| Abstract |
The concept of digital twin (DT) has rapidly progressed from a theoretical concept to a practical application, with widespread adoption across multiple sectors. This chapter explores sectors like manufacturing, energy, healthcare, transportation, construction, aerospace industry, and smart cities where digital twin knowledge is being used and highlights its various applications. In the manufacturing sector, digital twins are employed to improve product quality, enhance production processes, and predict equipment failures. In the energy sector, digital twins enhance the efficiency of energy systems, predict maintenance needs, and reduce energy consumption. In healthcare, digital twins are used to create tailored patient models, simulate surgical procedures, and optimize treatment plans. In transportation, digital twins optimize logistics and reduce delivery times. In construction, they help improve project management, reduce errors, and enhance safety. In agriculture, digital twins optimize crop yields and resource management, enabling farmers to make more informed decisions about water usage, fertilizer application, and pest control. In the aerospace industry, digital twins monitor the performance of aircraft, predict maintenance needs, and improve safety. This technology reduces maintenance costs and enhances overall aircraft reliability. In smart cities, digital twins enhances various aspects of city life, such as controlling traffic flow, minimizing energy consumption, and improving public safety. Planners can test different scenarios and maximize resources for effective and environmentally friendly city living. While digital twin technology offers numerous benefits, its implementation requires a significant investment in infrastructure, data management, and expertise. These factors present challenges to widespread adoption. Despite the challenges, this chapter analyzes how digital twin technologies have the potential to revolutionize a variety of industries by giving real-time information, lowering costs, optimizing processes, and improving safety. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
