Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks

Mojtaba Mozaffar, Shuheng Liao, Hui Lin, Kornel Ehmann, Jian Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Additive manufacturing (AM) is commonly used to produce builds with complex geometries. Despite recent advancements in data-driven modeling of AM processes, the generalizability of such models across a wide range of geometries has remained a challenge. Here, a graph-based representation using neural networks is proposed to capture spatiotemporal dependencies of thermal responses in AM processes. Our results tested on the Directed Energy Deposition process, indicate that our deep learning architecture accurately predicts long thermal histories for unseen geometries in the training process, offering a viable alternative to expensive computational mechanics or experimental solutions.

Original languageEnglish (US)
Article number102449
JournalAdditive Manufacturing
Volume48
DOIs
StatePublished - Dec 2021

Keywords

  • Additive manufacturing
  • Graph neural network
  • Machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • General Materials Science
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks'. Together they form a unique fingerprint.

Cite this