“A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help decision-making.”


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In plain English, this just means creating a highly complex virtual model that is the exact counterpart (or twin) of a physical thing. The ‘thing’ could be a car, a building, a bridge, or a jet engine. Connected sensors on the physical asset collect data that can be mapped onto the virtual model. Anyone looking at the digital twin can now see crucial information about how the physical thing is doing out there in the real world.

Digital twins let us understand the present and predict the future
What this means is that a digital twin is a vital tool to help engineers and operators understand not only how products are performing, but how they will perform in the future. Analysis of the data from the connected sensors, combined with other sources of information, allows us to make these predictions.

Ready for even more information? Read the IBM overview: What is Digital Twin?

With this information, organizations can learn more, faster. They can also break down old boundaries surrounding product innovation, complex lifecycles, and value creation.

Digital twins help manufacturers and engineers accomplish a great deal, like:

Visualizing products in use, by real users, in real-time
Building a digital thread, connecting disparate systems and promoting traceability
Refining assumptions with predictive analytics
Troubleshooting far away equipment
Managing complexities and linkage within systems-of-systems
Let’s look at some of these in more detail.

Use cases for digital twin: an engineer’s point of view
Let’s look at an example of digital twins in action. And since the primary users of digital twins are engineers, let’s use their perspective.

An engineer’s job is to design and test products – whether cars, jet engines, tunnels or household items – with their complete lifecycle in view. In other words, they need to ensure that the product they are designing is suitable for the purpose, can cope with wear and tear, and will respond well to the environment in which it will be used.

Creating real-world scenarios, virtually
An engineer testing a car braking system, for example, would run a computer simulation to understand how the system would perform in various real-world scenarios. This method has the advantage of being a lot quicker and cheaper than building multiple physical cars to test. But there are still some shortcomings.

First, computer simulations like the one described above are limited to current real-world events and environments. They can’t predict how the car will react to future scenarios and changing circumstances. Second, modern braking systems are more than mechanics and electrics. They’re also comprised of millions of lines of code.

This is where the digital twin and the IoT come in. A digital twin uses data from connected sensors to tell the story of an asset all the way through its life cycle. From testing to use in the real world. With IoT data, we can measure specific indicators of asset health and performance, like temperature and humidity, for example. By incorporating this data into the virtual model, or the digital twin, engineers have a full view into how the car is performing, through real-time feedback from the vehicle itself. For more on the engineering POV, read the customer case study: Ushering in Industry 4.0 with an agile approach to systems engineering.

In the video we are going to discuss the following topics-

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4. digital twin tutorial
5. digital twin manufacturing examples
6. what is a digital twin
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