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A Framework for Manufacturing System Reconfiguration based on Artificial Intelligence and Digital Twin

Mo, Fan; Chaplin, Jack C.; Sanderson, David; Rehman, Hamood Ur; Monetti, Fabio Marco; Maffei, Antonio; Ratchev, Svetan

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Authors

Fan Mo

Hamood Ur Rehman

Fabio Marco Monetti

Antonio Maffei

Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
Cripps Professor of Production Engineering & Head of Research Division



Abstract

The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications.

Citation

Mo, F., Chaplin, J. C., Sanderson, D., Rehman, H. U., Monetti, F. M., Maffei, A., & Ratchev, S. (2022). A Framework for Manufacturing System Reconfiguration based on Artificial Intelligence and Digital Twin. In Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus Proceedings of FAIM 2022, June 19-23, 2022, Detroit, Michigan, USA, Volume 2 (361-373). https://doi.org/10.1007/978-3-031-18326-3_35

Presentation Conference Type Edited Proceedings
Conference Name Flexible Automation and Intelligent Manufacturing International Conference (FAIM 2022)
Start Date Jun 19, 2022
End Date Jun 23, 2022
Acceptance Date May 5, 2022
Online Publication Date Oct 13, 2022
Publication Date Oct 13, 2022
Deposit Date May 16, 2022
Publicly Available Date Oct 14, 2023
Publisher Springer
Pages 361-373
Series Title Lecture Notes in Mechanical Engineering
Series ISSN 2195-4356
Book Title Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus Proceedings of FAIM 2022, June 19-23, 2022, Detroit, Michigan, USA, Volume 2
ISBN 9783031183256
DOI https://doi.org/10.1007/978-3-031-18326-3_35
Public URL https://nottingham-repository.worktribe.com/output/8129288
Publisher URL https://link.springer.com/chapter/10.1007/978-3-031-18326-3_35
Additional Information First Online: 13 October 2022; Conference Acronym: FAIM; Conference Name: International Conference on Flexible Automation and Intelligent Manufacturing; Conference City: Detroit, MI; Conference Country: USA; Conference Year: 2022; Conference Start Date: 19 June 2022; Conference End Date: 23 June 2022; Conference ID: faim2022; Conference URL: https://www.faimconference.org/

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