Digital twin
A digital twin is a digital representation of a physical object, system, or process. It is a virtual model of a real-world asset that can be used to simulate and analyze its performance, behavior, and interactions with other systems.
Digital twins can be used in a wide range of applications, such as:
- Manufacturing: Digital twins can be used to simulate and optimize the performance of manufacturing systems, such as assembly lines, to improve efficiency and reduce downtime.
- Industrial Internet of Things (IIoT): Digital twins can be used to monitor and control the performance of industrial systems, such as machinery and equipment, in real-time.
- Buildings and Cities: Digital twins can be used to simulate and optimize the performance of building systems, such as heating, ventilation, and air conditioning (HVAC), and to plan and design smart cities.
- Healthcare: Digital twins can be used to simulate and optimize the performance of medical devices and systems, such as prosthetic limbs and implantable medical devices.
- Automotive: Digital twins can be used to simulate and optimize the performance of automotive systems, such as advanced driver assistance systems (ADAS) and autonomous vehicles.
- Energy: Digital twins can be used to simulate and optimize the performance of energy systems, such as power plants and renewable energy systems.
Overall, digital twins can be used to simulate and optimize the performance of physical systems in a wide range of industries, which can help to improve efficiency, reduce downtime, and increase the lifespan of assets. They can also be used to plan and design new systems, and to train operators and maintenance personnel. By providing a virtual representation of the physical systems, digital twins can also be used to identify potential issues and to test and optimize different scenarios before they are implemented in the real world.
| Digital twin — recommended articles |
| Cyber-physical systems — Smart factory — Predictive maintenance — Internet of things — Machine Learning — Human-machine interaction — Ai in manufacturing — Preventive and predictive maintenance — Quality 4.0 |
References
- Singh, M., Fuenmayor, E., Hinchy, E. P., Qiao, Y., Murray, N., & Devine, D. (2021). Digital twin: Origin to future. Applied System Innovation, 4(2), 36.
