Dassault Systèmes has sponsored this post.
By now, you’ve probably come across the term digital twin. It’s one of those terms that everybody loves to use but perhaps nobody totally understands. The one thing that everybody can agree on is that digital twins are poised to revolutionize product development, improving engineering efficiency and customer experience alike.
So, what is a digital twin? We could go into a lot of detail on the topic, but let’s stay out of the weeds for now and simply crack open a nutshell.
Take an object that exists in the physical world, such as a car. Now equip it with sundry sensors of your choosing, and send that data off to the cloud. There, that data meets up with the car’s 3D model, which incorporates its geometry, mechanical properties, electrical and electronic systems, simulated performance data, and everything else there is to know about the car. The real-world data interacts with the 3D model and updates it in real time. What you’re left with is a mirror of the car made entirely of bits: a digital twin.
What Can a Digital Twin Do for You?
The advantages of digital twins are not hard to see. To stick with the car example, a digital twin could predict exactly when a part on the car will fail, based on real-world data. The twin could tell you that the car’s brakes needed to be replaced before a mechanic so much as opened the garage door. Similarly, the digital twin could serve as a diagnostic tool. Car broke down? Check the digital twin and see what went wrong without getting grease all over your hands.
The digital twin could also reveal insight into optimizing the car’s performance. Simulations could reveal that you’d maximize fuel efficiency by driving an alternate route to work, or extend the life of your vehicle’s tires by rotating them just so. And that’s all with a human operator still on board—the utility of digital twins for autonomous vehicles is greater yet. Such twins could simulate a drive on a virtual road—no real-world test required. In fact, digital twins are already being used for this purpose today—if they weren’t, physical autonomous vehicles would have to drive an estimated 14 billion kilometers to validate their performance.
In its loftiest form, a true digital twin is not possible today. There’s simply so many variables to account for that we don’t have enough sensors, computational power or knowledge to realize an identical digital twin. Thus, any attempts to do so could be both costly and risky. Does this mean that digital twins are only for large engineering firms with cash to burn? Are digital twins feasible for small and medium engineering companies?
No, and yes. We don’t need the ideal version of a digital twin to realize its benefits. Some would argue that even attempting to create an ideal digital twin is a waste of time and resources. Why not just focus on those few key aspects of a digital twin that provide the most benefit?
Digital Twins and Digital Threads
Today, computer-aided design software and simulation are so heavily used that, by the time a product is ready to ship, it already exists as a detailed digital model. The very act of developing a product gets you halfway to a digital twin. And the more cohesive your development environment—the more integrated your MCAD, ECAD, CAE, CAM, PDM and PLM tools—the closer you are to having a useful digital twin.
That’s the philosophy behind Dassault Systèmes’ concept of digital twins, which the company calls 3DEXPERIENCE twins. The name comes from Dassault Systèmes’ 3DEXPERIENCE cloud platform, which integrates product data through a range of unified design products like CATIA for mechanical and system design and SIMULIA for finite element analysis and simulation.
The full end-to-end, multidisciplinary integration of product design data has been called the digital thread, and it’s a key enabler of digital twins. Organizations that want to explore digital twins should evaluate their design process and work to tie it together with the digital thread from requirements to physical definition.
“To unify and understand the enormous and diverse information about the 3DEXPERIENCE twin, innovators have to overcome traditional, siloed-expert thinking,” said Olivier Ribet, vice president of High Tech Industry at Dassault Systèmes. “Of course, you need the capabilities to scientifically and physically simulate all the pieces working together as intended. But engineers also need methods and tools to foster a social dimension to their structured, physical and procedural information. It is about people, which has a social component that often requires coaching and change management to enable breakthrough thinking and open innovation.”
Building a Viable Digital Twin
To prove its value, a digital twin must enable a system-level view into a product or process. A model that can simulate only mechanical performance is one thing, but a model that can simulate how all aspects of a system fit together—mechanical, electrical, aerodynamics, control systems, everything—is much more powerful. 3DEXPERIENCE twins, for example, can simulate these interactions with Dassault Systèmes’ DYMOLA systems engineering software, based on the Modelica modeling language.
This is a paradigm known as model-based systems engineering (MBSE), which exchanges engineering discipline model information rather than design documents as the primary form of communication in product design.
“In order to understand the actual customer experience, and how innovations impact that experience, the digital twin needs to be able to simulate the plant, product or service at the system level, where you can efficiently manage its behavioral aspects that are driven by hardware, software and content-enabled functions,” Ribet said.
By targeting the system level, even an imperfect, partial, or incomplete digital twin can have many benefits. For instance, you could virtually simulate how changes to your product would likely affect real users. Imagine stepping into a virtual reality environment and seeing your digital twin in context. You could simulate that new firmware update and see exactly how it might impact users. Or you could apply that new material you’re considering and see how the thermal properties of your system would change.
Over time, as real-world data is streamed to your digital twin, you could gain insight into the weak points of your design and learn how to improve it. By doing so, product planning, engineering, manufacturing, marketing and servicing teams can collaboratively and continuously run what if scenarios, modeling and simulating the product experience.
Digital Twins on the Cloud
We’ve only scratched the surface of how digital twins can disrupt product development. The better digital twins become, the more utility they’ll have. For example, one day digital twins may be sufficient enough for virtual certification or commissioning. For a deeper dive, check out What is the Digital Twin and Why Should Simulation and IoT Experts Care? or Digital Twins: Where Are We Now?
As we’ve seen, digital twins don’t have to be perfect to be useful. Even a rudimentary digital twin that can represent the system level of your product can offer insight into its real-world performance.
Digital twins need more than just geometric data in the model, but a barrier for SMBs is the cost of acquiring CAD add-ons that they may not need everyday, but which could benefit their digital twin. The evolution of CAD products to the cloud enables on-demand use of these add-ons, as well as the ability to share the model with distant qualified experts.
As you augment your model, whether through a tighter digital thread or greater access to real-time sensor information, each step will bring you new benefits as you come closer to the ultimate digital twin. To learn more about 3DEXPERIENCE twins, read the digital twins white paper from Dassault Systèmes.