  Digital twins (DT) of industrial processes have become increasingly
important. They aim to digitally represent 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 can be generally
applicable to other industrial processes that use DT to generalize their
automated testing.
