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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 658.5:004
COBISS: 76795907 Povezava se bo odprla v novem oknu
ISSN: 0007-8506
Št. ogledov: 178
Št. prenosov: 15
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: proizvodni sistemi;digitalni dvojčki;strojno učenje;
Vrsta dela (COBISS): Članek v reviji
Konec prepovedi (OpenAIRE): 2023-06-11
Strani: str. 349-352
Letnik: ǂVol. ǂ70
Zvezek: ǂiss. ǂ1
Čas izdaje: 2021
DOI: 10.1016/j.cirp.2021.04.049
ID: 13470067