In the context of the Industry 4.0 approach, applications and solutions supporting monitoring, simulation, optimisation and decision-making in production systems are exponentially growing. These solutions are commonly built on digital twins, i.e., comprehensive, structured and effective digital representations of the production system and its entities, whose current status is constantly updated by the plugged data sources. The arising of the Industry 5.0 paradigm and the established key role of workers in manufacturing require new Digital Twins to represent also humans. In fact, as cognitive automation becomes more and more pervasive and its behaviour unintelligible to humans, it becomes essential for improving performance and well-being, at the same time, to model humans as data-driven agents and to represent their interaction with the factory systems. Currently, a standardised solution for creating Digital Twins is missing, forcing industrial solution architects to resort to ad-hoc implementations and models. These solutions lack re-usability, scalability and extensibility, preventing the introduction of a human digital representation in existent twins, so hindering the complete shift to the new Industry 5.0 paradigm. In this paper, such limitations are faced by introducing an extensible and flexible IIoT - industrial internet of things - based platform with a twofold benefit: on the one hand, to support the creation of customised data representations of production systems and their entities including humans; on the other hand, to provide a modular infrastructure, along with its interchangeable components, for easy digital twin instantiation and ramp-up. An implementation of the platform has been tested with different applications in a laboratory setting and released as a public resource. Finally, potential future applications of the proposed digital twin are discussed, highlighting its main benefits.

An IIoT Platform For Human-Aware Factory Digital Twins

Montini E.;Rocco P.;
2022-01-01

Abstract

In the context of the Industry 4.0 approach, applications and solutions supporting monitoring, simulation, optimisation and decision-making in production systems are exponentially growing. These solutions are commonly built on digital twins, i.e., comprehensive, structured and effective digital representations of the production system and its entities, whose current status is constantly updated by the plugged data sources. The arising of the Industry 5.0 paradigm and the established key role of workers in manufacturing require new Digital Twins to represent also humans. In fact, as cognitive automation becomes more and more pervasive and its behaviour unintelligible to humans, it becomes essential for improving performance and well-being, at the same time, to model humans as data-driven agents and to represent their interaction with the factory systems. Currently, a standardised solution for creating Digital Twins is missing, forcing industrial solution architects to resort to ad-hoc implementations and models. These solutions lack re-usability, scalability and extensibility, preventing the introduction of a human digital representation in existent twins, so hindering the complete shift to the new Industry 5.0 paradigm. In this paper, such limitations are faced by introducing an extensible and flexible IIoT - industrial internet of things - based platform with a twofold benefit: on the one hand, to support the creation of customised data representations of production systems and their entities including humans; on the other hand, to provide a modular infrastructure, along with its interchangeable components, for easy digital twin instantiation and ramp-up. An implementation of the platform has been tested with different applications in a laboratory setting and released as a public resource. Finally, potential future applications of the proposed digital twin are discussed, highlighting its main benefits.
2022
Procedia CIRP
Cyber Physical Systems (CPS)
Human Digital Twin (HDT)
Industrial Internet of Things (IIoT)
Industry 5.0
Reference Data Model
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1232346
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