Date of Award

Fall 2022

Document Type

Open Access Thesis


Computer Science and Engineering

First Advisor

Ramy Harik


Digital Twins of manufacturing systems are an emerging tool for improvement, optimization, and monitoring of physical manufacturing systems. The idea of representing physical systems with digital depictions has been prevalent since the early days of Computer Aided Design (CAD), but recently the concept of a Digital Twin has been expanded to encapsulate more than just a digital model of a design. Instead, the Digital Twin offers a unique, comprehensive, and real-time toolset for analysis and improvement of physical systems. These tools can be used to improve system efficiency, perform “what-if” analyses, virtually commission manufacturing equipment, analyze system performance in real time, among many other capabilities. To validate these capabilities and assess their efficacy in a manufacturing setting, the creation, use, and analysis of Digital Twin (DT) systems was recorded and assessed across two manufacturing use cases. These use cases were both physical systems where the DT models could be tested and validated against real manufacturing equipment performance. DT models were created for both the Future Factories robotics lab and the Automated Fiber Placement Lab at the University of South Carolina’s McNair Aerospace Research Center. The accuracy and utility of each DT model was assessed through physical experiments in each lab space. Advanced technologies such as Edge Computing, visual motion capture, virtual commissioning, and offline programming were utilized in the context of the DT models to enhance the DT environment and improve system accuracy. The assessment of these various DT techniques in the two labs demonstrated the utility and accuracy of a DT of manufacturing across both use cases. Significant improvements were observed across the manufacturing design lifecycle, including design, programming, commissioning, and production. Widespread use of DT systems in manufacturing can drastically improve manufacturing efficiency, safety, and time to production, and it is expected that these systems will see continued development and use in the future.