Surrogate modelling for continuous ergonomic assessment and adaptive configuration of industrial human-centered workplaces

Abstract

Industry 5.0 highlights the growing need to ensure the adaptability of manufacturing systems around humans. In the context of industrial assembly, the continuous execution of ergonomic assessment is fundamental to promoting a dynamic and safe reconfiguration of workstations. This allows for the accommodation of individual-specific needs, thus contributing to employee well-being and productivity. In practice, however, there is a lack of integrated resources to support operations at this level. This can lead to reduced efficiency due to a mismatch between worker and workstation, risk of injury, and expensive late design modifications. The goal of this research is to provide input for triggering the customization of workstations based on worker-specific parameters, utilizing simulation-based ergonomic assessment as an objective function. A surrogate model was developed to achieve this by combining Digital Human Modelling (DHM) simulation and data-based modelling using supervised machine learning methods. Finally, the proposed framework was applied to an assembly operation case study for validation purposes. Results show that surrogate models can enable proactive ergonomically-oriented customization of workplaces, thus allowing a human-centered design process within operational cycles.