Reinforcement learning for generating toolpaths in additive manufacturing

Generating toolpaths plays a key role in additive manufacturing processes. In the case of 3-Dimensional (3D) printing, these toolpaths are the paths the printhead will follow to fabricate a part in a layer-by-layer fashion. Most toolpath generators use nearest neighbor (NN), branch-and-bound, or linear programming algorithms to produce valid toolpaths. These algorithms often produce sub-optimal results or cannot handle large sets of traveling points. In this paper, the researchers at Oak Ridge National Laboratory's (ORNL) Manufacturing Demonstration Facility (MDF) propose using a machine learning (ML) approach called reinforcement learning (RL) to produce toolpaths for a print. RL is the process of two agents, the actor and the critic, learning how to maximize a score based upon the actions of the actor in a defined state space. In the context of 3D printing, the actor will learn how to find t he o ptimal t oolpath t hat reduces printhead lifts and global print time.