Digital Twin of Manufacturing Systems

The digitization of manufacturing systems is at the crux of the next industrial revolutions. The digital representation of the “Physical Twin,” also known as the “Digital Twin,” will help in maintaining the process quality effectively by allowing easy visualization and incorporation of cognitive capability in the system. In this technical report, we tackle two issues regarding the Digital Twin: (1) modeling the Digital Twin by extracting information from the side-channel emissions, and (2) making sure that the Digital Twin is up-to-date (or “alive”). We will first analyze various analog emissions to figure out if they behave as side-channels, informing about the various states of both cyber and physical domains. Then, we will present a dynamic data-driven application system enabled Digital Twin, which is able to check if it is the most up-to-date version of the Physical Twin. Index Terms Digital Twin, Cyber-Physical Systems, Digitization, Additive Manufacturing, Machine Learning, Sensor Fusion, Dynamic Data-Driven Application Systems