A systematic online update method for reduced-order-model-based digital twin DOI
Yifan Tang,

Pouyan Sajadi,

Mostafa Rahmani Dehaghani

et al.

Journal of Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Language: Английский

Transforming Additive Manufacturing with Artificial Intelligence: A Review of Current and Future Trends DOI
Shyam S. Pancholi, Munish Kumar Gupta, Marian Bartoszuk

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Exploring the integration of digital twin and additive manufacturing technologies DOI Creative Commons
Nursultan Jyeniskhan, Kemel Shomenov, Md. Hazrat Ali

et al.

International Journal of Lightweight Materials and Manufacture, Journal Year: 2024, Volume and Issue: 7(6), P. 860 - 881

Published: June 26, 2024

This paper offers a comprehensive overview of recent advancements in digital twin technology applied to additive manufacturing (AM), focusing on research trends, methodologies, and the integration machine learning. By identifying emerging developments addressing challenges, it serves as roadmap for future research. Specifically, examines various AM types, evolving methodologies within frameworks, highlighting role learning enhancing processes. Ultimately, aims underscore significance advancing smart practices. A total 133 papers were identified analysis through IEEExplore, ScienceDirect, Web Science, Google Scholar web resource. Approximately 74% are journals 21% conferences proceedings. Moreover, 78% journal Q1 journals. The identifies potential benefits twins at different levels, existing problems associated with implementing manufacturing, advancements, approaches, framework. review provides current landscape utilizing latest resources identify cutting-edge methodologies. Through an exploration implementation valuable insights researchers practitioners field. Additionally, contributes discourse by offering nuanced discussion directions, paving way further advancements.

Language: Английский

Citations

2
Computer Science Integrations with Laser Processing for Advanced Solutions DOI Creative Commons
Serguei P. Murzin

Photonics, Journal Year: 2024, Volume and Issue: 11(11), P. 1082 - 1082

Published: Nov. 18, 2024

This article examines the role of computer science in enhancing laser processing techniques, emphasizing transformative potential their integration into manufacturing. It discusses key areas where computational methods enhance precision, adaptability, and performance operations. Through advanced modeling simulation a deeper understanding material behavior under irradiation was achieved, enabling optimization parameters reduction defects. The intelligent control systems, driven by machine learning artificial intelligence, examined, showcasing how real-time data analysis adjustments lead to improved process reliability quality. utilization computer-generated diffractive optical elements (DOEs) emphasized as means precisely beam characteristics, thus broadening application opportunities across various industries. Additionally, significance predictive analyses manufacturing effectiveness sustainability is discussed. While challenges such need for specialized expertise investment new technologies persist, this underscores considerable advantages integrating with processing. Future research should aim address these challenges, further improving quality, processes.

Language: Английский

Citations

2
A digital twin framework for anomaly detection in industrial robot system based on multiple physics-informed hybrid convolutional autoencoder DOI
Shijie Wang, Jianfeng Tao,

Qincheng Jiang

et al.

Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 77, P. 798 - 809

Published: Oct. 31, 2024

Language: Английский

Citations

1
Human-in-the-loop Multi-objective Bayesian Optimization for Directed Energy Deposition with in-situ monitoring DOI Creative Commons
João Sousa, Armando Sousa, Frank Brueckner

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2024, Volume and Issue: 92, P. 102892 - 102892

Published: Nov. 7, 2024

Language: Английский

Citations

1
Current Applications of Machine Learning in Additive Manufacturing: A Review on Challenges and Future Trends DOI
Govind Vashishtha, Sumika Chauhan, Radosław Zimroz

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Language: Английский

Citations

1
A systematic online update method for reduced-order-model-based digital twin DOI
Yifan Tang,

Pouyan Sajadi,

Mostafa Rahmani Dehaghani

et al.

Journal of Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Language: Английский

Citations

0