ENHANCING DIGITAL TWINS WITH DEEP REINFORCEMENT LEARNING : A USE CASE IN MAINTENANCE PRIORITIZATION
This paper introduces an innovative framework that enhances digital twins with deep reinforcement learning (DRL) to support maintenance in manufacturing systems. Utilizing a sophisticated artificial intelligence (AI) layer, this framework integrates real-time and historical production data from a physical manufacturing system to a digital twin, enabling dynamic simulation and analysis. Maintenance
