The rapid advancement of Industry 4.0 has revolutionized the manufacturing sector with cutting-edge technologies such as Digital Twin (DT), which enables real-time monitoring, simulation, and optimization of production systems. Digital Twin technology integrates IoT sensors, Artificial Intelligence (AI), Big Data analytics, and cloud computing to create a dynamic, data-driven representation of physical assets. This paper provides a comprehensive analysis of DT architecture, key enabling technologies, and real-time optimization strategies in manufacturing. We discuss the challenges associated with implementation, including data synchronization, computational complexity, and cybersecurity risks. Additionally, case studies demonstrate the impact of DT on predictive maintenance, quality control, downtime reduction, and energy efficiency. The results indicate significant improvements in production speed, defect rate reduction, and resource utilization. Finally, we explore future trends and research directions for enhancing DT adoption in smart manufacturing environments.
Digital Twin, Smart Manufacturing, Real-Time Optimization, Industry 4.0, IoT, Artificial Intelligence, Predictive Maintenance, Cyber-Physical Systems, Process Efficiency, Big Data Analytics
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