AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0

There has been tremendous improvement in terms of automation and connectivity owing to the implementation of smart manufacturing as part of Industry 4.0; however, some issues continue to hamper the process of real-time adaptation, prediction, and decision making based on humans. Industry 5.0 represents the new era where there is the need for manufacturing systems that are not only resilient but sustainable and human-centric. This calls for frameworks combining AI in cyber-physical systems. In this paper, a novel Digital Twin with AI technology is introduced to provide an optimized approach for manufacturing systems following the Industry 5.0 guidelines. This approach consists of an intelligent multi-level architecture including physical systems, IoT-based data acquisition, digital model building, AI-based analytics techniques, and human-in-the-loop decision-making. It allows constant synchronization between the two worlds of physics and cyberspace, leading to proactive decision making and better system performance. A systematic approach is described here including data pre-processing, modeling, use of machine learning, and improvements to the system based on feedback. The analysis using simulation techniques shows the ability of the approach to increase efficiency, reduce downtime, and optimize predictive maintenance through real-time optimization. Additionally, human-AI interaction provides for flexibility in dealing with situations in manufacturing settings. The suggested solution addresses the main challenges that exist in existing Digital Twin applications by emphasizing such key factors as scalability, interoperability, and intelligence. This research provides a thorough conceptual framework that closes the gap between AI-powered analysis and manufacturing systems designed to serve humans. As it can be concluded from the results, Digital Twins enabled by artificial intelligence are expected to play a crucial role in future smart manufacturing.