A meta-model for modular composition of tailored human digital twins in production

Abstract

Multiple and diverse factory digital twins have been proposed in the literature. However, despite the recognized growing importance of workers in smart and autonomous industrial settings, such models still lack or oversimplify human representation. Human digital twins must include human monitoring and behavioural data and models based on psychophysical status, knowledge, skills, and personal needs to manage production systems that aim, at the same time, to achieve process performance and workers' wellbeing. This paper proposes a metamodel based on data, events, and connectors that supports the modular composition of tailored human digital twins. This work also addresses an industrial application of the metamodel for preliminary validation.