A digital twin ecosystem for additive manufacturing using a real-time development platform

Additive manufacturing is often used in rapid prototyping and manufacturing, allowing the creation of lighter, more complex designs that are difficult or too expensive to build using traditional manufacturing methods. This work considers the implementation of a novel digital twin ecosystem that can be used for testing, process monitoring, and remote management of an additive manufacturing-fused deposition modeling machine in a simulated virtual environment. The digital twin ecosystem is comprised of two approaches. One approach is data-driven by an open-source 3D printer web controller application that is used to capture its status and key parameters. The other approach is data-driven by externally mounted sensors to approximate the actual behavior of the 3D printer and achieve accurate synchronization between the physical and virtual 3D printers. We evaluate the sensor-data-driven approach against the web controller approach, which is considered to be the ground truth. We achieve near-real-time synchronization between the physical machine and its digital counterpart and have validated the digital twin in terms of position, temperature, and run duration. Our digital twin ecosystem is cost-efficient, reliable, replicable, and hence can be utilized to provide legacy equipment with digital twin capabilities, collect historical data, and generate analytics.