A digital twin, also known as an intelligent or self-aware model, is simply an interactive 3D representation of any real-world object or process. However, the power of digital twins is beyond that of traditional Computer-Aided Design (CAD) Models and the Internet-of-Things (IOT) by creating real time links between the physical and the digital world.
Through advances in artificial intelligence (AI) and Industry 4.0, it is now possible to map out every aspect of an object or process from its materials, to its exact location. Using a combination of applied mathematics and data science technology, digital twins are able to imitate movements and experiences that are happening in real time.
As more sensors are built into physical objects, the digital twins become even more intelligent, and more capable of learning how things work, and how they interact with their environments. The result becomes smarter spaces that anticipate our needs and operate automatically. Essentially, if something can be digitised, it can be turned into a twin with the goal of increasing innovation, predicting and preventing failures, and testing the efficiency of processes.
Digital twins rely on the power of sensors which are connected to the physical product, and collect data to be sent back to the digital twin. This communication between the sensors and the product data allow the digital twins to learn how to optimise the product's performance.
Using this interaction, businesses can better understand what happens when certain events happen, such as an engine breaking down, whilst giving insight into how it might affect their other operations. For example, if a factory has to take action due to an issue with its digital twin, another factory using that same digital twin can adjust its production levels accordingly. Basically, within manufacturing, digital twins help to optimise performance, through constant analysis and understanding of the product or process at hand.
A digital twin system has the potential to bring many key benefits to individuals and companies alike. Firstly, by improving real-time decision making by providing up-to-date information about your asset, it cuts down on maintenance costs because it can help predict equipment failures before they happen. Using the knowledge of data scientists in collaboration with digital twins, customer and business needs can be met more accurately. This can be through frequent innovation and streamlining between the several stakeholders involved to meet the ever-changing requirements and needs of individuals and businesses.
In short, the future of digital twins is bright. With time and the constant and increasing amount of learning that digital twins can do through their life cycle, they will continue to predict more accurately, and optimise decision making and outputs in order to generate more reliable and efficient insights to whatever product is using the digital twin.