Based on the recent announcement about NVIDIA's Omniverse, I have studied more about the platform and its tight integration with digital twins. What is Digital Twins? It is a new type of simulation or model used to understand and optimize complex systems. Here is some key knowledge about digital twins and their application in the real world.
- A digital twin is a digital or virtual copy of the physical assets of products.
- It delivers a virtual representation of real-world products, systems, plants, and cities.
- Entire factories down to individual machines can be simulated and tested.
- It is considered the innovation backbone of the future.
- Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) play essential roles in digital twins.
- Its key industries are manufacturing, automotive, construction, utilities, and healthcare.
- NASA was one of the first to use this kind of technology for space exploration missions in 1960. NASA used a mirroring technology to replicate systems in space.
- Initially, the idea of digital twins was published in 1991, in the book Mirror Worlds, by David Gelernter.
- Dr. Michael Grives was the first to apply the concept of digital twins to manufacturing in 2002. He formally announced the digital twin concept within product lifecycle management.
What is Digital Twins
Digital twins are a new type of simulation or model used to understand and optimize complex systems. It is a representation of a physical system that can be manipulated and monitored in order to improve its performance.
By understanding the behavior of digital twins, different industries can improve the performance of physical systems by optimizing their design and operations. Digital twins, in collaboration with artificial intelligence (AI), will allow computers to design advanced products independently.
Digital Twins and Simulations
Digital Twins and simulations utilize digital models to replicate a system's various processes; however, a digital twin is actually represented in a virtual environment, making it considerably richer for study. So, comparing a digital twin to a simulation is essentially a matter of scale; while a simulation usually studies one particular process, a digital twin can run a wide variety of valuable simulations to study multiple processes.
Simulations usually don't benefit from having real-time data. But digital twins, using sensors, are a two-way flow of information. Object sensors provide relevant data to the (virtual) system processor and then happen again when insights created by the processor are shared back with the actual (real) source object.
In short, a key difference is that digital twins are platforms that obey the laws of physics and follow true-to-reality submit scenarios. It is not just a simple approximation, representation, or a simulation; digital twins are actually virtual models that are true to reality.
Benefits of Digital Twins
There is no doubt that digital twins' significant benefit lies in the research and development of anything. Digital twins enable more effective product research and design, with extensive data generated about likely performance outcomes. As a result of that information, companies may be able to make product improvements before production begins.
Manufacturers can utilize digital twins to decide how to dispose of products that have reached the end of their lifecycle and must undergo final processing, such as recycling. This type of platform also helps organizations achieve and maintain peak efficiency throughout the entire manufacturing process, even after a new product is in production.
How it works
Objects being studied are outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical object's performance, such as energy output, temperature, weather conditions, etc. This data is then relayed to a processing system and applied to the digital copy.
- Digital twin connects the physical and virtual world, gathering data in real-time from installed sensors.
- The collected data is either decentralized locally or centralized in the cloud.
- After evaluating the data, the assets are simulated in a virtual copy.
- The parameters are applied to real assets after receiving the information from the simulation.
- In this way, data in both real and virtual representations can be integrated to optimize the performance of real assets.
Manufacturing is a crucial industry for Digital Twins. Smart Factory is the vision of a production environment in which production facilities and logistics systems are organized without human intervention.
The technical foundations on which the Smart Factory - the intelligent factory - is based are cyber-physical systems that communicate with each other using the Internet of Things and Services. In this process, data must be exchanged between the product and the production line. As a result, the Supply Chain can be connected more efficiently and production environments can be better organized.
The Fourth Industrial Revolution promotes what has been called a "smart factory". Modular structured smart factories have cyber-physical systems that monitor physical processes, create a virtual copy of the physical world, and make decentralized decisions. Through the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real-time, both internally and across the range of administrative services offered and used by participants in the value chain.
Other key industries are:
Using digital twins, industries, through simulation, can understand the performance and behavior of complex systems. These simulations can be used for decision-making, troubleshooting issues, and improving operational efficiency.
Digital Twins are the next big thing in the Fourth Industrial Revolution (Industry 4.0) to develop new products and processes. An entire ecosystem of digital twins will soon help industries respond to global challenges.