Synchronization evaluation of digital twin for a robotic assembly system using computer vision

Abstract

Traditional production processes are profoundly transforming in the era of Industry 4.0 and smart manufacturing. Smart manufacturing, a core element of Industry 4.0, employs advanced technologies such as automation, robotics, AI, big data analytics, and machine learning to boost productivity and manufacturing performance. Central to this evolution is the Digital Twin (DT), digital replicas of physical assets that blend real-time data with advanced analytics and simulations. Ensuring synchronization between the digital replica and its physical counterpart is crucial for the success of Digital Twins. This paper addresses the challenge of achieving and quantifying synchronization within DTs, focusing on replicating physical system behaviour and measuring deviations or delays. The study delves into the critical aspects of synchronization within digital twin applications, focusing on its implications for a robotic assembly system. The research successfully harnessed YOLOv8 to facilitate real-time event tracking and synchronization characterization, highlighting the potential of computer vision in enhancing synchronization accuracy and, consequently, the efficiency and reliability of manufacturing processes.