Digital Twins in Engineering Enabling Real-Time Modeling Virtual Simulations and Predictive Maintenance for Optimal Decision-Making

Abstract

Digital Twin technology represents a transformative approach in engineering, enabling real-time modeling, virtual simulations, and predictive maintenance. Originating in aerospace applications, its adoption has expanded across industries such as manufacturing, automotive, and energy. Digital Twins, as virtual representations of physical systems, leverage advancements in IoT sensors, cloud computing, and big data analytics to optimize operational processes. This study examines the integration of Digital Twin technology in engineering, highlighting its role in real-time monitoring, scenario analysis, and decision-making. Emphasis was placed on the interplay between real-time data, machine learning algorithms, and virtual simulations for predictive analytics and failure analysis, showcasing its potential to enhance efficiency and reliability in diverse engineering applications.