What is Digital Twin Technology?
It’s hard not to notice how ubiquitous the term “digital twin” has become on the websites of today’s leading media outlets and technology companies alike. It seems like every other article is singing this “new” technology’s praises and cheering the innovations and wonders it can produce. But for those who don’t work closely with technology or engineering experts, digital twin conjures only a vague image, a hint of an idea without any real substance or context. And when you add all the different ways companies speak about digital twin – is it for healthcare? Aerospace? Manufacturing? Energy? All of the above? – and the layperson can easily get tangled and trapped by the maze of contradictory and complex definitions swirling around.
Digital Twin: An Introduction
At its core, digital twin is a simple concept with a simple definition. In its broadest sense, digital twin technology is the process of using data streams to create a digital representation of a real-world asset to improve collaboration, information access, and decision-making. In other words, through a combination of simulation technology and data (gathered from sensors, historical records, and so on), engineers use software that builds a virtual twin of a physical object or process.
To use an example, it’s easiest to think of a piece of equipment, say, an assembly line robot. This robot’s digital twin would replicate its physical twin’s real-world performance, as gathered from data taken from the robot’s real-life operation. Using digital twins, companies and teams can see how physical objects will perform in certain conditions and under certain stresses. Doing this helps them reduce the reliance on physical prototypes, because where they’d used to build multiple real-life robots to run tests and gather accurate data, now their twin can do it all for them. This saves teams time, money, and reduces waste and material usage.
And in digital twin technology, the data the physical and virtual twins exchange creates a virtuous cycle – that is, the physical object’s data helps teams optimize the twin, and the twin’s data helps teams optimize the physical object. Expanding our example away from an assembly line robot, teams can use digital twin technology for a huge array of applications.
That’s digital twin at its most basic. But like any new technology underpinned by the latest advances in simulation, high-performance computing (HPC), artificial intelligence (AI), and data analytics, there’s more to learn.
Altair offers the most comprehensive, most streamlined digital twin offering on the market. Since we handle every aspect of the digital twin cycle – simulation, data, machine learning, and computing – there’s no need to go through other software vendors to handle data or simulation. Additionally, our open-architecture, vendor-agnostic philosophy means that organizations don’t have to migrate their data or models to a different data or model infrastructure. Lastly, and most importantly, our experts have been doing digital twin for decades and are available 24/7 for support and advice.
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As for the present, we hope to make digital twin more approachable and more flexible, and we hope this article has contributed to that effort. To learn more about Altair’s digital twin offerings and to see our latest digital twin customer stories, testimonials, videos, and case studies, visit https://altair.com/one-total-twin.
