A digital copy of a living or non-living physical thing is called a digital twin. It mentions a digital model of possible and actual physical assets, processes, people, places, systems, and devices that may be used for many purposes. The digital picture delivers together the elements and the dynamics of how an (IoT) Internet of Things device operates and lives during its life cycle.
Digital twins add Internet of Things, artificial intelligence, machine learning, and software analytics with 3-D network graphs to create living digital replication models which inform and change as their physical equivalents change. A digital twin always studies and informs itself from many bases to signify its close working condition or position. This learning system studies itself by sensor data which takes numerous features of its operating condition. A digital twin likewise mixes historical data from past machine practice to factor into its digital model.
Twins are being used in many industrial sectors to enhance the operation, repairs of physical assets, and industrial processes. They are an influential technology for the (IIoT) Industrial internet of things. There may be physical objects that can live and interrelate with other machines and people virtually. They are also mentioned as “cyber objects”, or “digital avatars” in the environment of the IoT. A digital twin is similarly a component of cyber-physical systems.
There are different definitions of digital twins used in literature. The Gemini Principles (2018) define in the situation of Digital Built Britain a digital twin is “a realistic digital representation of assets, processes or systems in the built or natural environment”.
Types of Digital Twins
The main types of Digital twins are;
- Digital twin prototype (DTP): It comprises the projects, examines, and procedures to understand a physical product. It happens earlier there is a physical product.
- Digital twin instance (DTI): This is the digital twin of every separate example of the product when it is factory-made.
- Digital twin aggregate (DTA): This is the combination of DTIs whose data and information may be used for examination near the physical product and learning.
The exact information controlled in the digital twins is determined by use cases. The digital twin is known as a logical construct which means the real data and statistics can be kept in check in other applications. A digital twin in the factory is frequently a careful part of robotic process automation.
Examples of Digital Twins
- Used to enhance machines is by the maintenance of power generation equipment for instance power generation turbines, jet engines, and locomotives.
- 3D modeling usage to create digital companions for the physical objects.
- Used for monitoring, diagnostics, and prognostics to optimize asset performance and utilization.
- Aircraft engines
- Wind turbines
- Large structures, e.g. offshore platforms, offshore vessels, etc.
- HVAC control systems
- Utilities (electric, gas, water, wastewater networks)
The characteristics of digital twin technology
Digital technologies have definite features which separate them from other technologies. These features, in sequence, have sure costs. Digital twins have the following characteristics.
One of the most characteristics of digital twin technology is its connectivity. The recent development of the Internet of Things (IoT) brings forward numerous new technologies. The event of the Internet of Things also brings forward the event of digital twin technology. This technology displays numerous characteristics which have resemblances with the character of the Internet of Things. The technology allows first and foremost, connectivity amid the physical component and its digital equivalent. By way of definition, earlier this connectivity is made by sensors on the physical product that get data and mix this data over many additional technologies.
Digital twin technology allows augmented connectivity between organizations, products, and customers. For instance, connectivity between partners during a supply chain is often increased by enabling members of this supply chain to see the digital twin of a product or asset. By simply checking the digital twin these partners may then check the status of this product. Also, connectivity with customers is often increased.
Servitization is that the process of organizations that are adding value to their core corporate offerings through services.
Digital twins are frequently further characterized as a digital technology that’s both the significance and an enabler of the homogenization of knowledge. Thanks to the very fact that any sort of information or content can now be stored and transmitted within the same digital form, it is often wont to create a virtual representation of the merchandise (in the shape of a digital twin), thus decoupling the knowledge from its physical form. Therefore, the homogenization of knowledge and therefore the decoupling of the knowledge from its physical artifact, have allowed digital twins to return into existence. However, digital twins also enable increasingly more information on physical products to be stored digitally and become decoupled from the merchandise itself.
As data is increasingly digitized, it is often transmitted, stored, and computed in fast and low-cost ways. Consistent with Moore’s law, computing power will still increase exponentially over the approaching years, while the value of computing decreases significantly. Therefore, this is able to cause lower marginal costs of developing digital twins and make it comparatively less expensive to check, predict, and solve problems on virtual representations instead of testing on physical models and expecting physical products to interrupt before intervening.
Another consequence of the homogenization and decoupling of data is that the user experience converges. One artifact can have multiple new affordances as information from physical objects is digitized. Digital twin technology allows detailed information on a few objects to be shared with a bigger number of agents, unconstrained by physical location or time. In his white book on digital twin technology within the manufacturing industry, Michael Grieves noted the subsequent results of homogenization enabled by digital twins:
In the past, factory managers had their office overlooking the factory in order that they might get a pity what was happening on the factory floor. With the digital twin, not only the factory manager, but everyone related to factory production could have that very same virtual window to not only one factory, but to all or any of the factories across the world.
Re-programmable and smart
As stated above, a digital twin enables a physical product to be reprogrammable in a certain way. The digital twin is additionally reprogrammable in an automatic manner. AI technologies over the sensors on the physical product, and predictive analytics, A consequence of this reprogrammable nature is that the emergence of functionalities. If we take the instance of an engine again, digital twins are often wont to collect data about the performance of the engine and if needed adjust the engine, creating a more modern version of the merchandise. Also, servitization is often seen as a consequence of the reprogrammable nature also. Manufacturers are often liable for observing the digital twin, making adjustments, or reprogramming the digital twin when needed, and that they offer this as an additional service.
Another characteristic that will be observed is that the incontrovertible fact that digital twin technologies leave digital traces. These traces are often employed by engineers for instance, when a machine malfunctions to travel back and check the traces of the digital twin, to diagnose where the matter occurred. These diagnoses can within the future even be employed by the manufacturer of those machines, to enhance their designs in order that these same malfunctions will occur less often within the future.
In the sense of the manufacturing industry, modularity is often described because of the design and customization of products and production modules. By adding modularity to the manufacturing models, manufacturers gain the power to tweak models and machines. Digital twin technology enables manufacturers to trace the machines that are used and see possible areas of improvement within the machines. By using digital twin technology when these machines are made modular, manufacturers may see which components make the machine perform poorly and replace these with better fitting components to enhance the manufacturing process.