On computers, it is possible to perform physical simulations that are as close to reality as possible based on the vast amount of data collected, which is an effective means of improving the manufacturing process of your products and the way you provide services.
For example, when changing parts of a production line, you can expect to reduce development time and costs by conducting test operations on the digital twin in advance.
The term “digital twin” has been gaining attention with the spread of IoT, AI, 5G, etc. It is considered a key technology for the future evolution of the manufacturing industry and is expected to be deployed in other fields as well. It differs from traditional virtual spaces in that they can reproduce a more realistic space in real-time. Various data acquired by IoT is sent to a server on the cloud in real-time, and AI analyzes and processes it, making it possible to recreate the physical space in real-time.
We will provide an overview of what kind of technology a digital twin is.
The three dimensions of the digital twin
A necessary condition for the creation of a digital twin is the existence of:
- Physical products in real space
- Virtual products in virtual space
- Data and information flow connection systems that unite physical space with virtual space and virtual subspaces
Product and process engineering teams have been using process simulation and 3D rendering for the past 30 years to confirm an asset’s viability.
A 3D model allows the entire system to be brought together in a virtual space so that conflicts and critical issues are discovered more cheaply and quickly. This is because the release only occurs when all problems have been resolved.
How does a digital twin work?
A virtual representation created to faithfully replicate a real object is called a digital twin. The research item, a wind turbine, for instance, has a number of sensors attached to it that are connected to essential functioning regions. These sensors produce data on a variety of operational parameters for the physical object, such as temperature, weather, energy output, and more. After that, a processing system receives this data and applies it to the digital copy.
The virtual model can be used to examine performance issues, run simulations, and come up with possible solutions once the data has been evaluated. The goal is to generate valuable information that can then be applied back to the original physical object.
Types of digital twin
- Product Twins: Imagine a virtual copy of your favorite physical things like cars, machines, or gadgets. These digital twins help predict when they need maintenance, track how well they’re working, and find ways to improve their design.
- Process Twins: Picture a digital model of how things work in a system or organization, like a factory or an office. It helps figure out how to make things run smoother, faster, and more efficiently.
- System Twins: Think of this as a big-picture digital replica of entire systems, like a whole factory or a city’s transportation network. It helps managers make decisions to make the entire system work better.
- Asset twins: Consider a virtual version of specific things within a system, such as a machine or a building. It helps monitor these things to know when they need maintenance or upgrades.
- Factory Twins: Envision a digital model of a manufacturing facility, including all the machines and processes. It helps optimize production, reduce downtime, and improve overall efficiency.
Benefits of digital twin
Digital twins enable real-time monitoring and simulation of the real world, making work more efficient. For example, if the status of an airplane’s engine is continuously monitored using a digital twin, the frequency of maintenance can be reduced if there is no sign of failure. This makes it possible to perform maintenance only on certain parts.
It also has the potential to save time. When digital twins are introduced into the planning and design stages of the manufacturing and construction industries, simulations can be performed in digital space, allowing actual prototypes to be created. Various tests can be conducted without having to manufacture the product, reducing costs and product development time.
Conclusion
Data twin signify an advancement in the field of duplication, presenting dynamic virtual replicas of actual data systems and operations. Through reflection and examination of data, they offer insights, predictive functionalities, and optimization prospects across diverse sectors. Data twins are positioned to transform decision-making processes, improve effectiveness, and stimulate creativity in the evolving realm of digital change.
