Integration of Artificial Intelligence in the life cycle of industrial Digital Twins. Issue 10 (2022)

• Record Type: Journal Article
• Authors: Abdoune, Farah; Nouiri, Maroua; Cardin, Olivier; Castagna, Pierre
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 10(2022)
• Page Start: 2545
• Page End: 2550
• Publication Date: 2022
• Subjects: Digital Twin -- Lifecycle -- Artificial Intelligence -- Machine Learning -- Reinforcement learning -- Unsupervised learning; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.10.092
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Digital twins (DT) constitute a major concept of future industrial systems. They are expected to enable efficient virtualization of manufacturing systems and enhance various decision-making processes. In parallel, many initiatives exhibited how artificial intelligence (AI) could increase the performance of the DT on specific applications. By reviewing the literature combining AI and DT, a lack of contributions on the whole life cycle of the DT was exhibited. Therefore, the main contribution of this paper is to define a global integration framework of AI into DT, focused on the exploitation phase of the DT. A case study, using a relatively simple physical twin, illustrates the potential of such integration for the response of the DT to unpredictable modifications of the physical twin.