An industrial edge computing architecture for Local Digital Twin

Cyber–Physical Production Systems usher in a new era of smart manufacturing, with Digital Twin being a pivotal concept driving their adoption. Most literature concentrates on global Digital Twin deployment, typically enterprise servers or the cloud. However, manufacturing relies on real-time control...

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Veröffentlicht in:Computers & industrial engineering 2024-07, Vol.193, p.110257, Article 110257
Hauptverfasser: Kondo, Ricardo Eiji, Andrade, Willian Jeferson, de Mello Henequim, Clayton, Lazzaretti, André Eugenio, de Souza Britto, Alceu, de Freitas Rocha Loures, Eduardo, Santos, Eduardo Alves Portela, Reynoso-Meza, Gilberto
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Sprache:eng
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Zusammenfassung:Cyber–Physical Production Systems usher in a new era of smart manufacturing, with Digital Twin being a pivotal concept driving their adoption. Most literature concentrates on global Digital Twin deployment, typically enterprise servers or the cloud. However, manufacturing relies on real-time control and decision-making, demanding swift system response. To address this, we introduce a novel Local Digital Twin architecture, bringing processing to the edge to meet strict timing constraints. We also introduce a Five-dimension GDT/LDTs model aligned with RAMI4.0 for practical application and integration. The LDT was implemented on a multinational company’s Brazilian subsidiary assembly line, predicting optimal assembly components. Machine learning techniques were employed, improving the First Pass Yield by 1.3 to 2.5% in a test machine, affirming the feasibility of the LDT approach. •LDT acts as a decentralized GDT subsystem, enhancing flexibility and reusability.•LDT mapped to RAMI4.0 to demonstrate its application level and interactions.•Local services, storage, models and edge connections ensure near real-time response.•Industrial PC-Based Control Edge Device for robust and flexible LDT implementation.•Implemented in assembly line, raising First Pass Yield between 1.3 to 2.5%.
ISSN:0360-8352
DOI:10.1016/j.cie.2024.110257