Numerous industries have embraced cutting-edge computer technology as digitalization has progressed, including big data, artificial intelligence (AI), cloud computing, digital twins, and edge computing. In order to analyze the state of the application of digital twins in conjunction with AI, this chapter looks at the research findings of recently published literature. It then evaluates the applications and futures of AI in digital twins. High-fidelity computer simulations are used in modern engineering practice for the design and research of complex engineering systems. In the field of mechanical and aeronautical engineering, computational simulations have long been used to support conceptual design, prototyping, manufacturing, production, test-data correlation, and safety evaluation. In the last 10 years, there has been a change in how computational simulations are used to give assistance across the whole product life cycle.
TopIntroduction
Digital twin technology is computer software that permits simulating the effects of various influences on a certain system or object. To generate a digital replica of a product or gadget, digital twins are used. With the help of this service, businesses may gain invaluable insights on how to enhance operations, boost efficiency, save expenses, or identify problems or errors before they arise. One must understand that it is more than simply a representation of the actual item. IoT sensors assist digital twins, allowing the twin to receive ongoing, real-time data from the physical object. You can thus really see how your items respond and behave. And all of that information is shown on his/her screen!
In the field of digital manufacturing, digital twins are quite important. Here, developing the product, manufacturing method, and assets is crucial before anything else. It's a huge simplification, but according to Arthur Haponik (2020), the procedure normally goes like this:
Digital twin Item -> Production of Digital Twins -> Digital Twin Possessions
Examine digital twins’ use in the four domains of aerospace, on-site smart manufacturing, unmanned aerial vehicles, and smart city transportation while evaluating current and future issues. The use of digital twins and AI in the aerospace sector has been shown to have significant effects on error alerts, aircraft development, and even unmanned flying. By combining data analytics, digital twins, and computer vision in this way, asset and production control may be better understood. As a result, producers might reduce expenses and enhance their goods. The actual road environment is reproduced with smart city traffic, and traffic accidents are simulated to guarantee clear and efficient traffic conditions and enable quick and precise urban traffic management. We will conclude by using the development of digital twins and artificial intelligence as a guide for future studies in related areas.
Digital Twins (DTs) were motivated primarily by the requirement for interaction between genuine physical systems and the digital cyberspace model (Tao, Fei & Qi, Qinglin & Wang, Lihui & Nee, Andrew, 2019). In cyberspace, people attempt to replicate real-world events. Only cyclic feedback in conjunction with whole-life monitoring captures the whole life cycle. This method makes it possible to properly ensure digital reality throughout the whole cycle. It can be tested using various simulations, analyses, data keeping, mining, and artificial intelligence applications to see if it works with real-world physical systems. To determine an intelligent system's intelligence, one must first observe, model, research, and reason. The real production system must be accurately modeled by the digital twins for the intelligent manufacturing system to be put into practice. Usage of Digital Twin technology in various fields and organizations can be understood from the Figure 1 graph (Karin, July 3, 2019).
Figure 1. Usage of Digital Twin Technology.
TopLiterature Review
In many industries, the use of digital twins has improved, and there are promising application possibilities in the future, according to research done by academics. This has also been supported by several recent studies. Recent developments in computer pipelines, Multiphysics solvers, AI, big data cybernetics, data processing, and management tools have made digital twins highly useful.