Digital Twins Offer Unmatched Insights For Design Engineers

The value of a new technology is not always obvious from the get-go. Apart from a “killer application” that makes the use case blatantly evident, innovative ideas can sometimes remain just that—ideas. That is, unless a market develops for these ideas or additional innovations come along that make the whole greater than the sum of the individual technologies.

The Internet of Things (IoT) is arguably one such innovation that some might say is a solution in search of a problem. The term IoT was coined by technology pioneer Kevin Ashton back in 1999, though only recently have enough factors—wide availability of inexpensive embedded sensors and proliferation of wireless Internet, for example—coalesced to make the IoT ready for mass adoption. A technology, however, is not necessarily a solution by itself. A subset of the IoT, known as the Industrial Internet of Things (IIoT), has seen some respectable success in the manufacturing market. Still, the cost of implementing IIoT technologies, especially in circumstances where the technology would have to be retrofitted into operational facilities, is not chump change. That said, the IoT and the IIoT have plenty of forward momentum and appear to be on a course for a rendezvous with another innovative concept that has been percolating for over a decade itself—the idea of the Digital Twin.

History of the Digital Twin

The idea of the digital twin is the brainchild of Dr. Michael Grieves and John Vickers (originally used in 2003 at a course at the University of Michigan), who are experts in manufacturing and product lifecycle management (PLM). The basic notion is that, for every physical product, there is a virtual counterpart that can perfectly mimic the physical attributes and dynamic performance of its physical twin. The virtual twin exists in a simulated environment that can be controlled in very exact ways that cannot be easily duplicated in the real world, such as speeding up time so that years of use can be simulated in a fraction of the time. These hyper-accurate models and simulations offer engineers and product designers unmatched insights across the entire product development cycle. Still, digital twins are more than just an evolution of digital models, although their goal is similar: Higher quality products and better product support at less cost and less effort.

From the Factory…

For decades, engineers and designers have heavily relied on software design applications to digitally capture their ideas for physical objects as parametric models. Even today, more complex software allows for the simulation of certain characteristics such as thermal properties or stresses and strains. While use of simulations in product design is nothing new, they have historically relied on relatively small data sets or engineering assumptions when making predictions. Digital twins, however, have access to unfathomably large data sets thanks to the IIoT. Sensors that monitor literally every facet of a product's lifecycle can be measured and fed back into an iterative design-manufacture-observe-improve loop.

… to Your Door

Once a product leaves the factory and is acquired by an end-user, the digital twin can begin to feed off real-world data collected by the onboard sensors. This is where perhaps the concept of the digital twin reaches its full potential. Sensors in the end item itself will track key performance characteristics of the device as it operates in real-world conditions. Comparing actual telemetry against the predictions of the various aspects of the digital twin model yield insights only dreamt of until now. A fortuitous loop results from this level of integration of the physical and virtual. Not only can the digital twin be improved based on real-world data, but future iterations can also be improved based on better understanding of actual data from end users. In some cases, where changes can be made through a firmware update, products that have already been shipped can also benefit from lessons learned through using a digital twin.

In addition to product telemetry, the external operating environment—ambient temperature, relative humidity, and so on—can also be analyzed by onboard sensors, so such factors can be accounted for in simulations. This type of information is invaluable in debugging errant device behavior by providing some operational context that would just not be possible otherwise. For example, if there are two products that are otherwise used and maintained in similar fashion but one keeps failing regularly, it might be of interest to the engineers that the product that is consistently failing is being used at very high elevations. Being able to get that feedback to a company would be invaluable. Not having to rely on a customer to call a help desk and to have that data fed into a digital twin to influence future design iteration is even more incredible. In addition, it would be almost magical if a customer received an email from the company proactively describing steps they could perform to minimize the failures that they might be experiencing without having the customer even place a call or email in the first place.

All of this data—both performance data and external factors—can be communicated in real-time back to the equipment manufacturers to improve the digital twin model and simulation factors. The digital twin could then analyze the operational data and predict failures if it sees data points outside of prescribed tolerances. For example, a circuit board might be seeing higher than expected operating temperatures or motors that are experiencing an unusually high number of stop-start cycles. The digital twin could determine with some level of confidence that the part will fail shortly and take a series of approved actions, such as placing an order with the company responsible for manufacturing the failing part and alerting a technician that they need to brush up on the process for replacing the component. As a result, any downtime is minimal and relatively predictable.

Beyond the obvious use of these rich datasets in maintenance prognostics, digital twins could have profound impacts on the design and engineering of subsequent product iterations. Understanding how a product is actually being used in an objective and data-driven manner will lead to faster development cycles and greatly reduce the time to detect product defects or identify useful tweaks, thus reducing waste by allowing manufacturers to make real-time improvements to the products still coming off the assembly line. This can translate to huge savings by avoiding costly rework.

Digital twins are not limited to assessing tweaks to physical properties of a design. Digital twins can also make it easier to study the impacts that software and firmware revisions have on performance. Various configurations and settings can be rapidly tested and assessed to determine which ones will deliver optimum performance. Firmware updates could then be pushed out seamlessly to all the devices, leveraging the same Internet connection that initially sent the data used to identify improvements.

Furthermore, complex systems such as wind turbine farms could also benefit from the application of digital twins from a system-of-systems perspective. Having multiple instances of a single product, each with their digital twin that communicates with all the other digital twins, means that products can begin to learn from each other. The aggregate knowledge that a digital twin represents can help augment the capabilities of trained human operators in ways to allow them to be more efficient and effective without having to manually collect and crunch the data before making major decisions. Therefore, digital twins allow technology and humans to work together while letting each focus on what the other does best. Technology can continuously monitor, collect data, and conduct analysis. Meanwhile, humans can keep their attention on higher-level work such as exploring implications of various complex courses of actions and making informed decisions.

The Future: More AI, Big Data Interaction

Embedded platforms, with their computational horsepower, energy efficient sensors, and reliable communications hardware, are critical to the collection and dissemination of telemetry data. This data is necessary to make digital twins smart enough so that their function is worthwhile. Then, all that data can be pumped into databases that are rapidly analyzed using Big Data techniques. Throw in the possibilities from a Watson-like Artificial Intelligence (AI) system to analyze and make improvement recommendations, and it’s possible that products could improve over time without any human intervention. The result is the ultimate in technology self-help! Digital twins might very well prove to be the long sought after use case that finally makes the adoption of the IoT mainstream. The implications of a more cost-effective, rapidly moving, and increasingly intelligent product development lifecycle would seem to make any investment well worth it.

For some, the value of digital goes way beyond just parts and products. Some see a future where every aspect of our lives—from our cars and homes to entire cities and even the human body—will be given a digital twin as a way to encourage experimentation and see what tweaks can be made to improve the quality of our lives. The success or failure of all these potential digital twin candidates will come down to the ease in which the lifeblood of a model—the data—can be collected, aggregated, and disseminated. This data and the associated dataflow, the so-called digital thread, will undoubtedly be fed to digital twins via the IIoT. Perhaps the digital twin is the killer app that the IIoT has been waiting for all these years. Or so my digital twin tells me.