Automated and Systematic Digital Twins Testing for Industrial Processes
Digital twins (DT) of industrial processes have become increasingly important. They aim to provide a digital representation of the physical world to help evaluate, optimize, and predict physical processes and behaviors. Therefore, DT is a vital tool to improve production automation through digitalization and becomes more sophisticated due to rapidly evolving simulation and modeling capabilities, integration of IoT sensors with DT, and high-capacity cloud/edge computing infrastructure. However, the fidelity and reliability of DT software are essential to represent the physical world. This paper shows an automated and systematic test architecture for DT that correlates DT states with real-time sensor data from a production line in the forging industry. Our evaluation shows that the architecture can significantly accelerate the automatic DT testing process and improve its reliability. A systematic online DT testing method can significantly detect the performance shift and continuously improve the DT’s fidelity. The snapshot creation methodology and testing agent architecture can be an inspiration and generally applicable to other industrial processes that use DT for generalizing their automated testing.
Sun 16 AprDisplayed time zone: Dublin change
09:00 - 10:30
|Keynote: Learning-based Testing of Robustness and Time
K: Bernhard Aichernig Graz University of Technology
|Automated and Systematic Digital Twins Testing for Industrial Processes
Yunpeng Ma Karlstads Universitet, Khalil Younis Karlstads Universitet, Bestoun S. Ahmed Karlstad University, Andreas Kassler Karlstad University, Pavel Krakhmalev Karlstad University, Andreas Thore RISE Research Institutes of Sweden, Hans Lindback Bharat Forge Kilsta ABPre-print