A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment
提出一种基于深度强化学习的实时动态参数自适应控制方法，用于优化热冲压工艺，在批量生产中减少缺陷率并缩短成形时间，相比传统静态控制方法提升超过20%的效率。
Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.