2026 ASEE Annual Conference & Exposition

The workforce shortage in the manufacturing sector has become a critical challenge, directly impacting manufacturers' ability to meet production demands. Industrial automation and robotics have become key in addressing the workforce shortage by extending and enhancing the capabilities of operators on the shop floor. Therefore, engineers must have the ability to understand and interface with industrial automation and robotics. However, the high cost of equipment, limited lab access, and time constraints present major barriers for hands-on instruction of industrial automation and robotics in undergraduate courses.
One potential opportunity to address these challenges is to leverage Digital Twins. A Digital Twin (DT) is a purpose-driven virtual representation of a physical system which represent components, assets, and/or processes in the system. Creating a DT of a component or system allows its state, data inputs, and actions to be represented and tracked over time. DTs have become an important concept in the manufacturing area and have been used in virtual commissioning and predictive maintenance, among other applications. While DTs are being deployed across different areas, there is a lack of applications in education due to both a lack of defined use cases and proven results.
This work presents the integration of digital twin technology into an undergraduate industrial automation and robotics course, highlighting how DTs can expand access to hands-on learning while reducing hardware reliance. We leverage the ProtoTwin simulation software to create a high-fidelity DT of a modular physical production line composed of Fischertechnik industrial education kits and a 4-axis robotic arm. The DT supports both fully simulated and hardware-in-the-loop configurations, enabling students to design, test, and refine their solutions in a virtual environment before committing to hardware. This flexibility addresses the challenge of limited lab availability and provides students with opportunities to iterate more rapidly and collaboratively, including outside of scheduled class hours.
The course is structured to mirror the ISA-95 automation pyramid, gradually building students’ understanding from the bottom up. We begin with the field level, focusing on sensors, actuators, and input/output modules. The course then moves to machines and robots, where students use PLCs and robotic control to control machines/robots. Finally, at the systems level, students coordinate multiple machines into an automated production line. The DT concept is introduced throughout the course in a sequence of lab activities, first as DTs of robots, and ultimately as DTs of the entire manufacturing system. The student deliverable at the end of our course is a project where groups of students work together to integrate three physical systems to perform an automated multi-step simulated manufacturing process. Limited amounts of hardware and time in the lab reduce students’ ability to test and iterate on their solutions. We demonstrate that independent simulation software with deterministic, timescale-independent physics can serve as be an instructional tool, enabling students to rapidly design and test complex production systems. By using interchangeable hardware and software components to represent processing, inputs, outputs, and the physical plant, students can minimize hardware iterations while still gaining experience with realistic system integration.