Digital Twins, LiDAR Scanning, and AI: A Powerful Combination for Industry 4.0

A digital twin is a virtual representation of objects or processes in the real world, providing a risk-free environment in which to simulate changes to its physical counterpart and provide monitoring and predictive capabilities. A digital twin can be used for myriad purposes in fields as varied as construction, manufacturing, transportation, retail, engineering, mining and healthcare.  A digital twin:

• Can serve as a prototype for engineers tweaking a car design.
• Can help monitor equipment and predict failure.
• Can extend the life expectancy of expensive equipment by planning maintenance and learning to spot performance abnormalities before they become catastrophes.
• Can monitor and optimize air quality and temperature for occupants of a building.
• Can provide a real-time picture of construction progress on a building site.
• Can optimize processes on the factory floor, the supply chain, and more.
• Can offer safer risk assessment for various scenarios.
• Can allow new employees to train on equipment virtually.
• Can integrate unconnected systems.

As the cliche says, the possibilities are limited only by imagination.

Digital Twins Grow Up

Though some date digital twins back only a couple of decades, NASA pioneered the idea of twinning back in the 1960s when they mimicked conditions on spacecraft at their earthbound space centers. The idea proved to be lifesaving.

In 1970, when things went south for the Apollo 13 mission after the explosion of its oxygen tanks, NASA had a digital twin of the craft on which engineers could audition possible solutions to get the astronauts home safely. Without its twin, Apollo 13 would likely not have had a happy return to Earth.

A few decades later, LiDAR scanning began being used to collect data for virtual digital twins. Those early iterations had plenty of applications, especially for the manufacturing, mining, and construction sectors.

More recently, however, LiDAR scanning has been combined with machine learning to produce highly sophisticated virtual digital twins that blow any predecessor out of the water.

The Marriage of Digital Twins and AI

Using AI enabled digital twins, we can do so much more than just simulate actions, though they excel at that. A digital twin should not be confused with a simulator, which remains immutable until someone changes its specifications. Modern digital twins operate in real time, fed by data sensors affixed to their real world counterparts. As such, they can literally learn.

AI constantly takes in real-time sensor data, adding it to its mass of historical data to become” smarter.” Smarter in the case of a digital twin translates to improved predictive capabilities, meaning they become better and better at offering alternative solutions to problems, informing decision making, optimizing processes, and recognizing abnormalities.

As a result, AI algorithms and machine learning enable digital twins to perform critical functions for industries. Imagine being able to test an engine design without first having to build it or maximizing production by changing the layout of your factory floor based on a solution provided by a digital twin.

Darling Geomatics and the Future of Digital Twins

AI is still in its infancy. We’ve only begun to scratch the surface when it comes to the full range of possibilities for AI-powered digital twins. The future could see digital twins doing things like solving traffic snarls for cities, improving sustainability for supply chains, and further reducing the time it takes for products to go from concept to market.

Wherever the future of digital twin technology takes us, Darling Geomatics will be at the forefront, bringing our clients cutting edge technology, innovative solutions, and expertise born of years in geospatial engineering.