Digital twin-based framework for an efficient execution of CPPS reconfiguration through human–robot collaboration

This paper presents a Digital Twin-based framework to support the reconfiguration process of Cyber–Physical Production Systems (CPPSs) through human–robot collaboration and Industry 5.0 enabling technologies. The proposed approach integrates a Mixed Reality (MR) module into the digital twin architecture to enhance human–machine interaction, data visualisation, and robot programming. It also incorporates Physics-Informed Neural Networks (PINNs), a hybrid methodology that combines machine learning and physical modelling to improve prediction accuracy and physical consistency. A proof of concept implementation of the framework is carried out on the reconfiguration of a real-world production line within a research platform. The communication mechanism between system modules is tested and discussed in detail. Additionally, the use of PINNs for predicting the energy consumption of a mobile robotic system involved in the reconfiguration task is implemented and benchmarked. The mobile robotic system integrates an AMR (Autonomous Mobile Robot) and a Cobot (collaborative robotic arm). Results show that the proposed model outperforms conventional physics-based and data-driven methods, significantly enhancing the predictive capabilities of the digital twin and broadening its applicability beyond the specific use case.