In the July issue of the “Hot Topics in Tech” newsletter series, we provide a quick foray into the technology of digital twins and its applications.
Digital Twin refers to a digital replica of a product, process or system that virtually represents its life-cycle and decision making capability. Additionally, it allows analysis of data and monitoring of systems to detect problems before they occur, preventing downtime, and opening up new opportunities, including planning for the future.
One of the major applications of the technology is in simulating physical assets before product launch. It takes years for an engineer or scientist to turn an idea into a working prototype. Besides the time involved, there are other problems to avoid such as the real-time faults, errors, the need for reworking and fixing, re-engineering, redesigning, troubleshooting and testing. Predictive analysis and risk assessment allow us to ensure that the likelihood of these occurring is mitigated.
With digital twin, all the above said difficulties and tasks and the expenses involved can be brought down. A computer-aided model replicating a physical substance interacts with the physical substance using data collected from sensors attached to it. The sensor data is fed to the digital model to imitate the real-time functions. This data helps in identifying problems, optimizing performance, maintaining the regime and correcting future risks.
Fig 1: Digital twin process flow chart
What differentiates digital twin from augmented or virtual reality and from other simulation techniques, is that while AR/ VR are unreal representations without any integrating real-time information into the system, in digital twin real-time data and parameters are fed to the system to imitate the performance.
Digital twin can, however, be used alongside AR/VR. VR allows its users to immerse themselves in the environment of the digital twin. On the other hand, the digital twin of a machine can be overlaid on top of it using AR, allowing users to visualize the machine’s inner workings and understand its data flows enabling faster and more effective decision-making.
Digital twin may be merged with technologies like Internet of Things (IoT) to enhance preventive maintenance and analytics/AI (artificial intelligence)-based optimization of the physical system and operational processes. Smart buildings are an excellent example of applications that stand to benefit from machine learning capabilities in the digital twin.
Fig 3: Digital Twin shown alongside virtual reality
Other industries influenced by digital twin include the manufacturing industry, wherein the technology allows optimized and more efficient emulations with reduced throughput times. Sports sectors such as Formula 1 cars, wind turbines in locomotives, jet engines are also manufacturing technologies where digital twin finds its application. Space technology is the pioneering industry that devised this technology, and it continues to find varied applications here. The retail industry opens opportunities by digitizing customer experience by making mirror twins for customers and modeling fashions for them on it. City planning ventures such as policy making, grievances, economic development, administration and quality of life are sectors hoping to be improved with emulation models provided by digital twin.
In healthcare, personalized patient monitoring, preventative cure, health predictions and analysis can be effectively managed with zero-risk using digital twin. Surgical procedures and the effects of certain drugs may also be tested and monitored digitally before they’re implemented onto patients.
Digital twin technology is starting to emerge as a key area of focus for technology giants looking to capitalize on the broad area of applications. It has a long way to go before it becomes integrated with other emerging technologies such as IOT, Block chain and AI.