Tech Coffee is a series that aims to introduce you to a new technology in less than 20 minutes. In this post, we will get an introduction to digital twins, and some of the terms and technologies needed to create them. So grab a cup of coffee and get started!
There is a lot of discussions and talks around digital twin solutions, where fleets of devices produce a massive amount of data, all communicated to the cloud for analysis and processing. This processed data is then consumed by interfaces such as digital twins, graph solutions, web sites and so on to present the real-time state of a device, or a result of a prediction created by a Machine Learning algorithm.
Let’s first spend two minutes to get a short introduction to what a digital twin really is:
These solutions consists of a large set of systems talking with each other, where each is a technology of its own. To name a few, we have topics like IoT, IoT Edge, Cloud, Artificial Intelligence, Machine Learning, Mixed Reality, Spatial Anchors, all fields deep enough to have its own specialists.
Getting started as well as understanding how all of this fits together can be hard due to this learning curve. Going from a small IoT device, through the cloud, and rendered on a mixed reality device such as HoloLens involves a lot of technological layers.
The following session from Microsoft Builds 2019 goes through some of these concepts, and explains how the world of IoT and the world of Mixed Reality can be brought together.
Why all the 3D?
3D is a powerful asset when it comes to visualizing data. If we get back to real life, we are used to see and work with items and objects from multiple angels, and get a feel for it. When working with a machine, we know how it looks, and how it feels.
Modern hardware such as mobile devices and PC’s can easily render advanced 3D models and environments, and if designed in the correct way, we can start to create brand new interfaces and ways of communicating with software. Suddenly we can bring the real asset into a digital world, where sensors and data are connected to it, so it renders and behaves much like in the real world. Thus creating a digital shadow of a real asset.
Now, if we apply computation and processing through the cloud, and machine learning algorithms to predict when things needs attention, or can give us a real-time production rate, we can start to both handle situations before they happen, and simulate fictional situations that might happen in the real world.