Controlling Product Properties in Forming Processes Using Reinforcement Learning—An Application to V-Die Bending

Uncertainty is unavoidable in forming processes due to fluctuating properties in the semi-finished product, the tool system and the environment. For this reason, numerous scientists have addressed this issue by developing control approaches like self-optimizing machine tools or the control of product properties. Machine learning algorithms, in particular reinforcement learning (RL) methods, show promising results for controlling production processes in this way. In this paper, the application of RL is demonstrated on an industrially commonly used process, V-die bending. For this purpose, first a flexible tool system is developed that allows the bending angle to be adjusted continuously between 80 and 110°. The developed tool is initially simulated through an FEM model in order to create a sufficient database for the training of an RL agent for springback compensation. The pre-trained agent is then used to control the springback in the real process. To close the resulting sim-to-real gap, it is then retrained on the experimentally generated data. It is shown that the springback can be significantly reduced compared to the uncontrolled case in both the simulative and the experimental process.