Reinforcement Learning for Generating Toolpaths in Additive Manufacturing

Conference · Wed Aug 01 00:00:00 EDT 2018

OSTI ID:1474597

Patrick, Steven  ^[1]; Nycz, Andrzej  ^[1]; Noakes, Mark  ^[1]; Gaul, Katherine  ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1474597
• Resource Relation: Conference: International Solid Freeform Fabrication Symposium (SFF) - Austin, Texas, United States of America - 8/13/2018 4:00:00 AM-8/15/2018 4:00:00 AM
• Country of Publication: United States
• Language: English