Automated Integration of External Data into Digital Twins for Manufacturing Processes

In the era of Industry 4.0, data is becoming increasingly vital for future manufacturing and digital twin applications. Currently, primarily asset-created data is utilized, while the potential benefits of incorporating external data from connected production systems remain untapped. This underutilization is largely due to the complex and time-consuming nature of integrating external data. Automating this process could significantly improve efficiency and unlock valuable insights for more effective decision-making. In this paper, we present the requirements for a system aimed at automating the integration of external data, considering factors such as data heterogeneity and the need to support diverse communication protocols. We further introduce an assistant system that facilitates this automated integration, enhancing manufacturing processes and digital twin applications. Furthermore, we provide a prototypical implementation of the assistant system in the context of a collaborative robot utilizing information from its surroundings obtained through external camera and LiDAR imaging. By addressing the challenges of external data integration, this research contributes to the advancement of data-driven manufacturing and the optimization of digital twin technologies.