A study on the predictive capabilities of digital twins for object transfers in a remanufacturing demonstration environment

Abstract

Remanufacturing processes are characterized by high uncertainty due to the variable conditions of returned cores, which makes automation challenging and necessitates considerable process flexibility. Industry 4.0 methods are often proposed to mitigate this uncertainty, yet real-world demonstrations that validate their effectiveness remain limited. This study addresses this research gap by presenting a flexible, digital-twin driven object transfer system implemented in a remanufacturing demonstration environment. The system under consideration involves an autonomous mobile robot that docks at multiple stationary transfer points to transfer unique starter motor cores without the use of load carriers. Since the object transfer process is probabilistic, virtual models are employed in a physics-simulated environment to predict object-specific pre-transfer states, defined as the state an object before the transfer is executed. The predictive capabilities of the digital twins are evaluated through an extensive experimental study, involving a series of physical and virtual experiments conducted on 37 unique starter motor cores.

The study includes calibration experiments to tune the virtual models, followed by large-scale virtual experiments to estimate the probability of successful transfer for a fixed set of pre-transfer states. A custom method is applied to determine the most promising pre-transfer state for each starter motor core. Final validation results highlight the effectiveness of the approach and indicate that increased modeling efforts reveal inherent limitations in the predictive accuracy of the virtual models. Sources of error, including mass distribution approximations and simulation inaccuracies, are discussed, and directions for future improvements are outlined.