How Digital Twins Are Constructed Through 3D Scanning

Digital twins are among the most exciting technologies for businesses today.

These “twins” are virtual representations of real-world objects, systems, or processes that update in real-time as their physical counterparts change.

Having these virtual models make it easier to notice potential issues or make informed decisions.

Despite this technology being fairly new, 69% of organizations today already use it — and many more expect to adopt it within the next few years.

Widespread adoption has many benefits, but you must understand how these models work before you can use them effectively. Here’s a closer look at how 3D scanning constructs digital twins.

The first step to creating a 3D digital twin is to gather data on what it’s supposed to represent.

On a large, company-wide scale, that means using Internet of Things (IoT) sensors to collect information about different workflows.

For smaller twins representing specific objects, that means 3D scanning.

You could record all the data you need for a digital twin manually, but manual measurements are often inaccurate and inefficient.

The better solution is to use 3D scanning devices to automatically record thousands of data points about an object’s size, dimensions, movement, materials, and more.

In some cases, teams may have to manually fill in data gaps that 3D scanners can’t record.

For example, they may want their virtual copies to include information like different parts’ serial numbers or which employee is responsible for maintaining it at various times.

Putting this data into digital twin software will make these digital models more practical.

Once teams have all the data they need from 3D scans, they can put it into digital twin software.

Some of these programs connect to the 3D scanners to automatically create a model that workers can then manually adjust as necessary.

In others, employees may have to import data from their sources to the program.

Generally speaking, automated systems that automatically create twins from 3D scans are the better option.

Manual data entry error rates can reach almost 4% in some industries — that small figure makes a big difference when making expensive decisions based on this data.

Some businesses may also want to split the resulting 3D digital twin into separate models.

Manufacturers could break a model of a machine into its specific, individually labeled parts to make it easier to track maintenance problems.

On a larger scale, businesses could take a model of their supply chain and assign different parts to the different companies they represent.

At this stage, businesses have a complete digital twin. However, that’s not the end of their data collection or 3D scanning.

One of the most helpful features of these technologies is that they update in real-time, so teams must connect their digital twin software to other systems for ongoing data monitoring.

More often than not, this means connecting IoT sensors to the digital twin.

As these sensors collect new data about the real-world object’s conditions or performance, they’ll send it to the digital twin software, which will automatically update the virtual model accordingly.

If employees are using a system where they must manually input data, they must regularly perform new 3D scans to keep the twin up-to-date.

This process is slower but may be less expensive than using an extensive IoT network.

Digital twins have the potential to disrupt industry after industry, and it all starts with 3D scanning. As futuristic as it may sound, this technology is ready to implement today.

Once 3D scanning lays the groundwork, businesses can apply digital twins to a wide scope of projects and processes.

They could use them to predict equipment failures, manage supply chain operations, refine product designs, and more.

As this technology improves, new possibilities will emerge, too.