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What Is Digital Twin?

digital twin technology

What does digital twin mean?

A digital twin (DT) is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and combines simulation, machine learning, and reasoning to help with decision-making. A digital-twin is also known as a digital duplicate (DV). As a method for enhancing the efficiency of corporate operations, digital-twin are gaining an increasing amount of popularity.

How does Digital Twin technologies work?

Research into the physics and operational data of a physical object or system is conducted by professionals that specialize in applied mathematics or data science in order to build a mathematical model that simulates the real thing. The creation of a digital twin begins with this first phase in its life cycle.

Programmers who are responsible for the design of DT ensure that the computer model that is housed on the virtual computer has the capability of receiving input from sensors that are able to collect data from the version that is located in the real world. The digital version is able to imitate and recreate what is happening with the original form in real time as a result of this. Because of this, it is now able to acquire insights not just on performance but also on any potential problems.

The degree to which a model accurately mimics its counterpart in the actual world is contingent on a variety of data values; the degree to which a digital twin is sophisticated or simple can vary according to the requirements of the user.

Together, the twin and the prototype can serve as a source of feedback on the product while it is still in the process of being developed. Alternately, the twin can serve its own purpose by acting as a prototype to simulate what might occur with the actual version when it is produced.

Digital-twins vs. simulations

Although simulations and digital twins both use digital models to replicate the various processes of a system, a digital-twin is actually a virtual environment, which makes it significantly more rich for research than a simulation. The primary distinction between DT and simulation lies in their respective scales of operation: In most cases, a simulation will investigate just one particular process; however, a digital twin concepts can independently execute any number of relevant simulations in order to investigate a variety of processes.

There are additional distinctions beyond this point. For instance, having access to real-time data is typically not beneficial when running simulations. However, digital twins are built around the concept of a two-way flow of information. This flow begins when object sensors send relevant data to the system processor, and it continues when the system processor shares the insights it has created with the original source object.

Because digital-twins have access to more accurate and up-to-date data pertaining to a wide variety of domains, along with the additional computing power that comes with a virtual environment, they are in a better position than standard simulations to investigate a greater number of issues from a significantly wider range of perspectives. As a result, they have a greater potential to ultimately improve both products and processes.

Types of Digital Twins

The degree to which the product is magnified determines the various types that are available. The domain of application is the primary distinction between these two identical siblings. It is not uncommon for a single system or procedure to host multiple varieties of DT at the same time. Let’s go over the various kinds of DT to get a better understanding of the differences between them and how they can be used.

1-Component twins/Parts twins

The component twin is the fundamental building block of digital-twin and the smallest operational component that can exist on its own. A component’s “parts twin” is essentially the same thing as the component itself, but it refers to a component that is of marginally less importance.

Asset digital twin technology

2-Asset twins

An asset is the result of a combination of two or more components working in concert with one another. You are able to study the interaction between those components by using asset twins, which generates a wealth of performance data that can be processed and then turned into actionable insights.

3-System or Unit twins

The next level of magnification involves system or unit twins, which allow you to see how various assets come together to form a functioning whole system. This level of magnification allows you to see the next level of detail. System twins offer visibility into how different assets interact with one another and may make suggestions for ways to improve performance.

digital twin technology

4-Process twins

The macro level of magnification that process twins provide sheds light on how the individual components of a production facility come together to form the whole. Are all of those systems coordinated to work at their maximum capacity, or will delays in one system have an effect on the performance of the others? The precise timing schemes that ultimately have an effect on overall effectiveness can be determined with the assistance of process twins.

Benefits of Digital Twin Technology

  • Equipment and production lines are now more reliable.
  • Increased OEE due to less downtime and better performance.
  • Made things work better
  • Less risk in many areas, such as product availability, market reputation, and more
  • Cut down on maintenance costs by predicting problems before they happen.
  • Making things faster
  • New business opportunities like mass customization, mixed production, small batch production, and more
  • Better customer service because customers can customize products from a distance
  • Better product quality and a clearer picture of how your products work in different real-time environments and applications
  • Supply and distribution chains that work better
  • Made more money

Digital Twin Examples

I will mention only two examples of digital twins:

AI and machine learning are often used to look at the model of operations shown by the digital-twin, even if the real facility is in space and the equipment is on Earth. In the early days of space exploration, NASA used pairing technology, which was the forerunner of the digital twin technology, to solve the problem of how to operate, maintain, and fix systems when you aren’t near them. This is exactly how engineers and astronauts on Earth figured out how to save the Apollo 13 mission. Today, NASA uses digital-twins to study the next generation of cars and planes.

People can get used to using the Internet of Things and automation with the help of digital twin technology because they can simulate the application before it goes live. A digital-twin can be used to learn lessons and find opportunities that can then be used in the real world.

Digital Twin Companies

World’s Leading Digital-Twin Companies: 

  1. Microsoft Corporation
  2. IBM Corporation
  3. Siemens
  4. Oracle Corporation
  5. Bosch
  6. General Electric Company
  7. Cisco Systems
  8. Dassault Systemes
  9. Ansys
  10. PTC Inc.

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