A digital twin is a virtual model of a physical object, system, or process that is synchronised with reality in real time. In the context of Industry 4.0, digital twins are used to monitor, simulate, and optimise the operation of equipment, production lines, and entire factories. Creating digital twins allows companies to increase efficiency, reduce costs, and minimise the risk of failure by analysing and testing virtual models before making changes in the real world.
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Digital twin
Definition
Basic kinds
- Product twin: A digital model of a physical product that is used to analyse its performance, monitor its efficiency, and optimise it.
- Process twin: A digital model of operational or production processes that enables simulation, monitoring, and optimisation of processes in real time.
- System twin: A virtual equivalent of complex systems, such as production lines or power grids, enabling monitoring and testing of interactions between system components.
Main roles
- Digital twin technology makes it possible to capture the most important information from data sets and to react to it in good time. For this reason, digital twin is used in city management.
- Computer simulations are also used in the manufacture of satellites. In this way, specialists can carry out complex tests quickly on the one hand and manage satellite production on the other.
- The idea of the digital twin is not limited to the device itself, the product, the factory or the city. The development of digital twin technologies in the future will mean the digital mapping of users, using different devices, applications and services, by means of avatars.
Related technologies
Artificial Intelligence
AI algorithms analyse digital twin data to predict failures, optimise operating parameters, and make decisions.
Cloud Computing
The cloud enables large-scale storage and processing of data from the digital twin, providing flexibility and accessibility.
Big Data
Analysing vast amounts of data from the digital twin enables accurate modelling and forecasting of performance as well as identification of potential problems.
Internet of Things
IoT devices deliver data from the real-world environment to a digital twin, enabling accurate real-time mapping of the state of an object or process.
Automation
The digital twin works with automation systems, automatically adjusting operational parameters to optimise processes.
Basic elements
- 3D model: A virtual representation of a physical object that replicates its geometry and behaviour.
- IoT sensors: Devices that collect data from an actual object or process and transmit it to a digital model.
- Data analysis platform: Software that processes data collected from sensors and enables analysis and real-time monitoring.
- Simulations and predictive algorithms: Tools for predicting results and testing different scenarios without interfering with actual resources.
- User interface: A visual environment that allows users to monitor and manage the digital twin.
Mechanism of action
- Creating a digital model: Based on the actual object or process, a digital model is created to represent the geometry and physical and operational properties.
- Data collection: IoT sensors installed on the object collect data on its state and operations, such as temperature, pressure, energy consumption, vibration, and other parameters.
- Data processing and analysis: The data collected by the sensors is processed by algorithms to monitor performance, identify anomalies, and predict failures.
- Simulation and optimisation: The digital twin makes it possible to simulate various scenarios, such as changes in operating conditions, operational parameters, or responses to potential problems, to optimise operations.
- Real-time monitoring: Thanks to the sensor data, the digital twin is constantly updated, enabling the monitoring of the state of the object or process on an ongoing basis and rapid response to changes.
- Updating and refining the model: Based on analysis of the results and new data, the digital model is regularly updated and refined to ensure accuracy and consistency with reality.
- Decision-making and automation: The analysis and simulation of the digital twin can be used to make decisions on automatic process optimisation, predictive maintenance, and modification of operational parameters.