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Digital twin refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things device operates and lives throughout its life cycle. The term was coined in 2002 by Michael Grieves at the University of Michigan.
Definitions of digital twin technology used in prior research emphasize two important characteristics:
- Each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart.
- This connection is established by generating real time data using sensors.
The concept of the digital twin can be compared to other concepts such as cross-reality environments or co-spaces and mirror models, which aim to, by and large, synchronise part of the physical world (e.g., an object or place) with its cyber representation (which can be an abstraction of some aspects of the physical world).
Worthy of mention is David Gelernter's book on Mirror Models. Digital twins integrate internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change.
A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system uses sensor data that conveys various aspects of its operating condition from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; from other similar fleets of machines; and from the larger systems and environment in which it may be a part of.
A digital twin also integrates historical data from past machine usage to factor into its digital model. In various industrial sectors, twins are being used to optimize the operation and maintenance of physical assets, systems and manufacturing processes. They are a formative technology for the Industrial Internet of things, where physical objects can live and interact with other machines and people virtually. In the context of the Internet of things, they are also referred as "cyberobjects", or "digital avatars". The digital twin is also a component of the Cyber-physical system concept.
As an exact digital replica of something in the physical world, digital twins are made using Internet of Things (IoT) sensors that gather data from the physical world and send it to machines to reconstruct. By creating a digital twin, insights about how to improve operations, increase efficiency or discover an issue are all possible before it happens to whatever it's duplicating in the real world. The lessons learned from the digital twin can then be applied to the original system with much less risk and increased return on investment.
Digital twin technology was included on Gartner's Top 10 Strategic Technology Trends for 2017 and 2018. Gartner predicted there would be 21 billion connected sensors by 2020, making digital twins possible for billions of things.
In manufacturing, digital twin technology is used to evaluate facilities, clusters and shop floors to increase productivity, enhance inventory management, monitor manufacturing processes, develop plans for downtime, develop real-time scheduling, and offer predictive analytics. The technology also offers solutions for fault or defect in the manufacturing process or the resulting product. This has included the use of digital twin technology to improve component construction in General Electric's wind turbine to generate gains in efficiency through data from its digital twin.
In the additive manufacturing industry, digital twin technology is being used to achieve defect-free production of parts. It is also used for rapid prototyping of components, reducing time-to-market while increasing quality and decreasing costs. Often, a physical model is used with the digital twin and further sensors to detect flaws as they occur in stress and heat testing.
In a study presented at the "Solid Freeform Fabrication Symposium" in 2019, the University of Nebraska-Lincoln stated that combining theoretical simulations alongside in-processor sensor data leads to higher statistical fidelity of detecting process flaws.
Similar to the benefits in manufacturing, digital twins can revolutionize healthcare operations as well as patient care. A digital twin of a patient or organs allows surgeons and health professionals to practice procedures in a simulated environment rather than on a real patient. Sensors the size of bandages can monitor patients and produce digital models that can be monitored by AI and used to improve care.
Digital twin technology helps city planners understand and improve the efficiency of energy consumption as well as many applications that can improve life for its citizens.
It has been implemented in smart city projects in Greece, Moldova, Portugal, Romania and Spain to support air quality monitoring, daily waste collection monitoring, and to assist municipal compliance with ISO 37120 certification. The technology is also being used to support mobile applications for citizens and tourists for real-time information about the city, including points of interest, locations, routing, and ongoing events.
Digital twin is being used in the microelectronics industry to improve the manufacturing integration and analytical capabilities, including predictive maintenance and optimized scheduling. The technology is also being used to validate the integrity of individual devices or the assembly of microprocessors by world militaries.
NASA uses digital twin technology to develop new recommendations for the operation, maintenance, and repair of systems that are not within reach of the engineers or when trying to talk astronauts through repairs or maintenance of systems beyond the ability for engineers to see them. NASA also uses digital twin technology for the development of technology roadmaps and vehicles and aircraft.
The United States Military Department of Defense uses digital twin technology to help validate the integrity of individual devices and the assembly of microprocessors. Part of the process is the Air Force Research Laboratory's aim at gathering data on microprocessor design and manufacturing processes to establish the provenance of semiconductors using digital twin capabilities.
