AI-Augmented Digital Twins: A New Frontier in Industrial Automation
Abstract
Digital twins—virtual replicas of physical systems—have gained traction in industrial automation and smart manufacturing. This paper explores how AI augments digital twins to enhance predictive maintenance, real-time monitoring, and decision-making. We propose a hybrid AI-digital twin model that integrates machine learning, IoT sensor data, and simulation techniques to optimize industrial processes. Case studies from manufacturing and energy sectors demonstrate the model’s ability to reduce operational downtime, improve efficiency, and extend asset lifespan. Our research highlights the transformative role of AI-powered digital twins in Industry 4.0 and beyond.
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References
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