This paper explores the integration of Digital Twins (DTs) in Industry 4.0 and 5.0, highlighting their role in enhancing intelligent, collaborative industrial ecosystems. By representing processes, machinery, operators, and products, DTs enable comprehensive life-cycle support and improved shop-floor operations. Intelligent applications and services can harness DTs as structured and interoperable virtual replicas, entrusted with the responsibility of interfacing with the physical world and facilitating access and mediation of interactions therein. Our study proposes structured DT modeling in industrial ecosystems to demonstrate how DTs enable an effective decoupling of responsibilities and capabilities supporting precise monitoring and data synthesis, optimizing production workflows and maintenance. We discuss DTs’ potential in industrial quality control, highlighting efficiency gains and operational improvements in electric motor production through case studies.
Digital Twin Driven Collaboration in Industry 5.0 / Martinelli, Matteo; Pietri, Marcello; Rossini, Enrico; Picone, Marco; Mamei, Marco. - (2025), pp. 104-109. (Intervento presentato al convegno 2024 32nd International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) tenutosi a Reggio Emilia, Italy nel 26-28 June 2024) [10.1109/wetice64632.2024.00027].
Digital Twin Driven Collaboration in Industry 5.0
Martinelli, Matteo;Pietri, Marcello
;Rossini, Enrico;Picone, Marco;Mamei, Marco
2025
Abstract
This paper explores the integration of Digital Twins (DTs) in Industry 4.0 and 5.0, highlighting their role in enhancing intelligent, collaborative industrial ecosystems. By representing processes, machinery, operators, and products, DTs enable comprehensive life-cycle support and improved shop-floor operations. Intelligent applications and services can harness DTs as structured and interoperable virtual replicas, entrusted with the responsibility of interfacing with the physical world and facilitating access and mediation of interactions therein. Our study proposes structured DT modeling in industrial ecosystems to demonstrate how DTs enable an effective decoupling of responsibilities and capabilities supporting precise monitoring and data synthesis, optimizing production workflows and maintenance. We discuss DTs’ potential in industrial quality control, highlighting efficiency gains and operational improvements in electric motor production through case studies.Pubblicazioni consigliate
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