The digital twin market is expected to grow from $7.5 billion in 2022 to $96 billion by 2029 at CAGR of 40.6%
The top 4 industries adopting digital twins are Aerospace & Defense, Automotive, Manufacturing and Healthcare
75 percent of organizations surveyed by Gartner in 2019 were already using Digital Twins or planned to within a year
A digital twin is a virtual representation of a real-world product or system and is identical to the original in its behavior.
Imagine a 3D, virtual model of a high-speed pump employed in an oil rig, miles off the coast. The virtual rotors on the 3D model would move as fast as the rotors on the real pump working hundreds of miles away. The outer surface of the 3D model starts to turn red as the real pump's outer surface heats up. Or if the flow of extracted crude slows down in one of the conduits, the 3D model immediately reflects that slow down too.
Further, imagine that a 3D model operator, by observing the model, figures out that the increasing heat on the top right surface of the pump is due to degraded coolant viscosity. At a click of a button on the virtual model, the old coolant starts to flush, and is replaced by new flow parallelly - and these events reflect on the actual pump at the same time.
And if during operations the actual pump shifted its position by 3 cm, the digital twin would also shift by the same amount.
That is what a digital twin is - a virtual representation that not just reflects the internal and external conditions of an actual product but can also change the behavior of the actual product connected to it over the internet.
Uses of a digital twin range from safety, maintenance, training, and downtime management.
They are useful in optimizing product performance, understanding the in-service life of a product, performing predictive maintenance, and extending a product’s remaining useful life (RUL).
After a malfunction, machines deployed in remote locations need to wait longer while engineers arrive and inspect. If on inspection they find that some spare parts will be needed to fix the machine, their transit adds to the downtime costs of the organization. For machines with digital twins, engineers can identify issues remotely, or even assist local engineers to fix them thereby reducing operational losses.
Training, safety, and expertise are enhanced by training manpower on digital twins simulating the real machines under real working conditions.
Smart cities are increasingly using digital twins to plan city layouts. Automotive, aerospace and defense are using digital twins to monitor and calibrate remote equipment in real time. The Healthcare industry is aggressively pursuing digital twins to monitor healthcare equipment and patient response. Here is a most interesting video that highlights how human digital twins can enhance personal healthcare.
A digital twin setup uses a combination of hardware and software tools. Various IoT sensors dot the machine and send real-time data of its working conditions and internal environment to connected cloud servers, where all that data is analyzed using Artificial Intelligence.
Augmented Reality and Virtual Reality techniques are used to render 3D models of the model, overlaid with real-life working conditions and with integrated controls to make changes in the real machine if required.
Special hardware is needed to create 3D models of the machines and their spatial surroundings to be fed into their VR counterparts.
Most industries stand to gain a lot through the introduction of digital twin technologies in their systems. The introduction will be initially cost-intensive but is capable of generating multiple times RoI by optimizing asset utilization and optimizing planning, safety and responsiveness.
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