Cross Paths: Digital Twin

“How would you know the difference between the dream world and the real world?” — Morpheus, The Matrix

Image Source: The Matrix Resurrections-WarnerBros

What is it?

A digital twin represents the structure, context, and behavior of a unique one or group of physical objects, creatures, processes, or organizations. It is a software model that allows it to dynamically update, process, and extract insights with data from its physical twin throughout its lifecycle.

What is not?

The digital twin is not a simulation. A simulation is just a starting point. This initial phase is static and requires parameter updating. These updates are essential to adapt to real physical conditions.

When did it begin?

Although the concept of the digital twin has been in the literature for about 20 years, it has found a place in our lives with the development of different repulsive technologies. With the widespread use of Internet of Everything (IoE) technology, the ability to collect faster and higher quality data has been gained. As a result, the digital twin has been appearing more and more in the last five years.

Why now?

Digital twin; AI was less popular 20 years ago than it is today due to the need to develop Machine Learning (ML), IoE, blockchain, cloud computing, Augmented Reality (AR), and communication technologies.

How does it work?

Although the definition or description is somewhat reminiscent of science fiction novels, if we go to the basics, it is a part of applied mathematics. A substantial data collection process is required to create a complete and individual representation of the real-world entity and its components. Once the data is collected, it is transferred to the virtual environment in real-time and continuously. While the model in the virtual environment continues life just like in the real world, it can make predictions about the processes in the real world.

Representation of the Digital Twin Concept

What are the types of it?

Although the digital twin concept is basically the same, its purpose and scope may vary according to its real-world counterpart. In this context, there are four types of digital twins, from narrower to broader coverage.

  • Component twins: Represent a part of a system. For example, The gearing of a rocket engine requires design for specific stress parameters. Designers and engineers can use a digital twin for this procedure.
  • Asset twins: They can be thought of as virtual representations of the product itself, not part of the physical product. An example would be the rocket engine with the gear in the model in the component twin. Another example; a wind turbine is a twin from which the operation of this asset can be monitored.
  • System twins: Represent assets/products working together. It is a whole structure made up of components and assets. For example, It can be considered a rocket with the engine itself or a manufacturing robot.
  • Process twins: Digital representations of systems working together. For example, Modeling of the production line used to manufacture the rocket is performed with a process twin.
  1. Solving how to integrate the main components of the architecture in the smart solutions vertical;
  2. Supporting the integration of different systems within an intelligent organization with a system of systems approach;
  3. Establishing the primary “common language” that the systems connected to the organizations speak and understand for the Data Space concept allows secure data sharing between organizations.
Levels of Integration supported following a Digital Twin approach

What are the benefits?

When thinking about the use cases of the digital twin, production will come first mentioned. The most important reason is that there are environments/factories where regular and high-quality data are collected through sensors for a long time, and processes are standardized with specific procedures. As can be expected, Industries that have met most of the requirements will adapt faster to the digital twin technology. Today, essential applications of the digital twin can be seen in different and dynamic environments, from aviation to health.

The digital twin’s workflow and relationship to other technologies

Digital Twin and Artificial Intelligence

It is not difficult to think of digital twin technology and AI together. If a material such as data can create a perfect twin, any application that can be done with AI, such as predictive maintenance, classification, and regeneration, can be done in virtual space, as we do in the real world. Moreover, this environment will contribute to the development of AI itself.

Where to Start to Develop an AI and Digital Twin Project?

What if we want to develop a digital twin project successfully:

  • Comprehensive, diverse, systematic, and quality data
  • A well-thought-out use case and goal
  • An acceptable set of rules
  • Ethical, human, and environmental-oriented integration of AI
  • Human resources prone to new technologies
  • Technology investments
  • Legislation and ecosystem readiness/maturity
  • The data collected at the endpoints/edge will increase drastically. These increased data points will lead to the need to update some security protocols.
  • Data governance will become more critical than ever.
  • Inevitably, the need for applied training to provide expert human resources in data science and engineering will increase.
  • Project managers who are familiar with ethical values and principles by design will be sought.



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Ayyüce Kızrak, Ph.D.

AI Specialist @Digital Transformation Office, Presidency of the Republic of Türkiye | Academics @Bahçeşehir University |