Digital twins are virtual models that accurately reflect a physical object, process, or system. Companies use them to simulate and study the behavior of a digital product, allowing for the efficient adaptation of solutions to the real product.
A digital twin is a virtual representation of an object that incorporates real-time data captured through sensors or data analysis technologies. It is a technology that combines the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning, and data analysis.
Digital twins have gained prominence in the Fourth Industrial Revolution and are in high demand for future professions. These virtual replicas allow for the analysis of real processes, experimentation with existing physical objects, and the creation of hypothetical scenarios to predict behavior. A digital twin can be designed for anything, from an airplane engine to a wind farm or an entire city.
The concept of digital twins was first introduced in 1991 in the book "Mirror Worlds" by computer scientist David Gelernter. However, it was not until 2002 that Michael Grieves, from the Florida Institute of Technology, applied this concept to manufacturing. The development was presented at the Product Lifecycle Management (PLM) conference hosted by the Society of Manufacturing Engineers. A few years later, in 2010, NASA used this technology to create digital simulations of capsules and spacecraft, referring to them as digital twins in a roadmap report.
How Do Digital Twins Work?
It all begins with a specialist, typically an expert in data science, applied mathematics, or engineering. Their job is to study the object that needs duplication through a mathematical model that simulates the physical original.
To accomplish this, companies employ digital twin technology, which utilizes software to collect real-world data and create virtual simulations capable of predicting the performance of a product, process, or system. This software integrates various technologies to analyze massive amounts of data and explore potential scenarios.
Therefore, digital twins consist primarily of three parts: the physical product, the virtual product, and the connections generated between them. The final result of the constructed digital version depends on the amount of data used to create and update it.
Advantages of Digital Twins
The main advantages of digital twin technology are:
- Real-time Monitoring: Enables remote and real-time monitoring of a process without the need for physical presence.
- Predictive Maintenance: Allows for early problem detection and resolution, enabling improvements in production levels and reducing maintenance costs.
- Fault Detection and Risk Mitigation: Utilizes processed information and data to detect potential errors in the prototype, enabling the development of solutions to prevent disruptions in the real twin system.
- Resource Optimization: Optimizes work processes, improves the production chain, reduces investment, and saves costs.
Characteristics of Digital Twin Technology
Digital twins are made possible in the Industry 4.0 era due to several characteristics:
- Connectivity: The technology relies on the Internet of Things (IoT), which allows physical devices to connect and communicate with each other.
- Homogenization: To create a digital representation of a product, process, or system, they combine various sources of information into a single software program. This concept is known as homogenization.
- Reprogrammable and Intelligent: Companies can reprogram digital twins, even automatically, through sensors in the physical product, Artificial Intelligence (AI) technology, and predictive analysis.
- This allows for gathering information to improve the functioning of the real twin, i.e., the physical prototype.
- Digital Traces: Digital twin technology leaves digital traces that technical engineers can use to identify any problems. This way, companies can localize errors and seek solutions to prevent their recurrence.
- Modularity: Both the virtual and physical twins can be divided into multiple parts or layers. This facilitates manufacturing and monitoring, as it allows for identifying which components are causing malfunctions, enabling their repair and overall process improvement.
Types of Digital Twins
There are three types of digital twins, based on the production phase of the product:
- Digital Twin Prototype (DTP): Created when the product does not yet exist. They develop a virtual prototype to gain detailed insights into how the physical product would behave.
- Digital Twin Instance (DTI): Created when the physical product already exists. They use the digital twin to conduct tests in various environments where it could be implemented.
- Digital Twin Aggregates (DTA): Uses previously collected information and data from a real product to predict and identify its capabilities.
Moreover, digital twins can be classified based on what they simulate:
- Process Digital Twins: Simulate end-to-end production processes to develop more efficient methodologies. They predict how processes will function and identify potential failures, allowing companies to implement solutions and save costs during physical execution.
- System Digital Twins: Enable the digital representation of production lines to optimize performance and predict and prevent necessary maintenance. They facilitate the design of more efficient manufacturing processes, detect potential issues, and save costs.
Applications of Digital Twins
Here are some examples of digital twins in different sectors:
- Energy Sector: Digital twins can simulate the management of energy resources efficiently and plan for different scenarios. They also enable modeling of real-time performance for energy plants.
- Healthcare Sector: Companies can use digital twins in product and equipment forecasting, comparing patient records to identify patterns, and reducing risks in various medical and surgical procedures.
- Automotive Sector: This technology is highly useful for improving individual parts, entire vehicles, production lines, and factories. It can apply throughout the entire process, from design to construction and storage.
- Logistics Sector: Digital twins can simulate the management of container fleets, monitor shipments, and design large-scale logistics systems.
- Industrial Sector: They can streamline the production chain of any product, optimize processes to avoid errors, and reduce manufacturing times and costs.
Current Example of Digital Twins
Repsol, in collaboration with OVS Group, has designed a digital twin for Automated Production Management (APM) and Integrated Flow Models (IFM) to improve asset efficiency and optimize production.
The primary goal of APM workflows is to "enhance operational efficiency and daily production in an integrated and standardized environment."
Repsol combines workflow information with its various data sources to create a centralized tool. This tool enables operators and engineers to quickly identify, classify, and track optimization opportunities.
In conclusion, digital twins are virtual models that accurately replicate physical objects, processes, or systems. They provide real-time monitoring, predictive maintenance, risk detection, and resource optimization. This technology operates through connectivity, homogenization of data, reprogrammability, digital traces, and modularity. Digital twins find applications in various sectors, including energy, healthcare, automotive, logistics, and industry. They are transforming industries and enabling more efficient and effective operations.
