By Jeremy Cook
The digital twin concept is a new way of representing “things.” But how did we get here? And how are we progressing to a full digital twin future in engineering?
If you went to engineering school in the mid-1980s or earlier, you were likely taught how to produce and interpret two-dimensional technical drawings on actual printed paper. By the 1990s and early 2000s, the same sort of 2D drawings could be produced using 2D CAD programs–most notably AutoCAD. While this sped up the drafting process, the real revolution in digitizing designs would come with the proliferation of 3D CAD software like SolidWorks and Pro Engineer.
Solid modeling was a huge leap forward, enabling engineers to more accurately represent object dimensions, and to perform simulations like finite element analysis. It also allowed for more developed integration with CNC equipment, and eventually 3D printing. Now, the integration of digital twin technology is poised to further revolutionize how engineers work on and optimize designs.
Digital Twin Technology in Engineering
A digital twin is a container for information, ideally capturing all relative current and historical data on a real or theoretical thing. Data is captured during manufacturing, and IoT technology is used to further monitor a thing’s state, updating its particular digital twin–i.e. a digital twin instance–appropriately.
While not a simulation in and of itself, digital twin data can be used in a simulation. The thing’s instantaneous characteristics (or even characteristics from the past) can be input into a simulated environment and tested with virtual external inputs to determine how it will react. Engineers can design, test, and refine their designs before physically making anything, potentially with the help of AI.
Digital Twins, AR, and the Metaverse
Once a design is put into the field, IoT feedback can be used to obtain stats on how it is performing. This data, potentially combined with data from other twin instances, can further refine the design based on real-world feedback. This can also help with true predictive maintenance, performed based on a thing’s actual state, not per a set schedule. This can keep unnecessary maintenance from being performed, while largely avoiding reactive maintenance in response to machine malfunctions.
The use of digital twin data can be enhanced with metaverse concepts, along with virtual reality (VR), augmented reality (AR), and extended reality (XR), as appropriate. In the design phase, imagine being able to step into a scene with colleagues in a metaverse environment in order to visualize and improve a thing or scenario. Once a virtual thing is physically built and put in the field, technicians could use an AR headset to overlay code actively running on a machine, temperature data, manuals, and much more, while metaphorically… or literally, turning a wrench to the exact specifications.
Digital Twin Technology and Future Engineering Education
With the proliferation of concepts like digital twins, AI, AR, and the metaverse, one might be tempted to think that the need for engineers will be diminished, or that the basic physics, chemistry, and mathematics education that we take for granted today can be pushed to the side. While concepts described here could allow technicians to work in a greatly expanded role, engineers will still need to understand how digital twins, and their represented things, behave. This will allow them to apply the proper inputs, and to understand what outputs equal a realistic result.
Fundamental engineering knowledge will still be essential, even if you’re able to apply it in a collaborative metaverse environment, and if computers perform the majority of our day-to-day calculations, often at an astronomical scale.
