Dynamic Process Control in Smart Manufacturing Systems Using Deep Q-Networks

Abstract

This research examines the use of Deep Q-Networks (DQNs) for the optimization of dynamic processes in intelligent manufacturing systems.  Utilizing real-time sensor data and adaptive decision-making, DQNs optimize essential manufacturing parameters, including production rate, energy consumption, and equipment utilization, to improve overall operational efficiency. A case study demonstrates the application of a DQN-based model in a manufacturing setting, resulting in a 15% reduction in production time and a 12% drop in energy consumption relative to conventional rule-based optimization techniques.  The model's adaptability to varying conditions, such as demand variations and equipment failures, was assessed by simulation, revealing a 20% enhancement in system responsiveness and an 18% increase in throughput.  The findings underscore the efficacy of reinforcement learning in enhancing smart manufacturing processes, delivering real-time, data-driven insights that markedly surpass traditional optimization methods. The results indicate that incorporating DQNs into industrial systems can significantly enhance operational efficiency and resource management, hence advancing Industry 4.0.