes (2025) Vol. 137, pp. 376-396
Closed Access
Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0
Saman Fattahi, Bahman Azarhoushang, Heike Kitzig-Frank
Journal of Manufacturing and Materials Processing (2025) Vol. 9, Iss. 2, pp. 62-62
Open Access
Integrating Artificial Intelligence in Nanomembrane Systems for Advanced Water Desalination
K. Anbarasu, S. Thanigaivel, N. Beemkumar, et al.
Results in Engineering (2024) Vol. 24, pp. 103321-103321
Open Access | Times Cited: 4
Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V
Gyeongho Kim, Sang Min Yang, Dong Min Kim, et al.
Journal of Manufacturing Systems (2024) Vol. 76, pp. 133-157
Closed Access | Times Cited: 3
Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing
Katarzyna Antosz, Lucia Knapčíková, Jozef Husár
Applied Sciences (2024) Vol. 14, Iss. 22, pp. 10450-10450
Open Access | Times Cited: 2
Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions
Gyeongho Kim, Yun Seok Kang, Sang Min Yang, et al.
Reliability Engineering & System Safety (2024) Vol. 253, pp. 110549-110549
Closed Access | Times Cited: 1
State-of-the-art review on various applications of machine learning techniques in materials science and engineering
Bing Yu, Lai‐Chang Zhang, Xiaoxia Ye, et al.
Chemical Engineering Science (2024), pp. 121147-121147
Closed Access | Times Cited: 1
CNC linear axis condition-based monitoring: a statistics-based framework to establish a baseline dataset and case study
Andres Hurtado Carreon, Jose M. DePaiva, Rohan Kumar Barooah, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access
Modeling and experimental study of the force and surface topography in cylindrical grinding of GH4169
Zhipeng Li, Quanli Zhang, Bao Wang, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access
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