Introduction to Digital Twin Technology
December 4, 2020
Global industries have achieved a tremendous technological transformation due to the linkage between the digital and physical worlds. Digital twin technology has played an important role in enhancing this convergence.
This technology enables companies or organizations to develop digital copies of their products, assets, or processes to optimize performance and maintenance.
For example, this technology has been employed in the automobile industry to create digital copies of cars. Manufacturing firms have also used it to replicate physical production processes.
This article will go over the concept of the digital twin and how digital twin technology works. It will also provide an overview of how this important technology has been employed in real-life applications.
What is a Digital Twin?
A digital twin is a virtual representation or digital replica (copy) of assets, people, processes, systems, devices, and places. Some of the things that can be replicated using digital twin technology include vehicles, aircraft engines, and people.
If an automobile company develops a virtual representation or digital copy of a car model, this digital replica is considered the physical car’s digital twin. Similarly, if a manufacturing company creates a virtual representation of its manufacturing process, the (digital) replicated process is a digital twin of the physical process.
A digital twin can also be described as a digital profile of a process or physical object’s current and historical state. This virtual representation provides the dynamics and elements of how an IoT device lives and operates.
Continuous learning and updates enable the digital twin to provide the real-time position, condition, and/or status of the physical assets. This pairing of the real world and the digital world enables organizations to monitor systems, develop plans, and anticipate problems before they occur.
Digital twins are created using digital twin technology. This technology integrates the internet of things (IoT), software analytics, artificial intelligence, and special network graphs to replicate physical assets or processes (physical twins).
Characteristics of digital twin technology
Digital twin technology comprises certain characteristics such as connectivity, homogenization, re-programmability, digital traces, and modularity.
In digital twin technology, we embed connectivity between physical assets and their digital counterparts. We attach sensors to physical objects to enhance their connectivity with their digital representations.
Data from the physical components is obtained and integrated via these sensors. This integration enables the sensors to communicate the collected data to a user.
Digital technology is also characterized by the homogenization of data from physical components. This means that a digital representation similar to the physical object can be created using the collected data. This technology can also enable data to be decoupled from the physical artifacts.
A digital twin can enable the replicated physical product to be reprogrammed. This can be used as the basis for creating new versions of the initial product.
A good example of this in the case of engines. A digital twin for a current engine can be reprogrammed to improve fuel efficiency and productivity.
This technology consists of digital traces that are left when creating a digital twin. Digital traces are used by engineers in the diagnosis of problems when machines break down.
Digital technology enhances the customization of digital production modules. This enables manufacturers to modify their models. Modularity in digital technology enables manufacturers to identify areas that need improvement.
Applications of digital twin technology
The digital twin technology is applied in various industries. The following are some use cases of this technology.
Digital twins are used for replicating the physical processes in manufacturing companies. Digital technology enhances the interaction of virtual objects and physical objects in the factory.
When there are problems in the actual physical processes, engineers assess the digital twin to trace the problem’s origin and nature. Digital twins in manufacturing can also be used to improve processes.
Through digital twin technology, health professionals can create personalized models to improve medical care. These professionals can use the digital twin of organs or patients (instead of real patients) to practice important health procedures.
Digital twin technology is important in the planning of smart urban cities. Digital twins are used for modeling urban cities and related data.
This technology has also enhanced the digitization of related activities such as construction, maintenance, and operation of urban projects. It integrates the built project with its digital replica to enhance optimal performance.
Automobile companies use digital twin technology to create digital twins of their cars. These digital twins enable these companies to showcase how real cars operate.
In case improvements are needed with the current car models, engineers will use the digital twins to suggest new features that can improve their performance.
Digital twin technology enables companies to virtualize their product packages before the actual packaging takes place. This helps in reducing errors in packaging.
Logistics companies can also use this type of technology to assess material feasibility, design effective warehouse layouts, and create feasible logistics networks.
Why digital twin technology is important
There are various reasons why digital twin technology is important to organizations. It enables companies to develop a digital footprint of the entire product life cycle.
Companies can use the digital copies of their products to perform product improvements. They can also utilize digital twin technology to develop virtual prototypes of new products.
The digital twin created by a company enables it to drive value through improved models and reduced defects.
A digital twin enables a company to detect problems in a product or manufacturing process. Through digital technology, outcomes can be predicted effectively with a high level of accuracy.
With this technology, there is a faster realization of value than before.
How digital technology works
Digital twin technology works by integrating artificial intelligence, internet of things (IoT), and software analytics with physical assets or processes.
Physical products, devices, processes, or equipment are built with sensors to collect data. A cloud-based system is used to send this data to the digital world.
Machine learning algorithms are then used to analyze the collected data. This analysis generates actionable information that creates a digital copy of the physical world (physical asset, product, or process).
The digital twin generates insights to signify areas that need a change in the physical process or product.
Continuous analysis of real-time data ensures that the digital twin is up to date with the physical world’s current state.
The digital twin model of a manufacturing process gives a good example of how digital technology works. In this case, the digital twin provides a virtual representation of what happens in the factory in real-time.
The following diagram shows how this digital twin model works.
The digital twin model consists of six main features such as: sensors, actuators, data, integration, analytics, and the digital twin.
These features perform specific roles that enhance digital twin technology.
Sensors: Sensors are positioned at various points of the physical process to collect data. Some of the important dimensions of data collected include works in progress (including speed, thickness, and color quality), environmental conditions of the factory, and machines’ behavioral characteristics.
Data: This is the information collected from the physical processes. The main data collected includes operational and environmental data. This data is aggregated and combined with other enterprise data such as design specifications, enterprise systems, engineering drawings, and customer logs.
Interaction: Interactive technology enhances the interaction between the physical processes and the digital representation of these processes. This technology enables sensors to communicate the data mentioned above to the virtual (digital) world. Some of the integration technologies used include edge, security, and communication interfaces.
Analytics: The digital twin application continuously analyzes incoming data. Data is analyzed through visualization routines and algorithmic simulations. Digital twins produce insights using this analysis. This analysis ensures the actual manufacturing process takes place seamlessly. It also identifies unacceptable trends in this process.
Digital Twin: This is the digital replica of the actual manufacturing processes. It’s shown on the right side of the diagram.
Actuators: If there is any need for action in the actual process, the digital twin will use actuators to generate the action.
The future of digital twin technology
Digital twin technology is driving optimal performance and innovation in various industries.
There will be many digital twins representing various physical realities in the coming years, such as people, processes, places, and objects.
This may call for more collaboration between experts who handle physical products and data scientists.
In the future, digital twin technology will have a major influence on customer and business value. Companies that will use this technology in their processes will experience significant improvements in their customer experience.
It will drive business value through improvements in the existing processes, products, and operations. Investing in digital twin technology will be an important source of competitive advantage for high-tech companies.
Peer Review Contributions by: Lalithnarayan C
About the authorOnesmus Mbaabu
Onesmus Mbaabu is a Ph.D. candidate pursuing a doctoral degree in Management Science and Engineering at the School of Management and Economics, University of Electronic Science and Technology of China (UESTC), Sichuan Province, China. His interests include economics, data science, emerging technologies, and information systems. His hobbies are playing basketball and listening to music.