We are excited to share with you the process in making our Digital Twin. We would like to walk you through the journey of this process.

The first step was to get all the data together in one place and make sure it is accessible to everyone involved. We spent some time talking with different users of our data and understanding what their pain points are and what they are looking for. With this information, we were able to understand how we can use a digital twin to support our users better.

We then built prototypes of what we thought a digital twin should look like, but before sharing these with others, we wanted to make sure that it actually works as an idea, so we used them ourselves for a while and did some usability tests with external parties.

With these insights in mind, we started setting up an internal team who can work on creating a proof of concept that can be shared with more people across the company. We have worked with different partners across the company who can provide us with insights into what we need to consider in building a digital twin for the bank.

We are now in the process of building the proof of concept which will be shared with more people across the bank.

To understand the what and why, we need to talk a little bit about digital twins. A digital twin is a visual representation of your physical space. You can think of it like a “digital copy” of your space. It can be used for anything from planning a project, to training new employees, or even just getting an idea of how your business looks from the outside.

Digital twins are usually created using 3D scanning technology, and then merged with BIM data that shows what each element in your space does (e.g., electrical distribution, plumbing etc.). But 3D scanning is both expensive and time consuming! In order to create our digital twin in the most efficient way possible, we decided to use photos instead of scans for our project. We took photos with a regular camera (a Canon Rebel T5i) – taking one every 6 inches along our walk path – and then stitched them together using Autodesk ReCap 360 Pro software.

For simplicity sake, I’m going to say that we just took photos at 6 inch increments all around the room (we didn’t). We did it by taking photos at 6 inch increments along 5 different paths (like spokes on a wheel), which we then stitched together using

In the last few months, we’ve been working on a digital twin of our office. The 3D model represents our office as it is today, but it also allows us to play with different setups and predict how people would behave in them.

Our office is not a fixed space. We have teams that change frequently, new colleagues join us every week, and we often host visitors or do presentations. Some days we work in large groups, others are spent on individual tasks. The space needs to be flexible enough to accommodate all these activities and more importantly: the people doing them.

In this blogpost I’ll talk about the process of making this digital twin and what I learned along the way.

A digital twin is a virtual representation of a real-life object or system. It is a dynamic software model that relies on sensor data to understand how an object or a process is performing. The digital twin technology was first introduced in 2002 for monitoring bridges, but it has since been used for other purposes and brought into the mainstream by NASA.

The recent development in IoT and computing power has made it possible to apply digital twin technology to consumer products. We were able to make our first digital twin using our 3D scanner, a Raspberry Pi, and the cloud service ThingSpeak.

We’ll explain how we did it below.

We made a digital twin of a user interface using a combination of open source software and machine learning. Here’s how we did it.

We started by analyzing the location of the UI elements on a screen, arranged in a tree structure. The nodes in the tree are the individual UI elements, and they’re connected to each other through edges that indicate the hierarchy. The root node is the screen itself, at the top level of hierarchy, while leaf nodes are individual buttons, text fields, and so on.

Armed with this tree structure, we can do two things:

– Calculate how similar screens are to each other by calculating the similarity between their trees

– Transform one screen into another by transforming one tree into another

For example, if we have two different screens that both contain a text field for entering a name, then we can transform one of them into the other by changing its name text field to match that of the first screen.

We’re excited to announce the launch of our new website, which features a digital twin of our conference room.

The digital twin includes an interactive 3D model of our office as well as a 360 virtual tour that you can explore on your desktop computer or mobile device. We think this format will make it easier for visitors to get a feel for our space before visiting in person!

We recently launched the new version of our website and wanted to share some details about how we made it:

How did we make this?

This is a great question! I’m glad you asked 🙂 It took us a while but we finally got everything figured out: here’s how we made the model + code used to generate it (with links at each step!).

The digital twin is a concept where a digital replica of the physical world is created for various use cases, ranging from design and manufacturing to simulation, optimization and control, service and maintenance. In short, the idea is that any model that can be connected to data from a physical product can be built in software.

In this blog post we will describe how we created our digital twin of a roller coaster. We will also share what we learned along the way. Let’s dive into it!

The first step was to select which of our roller coasters to use as a reference, and what data sources were available for it. We decided to use the “Blue Fire Megacoaster” at Europa-Park in Rust, Germany as our base model for the digital twin because it has an intriguing layout, is relatively easy to understand and high quality data is available from the operator.

Step two was finding out where to get data from. The best source turned out to be Gerstlauer Amusement Rides GmbH who are responsible for constructing the ride vehicles (among other things). They sent us PDF documents containing CAD drawings of all mechanical components used in the Blue Fire train including dimensions.