Reinforcement Learning Approach to Vibration Compensation for Dynamic

Vibration compensation is critical in many fields, particularly in the machine tool industry where it can lead to enhanced machining precision and prolonged component lifespan. This paper aims to address the shortcomings of current vibration damping methods, which often rely on precise dynamic models. We propose a novel reinforcement learning-based approach for vibration compensation tailored to machine tool axes. The method encompasses the problem formulation, the detailed solution strategy, and its implementation, followed by experimental validation using industrial machine tool hardware and control systems. Our findings indicate that the proposed reinforcement learning technique effectively compensates for vibrations in dynamic drive systems with initially unknown system parameters. The results showcase improvements in accuracy and efficiency in vibration management within dynamic feed drive systems, highlighting the applicability of AI-based methods in enhancing traditional manufacturing processes.