Digital twins are rapidly transforming how companies design, produce and manage their products and processes. As we will see in more detail, a digital twin is a virtual representation of a physical object or system, accurately reflecting its behaviour and characteristics, which offers numerous advantages to companies that use it, with practical applications in various sectors, including manufacturing, healthcare, urban planning and energy.

Let's delve deeper and try to better understand what digital twins are, how they work, what their benefits are for companies and how to create them.

What is a digital twin?

A digital twin is a virtual representation of a physical object or system, designed to reflect its real-life counterpart accurately. This digital replica covers the entire life-cycle of the object and is updated with real-time data, using simulations, machine learning and reasoning to support decision-making.

Digital twins combine multiple types of models and process data from different sources to offer a better approximation of the real object than traditional simulation approaches.

For example, a digital twin of a wind turbine receives data from sensors that monitor energy production, temperature, weather conditions and other vital parameters. This information is processed and applied to the digital twin, allowing simulations to be conducted, performance to be analysed and potential improvements identified.

Effectively, a digital twin acts as a virtual 'mirror' of its physical twin, providing valuable information to improve design, production, use and maintenance.

The different types of digital twins

There are different types of digital twins, which can be classified according to various factors, such as the level of detail, the scope of application and the stage of the product life-cycle to which they relate.

In terms of the level of detail, we find:

• components or parts twins: they represent the basic level and focus on single functioning components, such as a single part of a machine;
• asset twins: reproduce the function of two or more components working together, providing data on the interaction between them;
• system or unit twins: they show how different resources combine to form a whole functional system, providing an overview of their behaviour;
• process twins: they reveal how systems work together to create an entire process, highlighting interdependencies and overall efficiency.

In terms of scope, however, we find:

• product twins: they focus on a single product, simulating its behaviour under different conditions and life-cycle phases;
• production process twins: they simulate production processes, optimising efficiency, quality and sustainability;
• supply chain twins: they replicate the entire supply chain, allowing for optimised logistics, inventory management and collaboration between partners.

Finally, in terms of the product life-cycle, we can divide digital twins into:

• design twins: used during the design phase to explore different design options and optimise product performance;
• production twins: used during the production phase to monitor performance, identify problems and optimise processes;
• service twins: used during the service phase to monitor product performance in the field, predict faults and optimise maintenance activities.

Different types of digital twins can co-exist within a system or process. For example, a vehicle’s digital twin could include component twins for individual vehicle systems, a system twin for the entire vehicle, and a manufacturing process twin for the factory where the car is produced.

How does a digital twin work?

We have seen that a digital twin creates a virtual representation of a physical object or system, such as a single product or production line. This virtual replica is designed to accurately mirror the behaviour of its physical twin in its operating environment.

Let us see how it works, in detail:

• data collection: the digital twin receives data from various sensors that monitor the physical twin in real time. These sensors can measure a wide range of information, such as performance output, environmental conditions and other essential parameters;
• data processing: the data collected from the sensors is then processed and applied to the digital twin. This process may involve the use of technologies such as machine learning and artificial intelligence (AI) to analyse the data and identify meaningful patterns;
• simulation and analysis: thanks to the data obtained in real-time, the digital twin can be used to perform simulations and analyse the behaviour of the physical twin in different scenarios. For example, manufacturers can use a digital twin to simulate production bottlenecks, test different production sequences or assess the impact of design changes;
• feedback and improvement: information obtained from simulations and analyses can then be used to provide feedback to the physical twin and make improvements.

In practice, a digital twin continuously learns and evolves as it receives more data from its physical twin, enabling a perpetual cycle of improvement. This ability to simulate, analyse and improve systems in real-time makes digital twins a powerful tool for manufacturing and other industries.

What is the difference between a digital twin and a simulation?

Although both digital twins and simulations use digital models to replicate the processes of a system, a digital twin is a much richer virtual environment for study.

The main difference lies in the scale and use of real-time data.

• Scale: a simulation generally studies a specific process, whereas a digital twin can perform numerous simulations to study multiple processes.
• Real-time data: traditional simulations do not use real-time data. In contrast, digital twins are designed with a two-way flow of information. Sensors on the physical object send data to the processing system, which updates the digital twin. The processed information is then shared with the original physical object, creating a continuous feedback and update loop.

Access to real-time data and the ability to study multiple processes give digital twins a significant advantage over standard simulations. They can analyse problems from different perspectives and generate more in-depth information to improve products and processes.

What are the advantages of using a digital twin?

The use of a digital twin offers many significant advantages for companies, particularly in the manufacturing industry.

