Digital Twin Technology
- [Technologies Used in Digital Twins - Softengi]
- Overview
A digital twin revolution is coming. A digital twin is a digital representation of a physical object, process or service. Digital twins can be digital replicas of objects in the physical world, such as jet engines or wind farms, or even larger objects such as buildings or even entire cities.
Originally created for aerospace applications, digital twins are now used in a variety of engineering fields: from construction to automotive, from healthcare to aerospace, from software engineering to cyber-physical systems and blockchain. The series will showcase interdisciplinary research and development of digital twin technology. In addition to physical assets, digital twin technology can be used to replicate processes to collect data to predict how they will perform.
Essentially, a digital twin is a computer program that uses real-world data to create simulations that can predict the performance of a product or process. These programs can integrate IoT (Industry 4.0), artificial intelligence and software analytics to enhance the output.
With advancements in factors such as machine learning and big data, these virtual models have become a major tool in modern engineering to drive innovation and improve performance.
In short, creating a technology trend that can enhance strategic and prevent costly failure of physical objects, also through the use of advanced analytics, monitoring and predictive capabilities, testing processes and services.
- Digital Twin Technology
Digital twins are digital replicas of animate or inanimate physical entities. Digital twins are digital replicas of potential and actual physical assets (physical twins), processes, people, places, systems and equipment that can be used for various purposes. Digital representations provide the elements and dynamics of how Internet of Things (IoT) devices operate and live throughout their lifecycle.
The concept of a digital twin, a virtual representation of a product, plays an integral role in our technology-driven modern industrial world. Essentially, a digital twin is a computer program that takes as input real data about a physical object or system and produces as output a prediction or simulation of how that physical object or system will be affected by those inputs.
A digital twin is a virtual replica of a physical device that data scientists and IT professionals can use to run simulations before building and deploying the actual device. They are also changing the way technologies such as IoT, AI, and analytics are optimized. Digital twin technology has moved beyond manufacturing into the converged world of IoT, AI and data analytics. With more complex "things" associated with the ability to generate data, having a digital equivalent enables data scientists and other IT professionals to optimize deployments for maximum efficiency and create other what-if scenarios.
- Digital Twin Adoption Grows
A digital twin is often defined as a software representation of a physical asset, system or process designed to provide business value through real-time analytics to detect, prevent, predict and optimize. The concept of digital twins has been around since the early 2000s. The company will program the replica on their computer or server and enter the necessary data to update the design or environmental specifications. This information is rarely received in real time. This method is a very clumsy and slow way of rendering these virtual copies. With the advent of IoT technologies such as smart sensors and cloud systems, digital twins have become easier and cheaper to create, maintain and use. We have now reached the stage of intelligent enterprise asset management supported by an influx of connected information from digital manufacturing equipment, hyperautomation, and other sources.
Digital twins are becoming more common in and out of factories, as these exact virtual replicas of systems allow for larger experiments with various scenarios to determine outcomes. It's a surefire way to test new ideas and different conditions. Positive results can then be communicated to the physical twin for implementation. The idea of a digital twin as a real-time digital representation can be applied to more complex physical structures, often combining connected assets and products with specific business outcomes. Visualization allows companies to test for potential machine failures, optimize processes, plan for future capacity, and more.
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- Popular Use Cases for Digital Twin
Today, the application of digital twins is widely used in many industries. Applications of digital twins provide accurate virtual representations of objects and simulations of operational processes, enabling businesses of all kinds to rapidly identify areas of innovation and improve business processes and performance. Popular use cases for digital twins include: manufacturing, healthcare, utilities, disaster management, insurance, smart cities, and more. Examples of digital twins include product digital twins, component digital twins, and performance digital twins, all of which are used to visualize and process objects, whether single or more complex.
Industry 4.0 is sometimes referred to as the "Fourth Industrial Revolution". The current trend is that manufacturing is increasingly automated and relies on capturing and exchanging data through the Internet of Things and cloud computing, often referred to as "big data". Industry 4.0 creates so-called "smart factories". A digital twin as a computer representation of the product fits into this model.
- Technologies Used in Digital Twins
The application of a digital twin encompasses four technologies that allow the creation of digital representations, the collection and storage of real-time data, and the provision of valuable insights based on the information obtained. Digital twin technologies include Internet of Things (IoT), Extended Reality (XR), cloud and artificial intelligence. Depending on the type of application, more or less specific techniques can be used.
- Extended Reality (XR) is a visualization technology that creates digital representations of objects. XR capabilities enable digital twins to digitally model physical objects, allowing users to interact with digital content.
- Cloud computing technology is used to efficiently store and access data over the Internet. Since digital twin applications process large amounts of data, cloud computing allows to store all data in a virtual cloud and easily access the required information from any location.
- Artificial Intelligence (AI) is an advanced analytical tool that automatically analyzes the acquired data and provides valuable insights. It can also make predictions about possible outcomes and make recommendations on how to avoid potential problems.
- The Life of a Digital Twin
The life of a digital twin begins with being constructed by an expert, usually an expert in data science or applied mathematics. These developers study the physical properties behind the simulated physical object or system and use that data to develop a mathematical model that simulates the real-world original in digital space.
Sensors attached to the physical product collect data and send it back to the digital twin, and their interaction helps optimize the product's performance through maintenance mechanisms. For example, sensors in an aircraft engine might detect when a component needs to be replaced or repaired. Jet engine manufacturer Rolls-Royce already uses Engine Health Management (EHM) to track the health of thousands of engines, using onboard sensors and real-time satellite feeds. These data are used to strengthen maintenance regimes. Eventually they can be used in the initial aero-engine design process.
- Digital Twin in Smart Farming
Digital twins hold great promise to take smart farming to new levels of agricultural productivity and sustainability.
A digital twin is the digital equivalent of a real-life object that mirrors its behavior and state during its lifetime in a virtual space. Using a digital twin as a central means of farm management can decouple the physical flow from its planning and control.
As a result, farmers can manage operations remotely based on (near) real-time digital information, rather than relying on direct observations and manual tasks in the field. This enables them to act immediately when (expected) deviations occur and simulate the effects of interventions based on real data.
[More to come ...]
