A Digital Twin is a virtual copy of a physical object, process, or system. This advanced technology is essential for simulating, analyzing, and predicting the future behavior of physical counterparts using data and models in a virtual environment. Originally developed by NASA for remote operations and maintenance of space equipment, its use has expanded significantly due to advances in information and communication technologies.
A Digital Twin is a tool that simulates production scenarios of a process or physical asset and represents the results digitally.
The term Digital Twin was introduced by Dr. Michael Grieves in 2002. However, it has gained more relevance in recent years with the development of the Internet of Things (IoT), which enables real-time data collection and transmission—fundamental aspects for the effective creation and management of Digital Twins.
Characteristics of Digital Twins
Digital Twins allow factory managers to make strategic decisions regarding process operation and asset management based on the analysis of simulated scenarios, without risking production or equipment integrity.
A Digital Twin consists of three fundamental levels for successful implementation:
- Sensorization of processes or assets to gather correct inputs (data) to feed the analytical model. These data may already exist in factory control systems and only need to be unified.
- Data analytics and simulation model design to obtain the desired outputs.
- Visualization of simulation results to make fast and accurate decisions.
Types of Digital Twins
Product Digital Twin
Simulates the existence of a product under different conditions, observing its adaptability and making necessary adjustments to ensure proper functioning in future production.
Production Digital Twin
Simulates the interaction of various production processes to verify efficiency. By analyzing all simulated processes, managers can determine which processes need modification and which are working correctly.
Performance Digital Twin
Collects and analyzes data generated continuously by production plants and products in Industry 4.0, enabling informed decision-making.
Applications of Digital Twins
The applications of Digital Twins vary depending on factory needs, but the most common are:
Digital Twin in Production Processes
Allows understanding the impact of operational changes, breakdown rates, or stoppages on key performance indicators (KPIs).
Digital Twin in Logistics Flows
Optimizes intralogistics routes of forklifts or AGVs and simulates process modifications to support decision-making in restructuring or incorporating new elements.
Digital Twin in Physical Assets
Simulates machine behavior under different operating parameters without compromising asset integrity, especially useful for critical plant equipment.