Digital Twin technology is maturing; digital twins are assigned new tasks; they are deployed in more and more industries, and are moving outside the realm of their original applications. From its humble origins in product development in automotive, aerospace, and defense, digital twins have become a common analysis tool in architecture, engineering, construction, procurement, or city planning projects and even the decision-making processes for buying/selling, commissioning, and operations management. A digital twin is a model of a physical object in the digital realm, allowing developers to analyze it as a reference even if the product does not exist yet. Initially, digital twins were mainly used for low-cost prototyping, but now they are being used for various operational streamlining purposes.
What Is a Digital Twin and How Does It Work?
A digital twin is a virtual model that accurately describes a physical object. The physical object has various IoT sensors. Data generated by the physical object – a combustion engine for example – such as power, torque, fuel consumption, and the temperature at various locations in the engine, can be relayed to the digital twin to create a real-time digital copy. Once the virtual model has collected a complete dataset, the model can be used to run simulations, analyze performance issues, and study possible improvements, which after the tests have been completed, can then be used to adjust and improve the original real-world object.
The difference between a simulation and a digital twin is that a simulation refers to replicating one process, whereas a digital twin can replicate multiple processes at once. By using a virtual model to study relevant data, an analyst can learn real-time insights about the project to make quick improvements.
Digital twins have the capability of bringing together several insights at once to make a process more efficient. They serve as a much more powerful resource than what a single simulation generates because they provide data from various vantage points. It’s possible to use this data-rich system to make a more cohesive and competitive product based on analyzing digital manipulations to the 3D version of a prototype.
History of Digital Twins
According to IBM, the original concept behind digital twin technology began in 1991 with the book by Yale professor David Gelernter called Mirror Worlds: or the Day Software Puts the Universe in a Shoebox. How It Will Happen and What It Will Mean. The book explained how reality can be manipulated with the help of a computer. It was an early look into the idea of exploring the world via software without leaving home.
The first scientist to announce the application of digital twins to manufacturing in 2002 was Dr. Michael Grieves at the University of Michigan. Then in 2010 the term “digital twin” was coined by John Vickers of NASA, the aerospace agency that introduced the basic concept during space missions of the 1960s.
One of the leading AI companies of the past decade to capitalize on digital twins has been tech giant Nvidia, which has contributed to improving Pixar’s Universal Scene Description (USD) format. Nvidia is further planning on using USD for advancements in medicine and other industries. It’s also playing a role in creating virtual tools for the metaverse. Since its launch in 1993, the company has grown to be a leader in video games and graphic-based supercomputing.
Construction has been a rapidly-evolving industry due to the adoption of digital twins, particularly generative AI. Virtual technology is helping builders identify cracks in concrete more swiftly so that they can apply sealant and more durable solutions. It’s leading to conditions for allowing more open-source and customized development at worksites. The flip side of the coin is that generating 3D images using OpenAI technology raises questions as to who owns the rights to such images.
Many of today’s business leaders now predict digital twin technology will become a game-changer in 2023. It might even help alleviate supply chain disruptions that have contributed to high inflation.
Types of Digital Twins
Digital twins have evolved over the past few decades to include various types for different purposes. To show the versatility of the digital twin approach, here are some of the most common types:
- Component twins: These models represent the smallest components and basic units of a physical object. They may include parts of a system.
- Asset twins: The combination of two or more components of a digital twin working together is called an asset. It allows for the analysis of interactions between components.
- System or unit twins: These virtual replicas give you a view of how different assets interact within a system.
- Process twins: From a broader perspective, these models allow you to analyze the systems of an entire production operation. They can be used to refine the timing precision of processes.
Digital Twin Trends in 2023
- From connecting files to connecting data: The use of generative AI techniques and new file formats such as USD and glTF make it easier for businesses to analyze 3D shapes and digital twins. The glTF format has been tagged by The Khronos Group as the “JPEG for the metaverse and digital twins.” Data integration tools are contributing to simplifying data exchange processes as well.
- Entertainment firms target the industrial metaverse: Two companies that have helped pioneer the use of digital twins in the entertainment industry have been Epic and Unreal, partnering with GIS, construction, and automotive firms. The globe behind Microsoft’s new flight simulator was developed by Blackshark AI, allowing for the automated transfer of raw satellite photos.
- Generative AI meets digital twins: The combination of generative AI and digital twins opens the door to new ways of training complex computer vision systems. It has already been useful for content creation with ChatGPT, which generates text, and Stable Diffusion, which generates images. This combination will be used to improve autonomous vehicles.
- Hybrid digital twins: Using virtual models presents an array of choices for prioritizing production accuracy versus performance. Hybrid digital twins collect data from IoT sensors and use AI for predictive maintenance. Businesses are expected to increasingly use hybrid digital twins for system optimization and market disruption.
- FDA Modernization Act replaces animals with silicon: Congress recently passed this bill so that pharma companies can abandon animal testing in favor of virtual methods. The new law is set to drive innovation toward patients-on-a-chip technology. It will further make medical digital twins more cost-effective.
- Digital twins drive 5G: Even though 5G delivers data transmission at a much higher speed than 4G, there can still be latency in the radio shadow zone. Cellphone carriers are currently competing to fill these shadows, as digital twins can point to transmission improvements. The 5G market is expected to grow 54.4 percent through 2028, as forecasted by Fortune Business Insights.
Conclusion
A wide range of industries now views the use of digital twins as necessary for gaining a competitive edge in the market. Large operations that serve many people such as hospitals and banks have particularly benefited from digital twin strategies. Contact us at IoT Marketing for more information on using virtual technology to expand your business capabilities.