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Digital Twins: Connecting Real and Virtual Space

The Fourth Industrial Revolution has been all over the news in recent years, transforming how manufacturing and other industries function. It includes many technologies that bridge between the physical and digital worlds, including the Internet of Things (IoT), artificial intelligence, augmented reality, robotics, data analytics and 3D printing. These and other technologies and their applications are rapidly changing the way products and processes are designed, built and used.

ENTER THE DIGITAL TWIN

Computer modeling of components and systems has been around for a long time. So has using sensors for real-time monitoring of equipment and processes. However, the modeling and monitoring have most often been quite separate from each other.

Connectivity between the physical world and the computer models was missing, says Michael Grieves, chief scientist for advanced manufacturing at Florida Institute of Technology, in a white paper. He refers to physical products in real space and virtual products in virtual space. Real space and virtual space, along with the connections for moving data and information between them, embody the concept of a digital twin (Figure 1). The real-world components or systems are reproduced in digital form as a twin, something like a mirror image.

One definition, from IBM, calls a digital twin “a virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning.”

Grieves credits the origin of the term digital twin to John Vickers, principal technologist at the National Aeronautics and Space Administration (NASA), who first used it around the year 2000.

digital twin modelFigure 1. Digital twin model

TWINNING IN THE SPACE PROGRAM

Long before the digital twin terminology existed, however, NASA made use of the twinning or mirroring concept. It had to, because equipment involved in the space program was largely inaccessible to direct observation, analysis or repair while in operation.

The twinning approach paid off during the Apollo 13 moon mission in 1970 when a rupture of one of the oxygen tanks resulted in the command module’s power systems being shut down. Because NASA had prepared a simulator (physical twin) of the spacecraft, engineers could devise and test possible remedies for the problems resulting from the rupture. The solution was to reconfigure the lunar module power system to supply electricity to the command module long enough for the safe return of the crew to Earth.

Now, decades later, NASA has embraced the digital twin. “The ultimate vision for the digital twin is to create, test and build our equipment in a virtual environment,” says Vickers, quoted in Forbes magazine. “Only when we get it to where it performs to our requirements do we physically manufacture it. We then want that physical build to tie back to its digital twin through sensors so that the digital twin contains all the information that we could have by inspecting the physical build.”

TWINS ON THE FACTORY FLOOR

In the white paper, Grieves suggests opportunities in manufacturing for the digital twin approach. Manufacturers commonly create 3D models of products during the development process. The models may be available on the production line, but just as images and data on a screen. The advantage would come from connecting the 3D model with data collected about the physical product, resulting in a virtual (twin) product.

As the physical product proceeds along the production line, automated measurements would be made. At quality control stations, coordinate measuring machines could check critical dimensions. At fabrication or assembly stations, feeds, speeds and forces would be measured. For example, Grieves says, “we can collect the torque readings of every bolt that attaches a fuel pump to an engine in order to ensure that each engine/fuel pump attachment is successfully performed.” All the data would be fed to a unified repository (UR) in real time.

In a digital twin approach, the data collected would be overlaid on the 3D model and any differences highlighted. This would allow alerts in real time and immediate corrections as needed.

HOW TO GET THERE

One way to enable the necessary connection would be to develop a UR to provide two-way connection to link the virtual and physical products together. Both the virtual development tools and the production line measurement tools would populate this repository.

“On the virtual tool side,” Grieves says in the paper, “design and engineering would identify characteristics, such as dimensions, tolerances, torque requirements, hardness measurements, etc., and place a unique tag [for each] in the virtual model that would serve as a data placeholder for the actual physical product.” The tags, including their geometric location and required dimensions or other characteristics, would be collected into the UR.

On the physical side, when the product is released to production, the tags would be incorporated into the manufacturing process at the appropriate step for making the measurements. As measurements are made, they are output to the UR.

The final step is to incorporate this data back into the factory simulation. “Instead of simulating what should be happening in the factory,” Grieves says in the paper, “the application would be replicating what actually was happening at each step in the factory on each product.”

TWINS EVERYWHERE

A digital twin is a “virtual representation of both the elements and the dynamics of how an IOT device operates and lives throughout its lifecycle,” according to an explanatory video by Chris O’Connor, general manager and senior executive leader of the Internet of Things at IBM.

The digital twin approach can go beyond manufacturing or spacecraft systems. From a broader view, a digital twin would be a digital replica of physical assets, processes, people, places, systems and devices, providing both the elements and the dynamics of how devices or systems operate.

More and more digital twin applications are being developed, including digital twins for buildings, motor racing, health care and supply chain and logistics infrastructure.

DIGITAL TWINS EMPOWERING HUMANS

The digital twin approach makes use of data and analysis, but it also supports three powerful human capabilities, Grieves says in the paper.

Conceptualization: Humans take in data and then try to visualize it. With twins, the data can be presented visually in a virtual factory image, for example, showing the actual data trend lines that may reveal a developing problem.

Comparison: People naturally compare the actual result against the desired result and seek to eliminate any gap. A digital twin application can enhance this ability by indicating differences between specified and actual measurements, showing color ranging from green to red, for example, where a problem may lie. This would allow humans to make rapid decisions about where action needs to be taken.

Collaboration: One of the most powerful things humans do, Grieves says, is collaborate with each other, bringing more intelligence, different perspectives and better problem solving and innovation to a problem. The digital twin allows multiple people to view the same situation from anywhere in the world and apply their skills and knowledge collaboratively toward a solution.

While both the virtual and physical aspects of processes have had plenty of data has been available, they have not generally been used together. The digital twin approach can make information about both aspects immediately available in a usable form, potentially improving quality, reducing downtime and promoting greater efficiency.


This email address is being protected from spambots. You need JavaScript enabled to view it. is web editor at VALVE Magazine

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