Deep Reinforcement Learning for Continuous Control of Material Thickness

To achieve the desired quality standards of certain manufactured materials, the involved parameters are still adjusted by knowledge-based procedures according to human expertise, which can be costly and time-consuming. To optimize operational efficiency and provide decision support for human experts, we develop a general continuous control framework that utilizes deep reinforcement learning (DRL) to automatically determine the main control parameters, in situations where simulation environments are unavailable and traditional PID controllers are not viable options. In our work, we aim to automatically learn the key control parameters to achieve the desired outlet thickness of the manufactured material. We first construct a surrogate environment based on real-world expert trajectories obtained from the true underlining manufacturing process to achieve this. Subsequently, we train a DRL agent within the surrogate environment. Our results suggest a Proximal Policy Optimization (PPO) algorithm combined with a Multi-Layer Perceptron (MLP) surrogate environment to successfully learn a policy that continuously changes parameter configurations optimally, achieving the desired target material thickness within an acceptable range.