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Title: Reinforcement Learning for Generating Toolpaths in Additive Manufacturing

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1474597
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
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

Citation Formats

Patrick, Steven, Nycz, Andrzej, Noakes, Mark, and Gaul, Katherine. Reinforcement Learning for Generating Toolpaths in Additive Manufacturing. United States: N. p., 2018. Web.
Patrick, Steven, Nycz, Andrzej, Noakes, Mark, & Gaul, Katherine. Reinforcement Learning for Generating Toolpaths in Additive Manufacturing. United States.
Patrick, Steven, Nycz, Andrzej, Noakes, Mark, and Gaul, Katherine. 2018. "Reinforcement Learning for Generating Toolpaths in Additive Manufacturing". United States. https://www.osti.gov/servlets/purl/1474597.
@article{osti_1474597,
title = {Reinforcement Learning for Generating Toolpaths in Additive Manufacturing},
author = {Patrick, Steven and Nycz, Andrzej and Noakes, Mark and Gaul, Katherine},
abstractNote = {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.},
doi = {},
url = {https://www.osti.gov/biblio/1474597}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 01 00:00:00 EDT 2018},
month = {Wed Aug 01 00:00:00 EDT 2018}
}

Conference:
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