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Exploring the Digital Twins: industrial innovation that transforms reality.


Digital twins are a virtual replica of a real-world object, system or process, spanning its life cycle. These twins often are continuously updated with real-time data, or through simulation, machine learning and reasoning. The key to their effectiveness lies in the constant connectivity and synchronization between the digital model and the physical one.

This bi-directional approach enables real-time data collection, giving companies the power to make informed decisions. From optimizing product performance to performing predictive maintenance, digital twins provide detailed and valuable information to better understand the behaviour of the physical twin, improve its efficiency and extend product life.


How can Digital Twins be applied in industry?

Intelligent manufacturing: In the manufacturing process, Digital Twins allow operations to be simulated and optimized before they physically occur, reducing costs and improving efficiency. During the process, data from different parameters can feed the Digital Twins to detect or predict deviations by means of real time simulation or reduced order models, and sometimes is possible to act on the process “on the fly” to get a good result.

Predictive maintenance: By constantly monitoring the condition of equipment in the real world, Digital Twins enable the early identification of problems and the implementation of predictive maintenance strategies, extending or shortening the planned maintenance times.

Design and prototyping: They facilitate the creation and evaluation of digital prototypes, shortening development cycles and improving design quality.

Logistics and supply chain: Optimize inventory management, product tracking and logistics planning, improving visibility and efficiency.


Example of the use of Digital Twins

The use of Digital Twins of physical objects  benefits of integrating sensors into the objects in key areas of its operation. These sensors collect data on various aspects of operation, such as energy production, temperature or weather conditions. This data is transmitted to a processing system and applied to the corresponding digital copy. This approach enables detailed, real-time monitoring, providing valuable information to optimize performance and perform predictive analytics based on the conditions of the physical twin.


Some advantages and challenges:

  • Digital twins provide real-time analytics, improving operational efficiency and reducing downtime.
  • They provide a solid foundation for strategic decision making based on real data.
  • However, their adoption often involves significant investments in technology and processes, facing challenges related to cybersecurity and data privacy.


How it is expected to evolve:

As technology advances, Digital Twins are expected to evolve to address even more complex challenges. Their integration into Industry 4.0 promises a revolution in the way we interact with the physical world, driving innovation and efficiency.


In short, we are at the dawn of an era in which virtuality and reality merge to drive innovation and efficiency in unprecedented ways, and at IDAERO we work every day to keep up with, evolve and participate in the development of these trends. An example of this is the SM@RTM project, a project whose objective is to improve the competitiveness of RTM processes by reducing defects occurrence, and in which IDAERO is in charge of the RTM defectology and structural Digital Twins.