Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems
This addresses vibration damping for the machine tool industry, but appears incremental as it applies an existing RL method to a specific domain problem.
The paper tackled vibration compensation in machine tool axes to improve machining precision and component lifetime, presenting a reinforcement learning approach that was implemented and tested on industrial hardware.
Vibration compensation is important for many domains. For the machine tool industry it translates to higher machining precision and longer component lifetime. Current methods for vibration damping have their shortcomings (e.g. need for accurate dynamic models). In this paper we present a reinforcement learning based approach to vibration compensation applied to a machine tool axis. The work describes the problem formulation, the solution, the implementation and experiments using industrial machine tool hardware and control system.