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  • 标题:Integration of Artificial Intelligence in the life cycle of industrial Digital Twins
  • 本地全文:下载
  • 作者:Farah Abdoune ; Maroua Nouiri ; Olivier Cardin
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2022
  • 卷号:55
  • 期号:10
  • 页码:2545-2550
  • DOI:10.1016/j.ifacol.2022.10.092
  • 语种:English
  • 出版社:Elsevier
  • 摘要: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.
  • 关键词:Digital Twin;Lifecycle;Artificial Intelligence;Machine Learning;Reinforcement learning;Unsupervised learning
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