A FRAMEWORK TO INVESTIGATE KEY CHARACTERISTICS OF DIGITAL TWINS AND THEIR IMPACT ON PERFORMANCE
The modern world of manufacturing is in the middle of an industrial revolution with the digital and physical worlds integrating through cyber-physical systems. Through a virtual model that is able to communicate with its physical system known as the Digital Twin, catered decisions can be made based on the current state of the system. The digital twin presents immense opportunities and challenges as there is a greater need to understand how these new technologies work together.
This thesis is an experimental investigation of the characteristics of the essential components of the Digital Twin. A Digital Twin Framework is developed to explore the impacts of model accuracy and update frequency on the system’s performance measure. A simple inventory management system and a more complex manufacturing plant is modeled through the framework providing a method to study the interactions of the physical and digital systems with empirical data.
As the decision policies are affected by the state changes in the system, designing the Digital Twin must account for the direct and indirect impact of its components.
Furthermore, we show the importance of communication and information exchange between the Digital Twin and its physical system. A key characteristic for developing and applying a digital twin is to monitor the update frequency and its impact on performance. Through the study there are implications of optimal combinations of the digital twin components and how the physical system responds. There are also limits to how effective the Digital Twin can be in certain instances and is an area of research that needs further investigation.
The goal of this work is to help practitioners and researchers implement and use the Digital Twin more effectively. Better understanding the interactions of the model components will help guide designing Digital Twins to be more effective as they become an integral part of the future of manufacturing.
- Doctor of Philosophy
- Industrial Engineering
- West Lafayette