A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

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

Abstract

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.

Citation

Mo, F., Rehman, H. U., Monetti, F. M., Chaplin, J. C., Sanderson, D., Popov, A., Maffei, A., & Ratchev, S. (2023). A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Robotics and Computer-Integrated Manufacturing, 82, Article 102524. https://doi.org/10.1016/j.rcim.2022.102524

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2022
• Online Publication Date: Jan 23, 2023
• Publication Date: 2023-08
• Deposit Date: Feb 21, 2023
• Publicly Available Date: Feb 21, 2023
• Journal: Robotics and Computer-Integrated Manufacturing
• Print ISSN: 0736-5845
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 82
• Article Number: 102524
• DOI: https://doi.org/10.1016/j.rcim.2022.102524
• Keywords: Reconfigurable manufacturing system; Modular artificial intelligence; Digital twin; Process simulation; Knowledge graphs
• Public URL: https://nottingham-repository.worktribe.com/output/16789986
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S073658452200206X?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence; Journal Title: Robotics and Computer-Integrated Manufacturing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.rcim.2022.102524; Content Type: article; Copyright: © 2023 The Author(s). Published by Elsevier Ltd.