Automation of polymer pressing by robotic handling with in-process parameter optimization

In this study, we introduce an autonomous system for polymer pressing that integrates robotic manipulation, specialized equipment, and machine learning optimization. This system aims to significantly reduce lead time and human labor in polymer-materials development. Our approach utilizes an arm-type robot to handle polymer beads and operate a press machine, with process parameters autonomously determined by Bayesian optimization. The keys to this automation are custom-designed press tools that are suitable for robotic handling, such as press plates or fork, a gripper—tool interface with tapered convex and concave parts that enables the handling of multiple tools by a single robot gripper, and an integrated control system that synchronizes the robot with the press machine. Additionally, we implement a closed-loop process that incorporates image processing for pressed-polymer recognition and Bayesian optimization for continuous parameter refinement, with an evaluation function that considers polymer-film thickness and press times. Verification experiments demonstrate the capability of the system to autonomously execute pressing operations and effectively propose optimized press parameters.