An intelligent agent-based architecture for resilient digital twins in manufacturing

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dc.contributor.authorVrabič, Rok
dc.contributor.authorErkoyuncu, John Ahmet
dc.contributor.authorFarsi, Maryam
dc.contributor.authorAriansyah, Dedy
dc.date.accessioned2021-06-28T15:17:10Z
dc.date.available2021-06-28T15:17:10Z
dc.date.issued2021-06-11
dc.description.abstractDigital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturingen_UK
dc.identifier.citationVrabic R, Erkoyuncu JA, Farsi M, Ariansyah D. (2021) An intelligent agent-based architecture for resilient digital twins in manufacturing. CIRP Annals - Manufacturing Technology, Volume 70, Issue 1, 2021, pp. 349-352en_UK
dc.identifier.issn0007-8506
dc.identifier.urihttps://doi.org/10.1016/j.cirp.2021.04.049
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16815
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMachine learningen_UK
dc.subjectDigital twinen_UK
dc.subjectManufacturing systemen_UK
dc.titleAn intelligent agent-based architecture for resilient digital twins in manufacturingen_UK
dc.typeArticleen_UK

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