Reinforcement Learning for Generating Toolpaths in Additive Manufacturing
By:
Patrick, Steven D; Nycz, Andrzej ; Noakes, Mark W; Gaul, Katherine T
Page Number:
TBD
Book Title:
Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium
Publication Date:
August 2018
Publisher Location:
University of Texas at Austin, Texas, United States of America
Conference Name:
International Solid Freeform Fabrication Symposium (SFF)
Conference Location:
Austin, Texas, United States of America
Conference Sponsor:
Laboratory for Freeform Fabrication and University of Texas at Austin
Abstract
Generating toolpaths plays a key role in additive manufacturing processes. In the case of 3-Dimensional (3D) printing, these toolpaths are the pathways 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 player and the critic, learning how to maximize a score based upon the actions of the player in a defined state space. In the context of 3D printing, the player will learn how to find the optimal toolpath that reduces printhead lifts and global print time.
