A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. (August 2023)

• Record Type: Journal Article
• Authors: Mo, Fan; Rehman, Hamood Ur; Monetti, Fabio Marco; Chaplin, Jack C.; Sanderson, David; Popov, Atanas; Maffei, Antonio; Ratchev, Svetan
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 82(2023)
• Publication Date: 2023-08
• Subjects: Reconfigurable manufacturing system -- Modular artificial intelligence -- Digital twin -- Process simulation -- Knowledge graphs; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102524
• Languages: English
• ISSNs: 0736-5845

Abstract

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%. Highlights: A dynamic reconfiguration of manufacturing systems using AI and digital twins. Combining knowledge graphs with simulation environments improves decision-making. Design of a data pipeline and an API to integrate multiple AI methods easily. Rapid response to changing needs in layout, parameters and operation time. A demonstration of an example of an industrial robotic manufacturing cell.