The Digital Twin market is becoming increasingly popular, with enterprises and businesses utilizing the technology to simulate business processes to improve operational efficiency and performance. Due to the increasing demand for these twins, there is often misinformation regarding what a digital twin is and is not. This article will dive into this topic, shedding light on what characterizes a twin and what technologists often confuse as being a twin.
Defining Digital Twins
Digital Twins can be defined as virtually simulated replicas of real-world entities. However, unlike popular misconception, digital twins are not simply digital copies of physical replicas but exact one-to-one duplicates built with meticulous precision using custom-built software such as NVIDIA Omniverse.
A twin consists of realistic physics, geometry, and real-time interaction with a physical counterpart. Realistic physics is the key here, with digital twins often confused with digital replicas that are not exact nor have realistic physics, such as in video games. For example, a digital replica of a broom found in a video game would not be a digital twin, whereas a one-to-one copy of a broom with realistic physics and the above criterion would be. Digital twins also act as critical pieces of software in a larger web of the Internet of Things, providing and using data.
How do we Classify Digital Twins?
Digital twins can be categorized into three distinct groups. The first is Mirroring. A Mirroring Digital Twin replicates a physical counterpart in ensuring optimization
A Mirroring Digital twin replicates a physical counterpart state in real time. Used for optimization and remote monitoring purposes, this twin is used by companies globally to improve efficiency measures within an organization.
The second type of digital Twin is known as shadowing. This twin virtually simulates a physical entity in real-time and is used for testing and evaluation. The third type of twin, Threading, deals with complex interactions between a virtual replica and its counterpart.
The twin simulates physical counterparts interacting with one another in real-time. It is used in advanced testing and evaluation. Across the three categories, the complexity of these twins increases. Through integrating AI, IoT, and other software and hardware, these twins develop extensive data feedback loops that enable users to make real-time changes in the real world, optimizing processes, changing workflows, and improving performance. The integration of machine learning and AI has further developed digital twins with the technology able to shift manufacturing production easily.
The advancement of these digital twins has pivoted towards the simulation of full-blown factories such as the Freyr Battery factory digital twin over single production lines, showcasing the potential of digital twins to transform manufacturing. What’s not a Digital Twin? As mentioned above, digital twins are conflated as simple virtual replicas of physical objects.
On the contrary, digital twins are complex, multifaceted software that requires precise one-to-one replicating physical entities, real-time interaction, and accurate and realistic physics and geometry. Virtual replicas without these criteria are not digital twins. For more information on how we define digital twins, check out our latest Industrial Metaverse report.
Final Thoughts
Overall, digital twins are complex feats of software engineering that should not be overlooked as simplistic digital replicas of real-world counterparts. Digital Twins are rigorous and often challenging to implement digital tools that are beginning to dynamically change industries globally due to their unique use cases. If you want to learn about the unique use cases of digital twins, providers, and enterprise users, fine-tune your knowledge using our intelligence platform.
