AI-Driven Digital Twin Framework for Smart Industrial Process Optimization
TL;DR
An AI-driven Digital Twin framework that integrates real-time sensing, machine learning, deep learning, and predictive analytics for intelligent process monitoring, predictive maintenance, anomaly detection, energy optimization, and autonomous decision-making is proposed.
Abstract
Industry 4.0 has accelerated intelligent manufacturing through AI, IIoT, cloud-edge computing, and Digital Twin technologies. This study proposes an AI-driven Digital Twin framework that integrates real-time sensing, machine learning, deep learning, and predictive analytics for intelligent process monitoring, predictive maintenance, anomaly detection, energy optimization, and autonomous decision-making. The framework enables continuous synchronization between physical assets and their digital counterparts, supporting closed-loop optimization with low-latency edge computing and cloud-based analytics. Reinforcement learning further improves production efficiency, equipment reliability, and resource utilization while reducing downtime and operational costs. Applicable across multiple industrial sectors, the framework also addresses interoperability, cybersecurity, and data governance challenges, providing a scalable foundation for sustainable and human-centric Industry 5.0 manufacturing systems.