An intelligent agent-based architecture for resilient digital twins in manufacturing. Issue 1 (2021)

• Record Type: Journal Article
• Authors: Vrabič, Rok; Erkoyuncu, John Ahmet; Farsi, Maryam; Ariansyah, Dedy
• Is Part Of: CIRP annals. Volume 70:Issue 1(2021)
• Page Start: 349
• Page End: 352
• Publication Date: 2021
• Subjects: Manufacturing system -- Digital twin -- Machine learning; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2021.04.049
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: Digital 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 manufacturing.