Here are some of the main advantages of digital twins:

• improved Research and Development (R&D): enables more effective design and testing of products, providing a wealth of data on likely performance outcomes. This allows companies to make significant improvements to products before they go into production, reducing the risks and costs associated with design error;
• increased efficiency: as explained, they can be used to monitor and optimise production systems in real-time, to maximise efficiency throughout the entire production process;
• increased sustainability: they can help improve the sustainability of production operations. For example, they can be used to optimise energy consumption, reduce waste and improve resources utilisation;
• improved decision-making processes: digital twins can provide a better understanding of production processes, enabling more informed decisions on how to improve efficiency, reduce costs and improve quality;
• risk reduction: they can also allow new solutions to be tested in a wide range of realistic scenarios, including unusual and extreme operating conditions, without having to resort to expensive physical prototypes. This helps to identify and mitigate risks in advance, avoiding potential problems during production or product use;
• creation of a low-risk development environment: they provide a safe environment to explore different design options without excessive costs sometimes associated with producing and testing physical prototypes. This allows design and engineering teams to be creative and innovative, without the fear of costly mistakes;
• in-depth information on product behaviour: twins allow the state of any part of the system to be monitored at any time, tracking of the complex interactions between product elements. This provides valuable information about product behaviour in the real world, enabling targeted improvements and performance optimisation;
• product improvements based on real-world data: twins can simulate the impact of proposed design changes using data collected from operational products in the field. This allows them to continuously improve the product based on real needs and user behaviour.

In short, it is clear that they are a powerful tool that can offer numerous advantages for companies wishing to improve the efficiency, sustainability and quality of their products and processes.

In which sectors are digital twins used?

Digital twins are finding applications in a wide range of sectors, with a significant impact on production processes.

In particular, they are widely used in various manufacturing sectors, including:

• automotive production: they are used to improve vehicle performance, optimise production processes and test new technologies such as autonomous vehicles;
• aerospace manufacturing: they are essential for the design, production and maintenance of aircraft and other aircraft. They make it possible to simulate flight conditions, optimise designs and predict maintenance requirements;
• railway carriage design: support the design, production and maintenance of train carriages, optimising performance, safety and reliability;
• building construction: they are used to design, construct and operate buildings efficiently and sustainably. They can simulate energy performance, optimise structural designs and simplify construction operations;
• industrial production: they are used to optimise production processes, reduce costs and improve quality. They can simulate workflows, identify bottlenecks and optimise resource utilisation;
• energy production: they are essential for the efficient and sustainable management of power plants, energy networks and energy infrastructure. They can simulate performance, optimise energy production and predict maintenance requirements.

In addition to the manufacturing industry, digital twins are finding applications in several other sectors, including:

• health services: they are used to create digital models of patients, organs and biological systems. This makes it possible to personalise treatments, simulate surgeries and improve the diagnosis and prevention of diseases;
• urban planning: they support the planning and management of cities, making it possible to simulate traffic flows, optimise land use and improve service provision;
• logistics and supply chain management: these are used to optimise logistics operations, improve inventory management and anticipate supply chain disruptions.

Technological advances, such as artificial intelligence and machine learning, will enable digital twins to become even more sophisticated and capable of providing more accurate and useful information.

How do you create a digital twin?

Creating a digital twin is a complex process that requires a deep understanding of the physical system you wish to replicate.

To simplify, the key steps involved are the following.

• definition of objectives and scope: the first step is to clearly define the objectives you want to achieve with the digital twin. This involves determining which aspects of the physical system you wish to replicate and what information you want to obtain from the digital twin;
• data collection: the digital twin needs a large amount of data to accurately represent its physical twin. This data can come from a variety of sources, including the following:
  • sensors: sensors installed on the physical twin can collect real-time data on various parameters, such as temperature, pressure, vibration and so on;
  • historical data: historical data on the performance of the physical twin, such as maintenance logs and production data, can provide valuable information on system behaviour;
  • design documentation: design documentation, such as CAD models and technical specifications, can be used to create the geometric and structural model of the digital twin.
• digital model creation: the collected data are used to create a digital model of the physical system. This model can include several types of models, including the following:
  • geometric models: they represent the shape and size of the physical twin;
  • physical models: they simulate the physical behaviour of the physical twin, such as its response to different forces and environmental conditions;
  • behavioural models: they represent the behaviour of the physical twin in its operational environment, such as its production process or life-cycle.
• data and model integration: data and models are integrated into a digital platform that enables visualisation, analysis and interaction with the digital twin. This platform can use technologies such as simulation, machine learning and artificial intelligence to process the data and generate useful information;
• validation and calibration: the digital twin is validated and calibrated by comparing its predictions with real-world data. This iterative process allows the accuracy and reliability of the digital twin to be improved.

The complexity of the digital twin will depend on the objectives and scope of the project. Some digital twins may be relatively simple, while others may be extremely detailed and sophisticated, replicating the entire life-cycle of the physical twin.

The choice of tools and technologies for creating the digital twin will depend on the specific needs of the project. Simulation software, data analysis platforms and visualisation tools can be used to create and manage digital twins.

The creation of a digital twin is always an ongoing process. As more data is collected and the physical twin evolves, it must be updated and improved to maintain its accuracy and relevance.