Digital twin-driven self-adaptive reconfiguration planning method of smart manufacturing systems using game theory and deep Q-network for industry 5.0

In the Industry 5.0 era, as market demand shifts to personalization, smart manufacturing systems (SMS) with the rapid, accurate, responsive and resilient are becoming increasingly critical. To address the reconfiguration problem of SMS due to the dynamic production tasks, a digital twin-driven self-adaptive reconfiguration planning method of SMS is proposed by integrating game theory and deep reinforcement learning (DRL). Firstly, digital twin- driven self-adaptive framework for SMS is proposed to perceive production task changes for dynamically optimizing reconfiguration processes of SMS efficiently. Secondly, game theory is adopted to model the dynamic reconfiguration processes of SMS composed of multi-level reconfiguration, including system level, cell level, and machine level, where virtual manufacturing cells (VMC) as game entities will play games to reach Nash equilibrium by selecting appropriate reconfigurable machine tools (RMT) according to the proposed game strategy and utility function. Thirdly, due to the complexity of the game processes, a DRL algorithm named as deep Q-network (DQN) is used to execute the reconfiguration game for finding the optimal reconfiguration scheme to enhance the resilience of SMS. Finally, a case study is presented to demonstrate the effectiveness and adaptability of the proposed method.