Digital twin technology is an emerging concept at the forefront of the Industry 4.0 revolution facilitated through advanced data analytics and the Internet of Things (IoT) connectivity.
The digital twin tackles the challenge of seamless integration between IoT and data analytics by creating a connected physical and virtual twin (digital twin). A digital twin environment allows for rapid analysis and real-time decisions made through accurate analytics.
In other words, a digital twin is a real-time virtual model of something in the real world, like a spacecraft or a supply chain process. A digital twin of a critical infrastructure could be a virtual representation of a power plant, including data from sensors, simulations, and historical performance data.
With information from sensors and systems, a digital twin receives constant feedback from its physical twin. Digital twins allow us to study a real-life object or process through simulations or tests that would otherwise be physically impossible or too costly.
Another advantage is that each physical object or process can have its digital twin. Therefore, the digital twin can account for different contexts and developments. This trend builds upon the growth in edge computing and real-time data streams.
Digital twin applications
1. Smart cities
Because of the rapid advancements in IoT connectivity, digital twins are increasingly being used and have the potential to be extremely effective in smart cities. As more smart cities are built, communities become more connected, which increases the use of digital twins. In addition, the more information we collect from IoT sensors integrated into a city’s core services will open the door for research aimed at developing sophisticated AI algorithms.
Future-proofing smart city services and infrastructure greatly benefits from their ability to have sensors and be monitored by IoT devices. It can be used to support the ongoing development of other smart cities and the planning and development of the current smart cities. In addition to the advantages of planning, there are advantages to energy conservation. This information provides a wealth of information about how our utilities are used and distributed.
Digital twin technology has the potential to advance the smart city. Creating a living testbed within a virtual twin that can accomplish two goals—testing scenarios and enabling digital twins to learn from their surroundings by examining changes in the data collected—can facilitate growth. Data analytics and monitoring can be done with the collected information. As smart cities develop, connectivity and the amount of usable data increase, the potential for digital twins becomes more viable.
2. Manufacturing
The manufacturing industry has been identified as the next potential use for digital twins. The main cause is that manufacturers constantly look for ways to track and monitor products to save time and money, a major driving force and motivation for any manufacturer. This is why digital twins appear to have the biggest effect in this context. Similar to how connectivity is one of the main factors driving the use of digital twins in manufacturing, smart cities are developing. The current expansion fits with the idea of Industry 4.0, also known as the fourth industrial revolution, which uses device connectivity to make digital twins of manufacturing processes a reality.
The digital twin has the potential to provide real-time feedback on the production line as well as machine performance. It enables the manufacturer to anticipate problems earlier. Utilizing digital twins improves device reliability and performance by increasing connectivity and feedback. As a result of the machine’s ability to store large amounts of data required for performance and prediction analysis, AI algorithms combined with digital twins have the potential to be more accurate. The ability to test products in an environment and have a system that reacts to real-time data is made possible by the digital twin, which can be a very useful tool in the manufacturing industry.
The automotive sector is where digital twins are also used, perhaps most famously by Tesla. Creating a digital twin of an engine or auto component can be useful for data analytics and simulation. AI increases testing accuracy by analyzing real-time vehicle data to forecast present and future component performance.
Another industry that offers a variety of applications for using digital twins is the construction sector. A digital twin could be used in the planning phase of a building or structure. The technology can create smart city structures or buildings and a continuous real-time monitoring and prediction tool. When predicting and maintaining buildings and structures, using the digital twin and data analytics may lead to greater accuracy with any changes made virtually and then applied physically. Construction teams can simulate more precisely thanks to the digital twin’s ability to apply algorithms in real-time before the physical building even exists.
Unlike low-detailed static blueprint models, real-time simulation is a common objective observed across the digital twins field. These models are useful, but their predictability and learnability are constrained because they don’t use real-time parameters. The digital twin can use the machine and deep learning algorithms while simultaneously learning and monitoring.
3. Healthcare
The application of digital twin technology can also be found in the healthcare industry. The impact of advancing technology on healthcare is unprecedented because it is making previously unthinkable things possible. IoT connectivity has increased because devices are more affordable and simple to set up. The potential use of digital twins in the healthcare industry is only increasing due to the increased connectivity. One potential future use is a digital twin of a person that provides a real-time analysis of the body. A digital twin is a modern application that more accurately mimics the effects of some drugs. A digital twin is used in different applications to plan and carry out surgical procedures.
Using a digital twin allows researchers, physicians, hospitals, and healthcare providers to simulate environments specific to their needs, whether in real-time or in anticipation of future developments and uses. This is similar to other applications within a healthcare setting. Additionally, the digital twin can be used with AI algorithms to generate more accurate predictions and judgments. Numerous healthcare applications do not directly involve the patient but are still helpful for ongoing care and treatment; this explains the crucial role such systems play in patient care. Although digital twins in healthcare are still in their infancy, they have enormous potential, from being used for bed management to managing large-scale wards and hospitals.
The importance of simulating and responding in real-time is even greater in the healthcare industry because it can mean the difference between life and death. The digital twin could also benefit from predictive maintenance and ongoing medical equipment repair. Together with AI, the digital twin in the medical setting has the potential to make life-saving decisions based on recent and historical data.
