Abstract
This work presents a framework for interfacing a reinforcement learning algorithm with a finite element model in order to develop an artificial neural network controller. The goal of the controller is accelerating the hot compression process of the titanium aluminide TNM-B1. The reinforcement learning algorithm interacts with the finite element model by exploring different die velocities and receiving input measurements (the velocity, displacement and force of the die) while collecting rewards if a constant stress state in the workpiece is achieved. Synthetic stochastic material behavior was used to simulate the observed variations in deformation behavior of TNM-B1. The same reinforcement learning setup and reward function was able to adapt to two example finite element environments; the compression of a simple cylinder workpiece between flat dies and the compression of a more complex bone workpiece between flat dies. The performance of the controller for the bone compression environment was comparatively reduced and less consistent. In addition, training times and training instability were significantly increased. Furthermore, the results suggest that the framework can be used as a tool to find process optimizations or alternative process routes. This work demonstrates the concept and provides the groundwork and fundamentals for transferring the method to a physical setup.