Rok Vrabič (Author), John Erkoyuncu (Author), Maryam Farsi (Author), Dedy Ariansyah (Author)

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.

Keywords

proizvodni sistemi;digitalni dvojčki;strojno učenje;manufacturing systems;digital twin;machine learning;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 658.5:004
COBISS: 76795907 Link will open in a new window
ISSN: 0007-8506
Views: 178
Downloads: 15
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: proizvodni sistemi;digitalni dvojčki;strojno učenje;
Type (COBISS): Article
Embargo end date (OpenAIRE): 2023-06-11
Pages: str. 349-352
Volume: ǂVol. ǂ70
Issue: ǂiss. ǂ1
Chronology: 2021
DOI: 10.1016/j.cirp.2021.04.049
ID: 13470067