Toolpath design for additive manufacturing using deep reinforcement learning

Mojtaba Mozaffar, Ablodghani Ebrahimi, Jian Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Toolpath optimization of metal-based additive manufacturing processes is currently hampered by the high-dimensionality of its design space. In this work, a reinforcement learning platform is proposed that dynamically learns toolpath strategies to build an arbitrary part. To this end, three prominent model-free reinforcement learning formulations are investigated to design additive manufacturing toolpaths and demonstrated for two cases of dense and sparse reward structures. The results indicate that this learning-based toolpath design approach achieves high scores, especially when a dense reward structure is present.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Sep 29 2020


  • Additive Manufacturing
  • Deep Learning
  • Reinforcement Learning
  • Toolpath

ASJC Scopus subject areas

  • General

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