A generalizable artificial intelligence tool for identification and correction of self-supporting structures in additive manufacturing processes

Marshall V. Johnson, Kevin Garanger, James O. Hardin, J. Daniel Berrigan, Eric Feron, Surya R. Kalidindi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

High mix, low volume processes such as additive manufacturing (AM) offer tremendous promise for increasing the customization in manufacturing but are hindered by the lack of efficient methods for identifying process parameters for complex new geometries exhibiting the desired performance. The search over the process space can be automated with analysis tools that can be applied in a time and resource efficient manner such that ambitious print designs are not dissuaded by the cost of process parameter discovery. In this work, we propose an image analysis tool that can classify spanning prints as one of five process-relevant archetypes, invariant of the span dimensions. We describe a modular design of the tool such that simple adjustments to image processing parameters allow for compatibility with different print processes and environments. Furthermore, we demonstrate how this tool may be incorporated into a fully automated workflow on multiple AM systems to facilitate rapid autonomous process parameter discovery and/or deeper scientific understanding.

Original languageEnglish (US)
Article number102191
JournalAdditive Manufacturing
Volume46
DOIs
StatePublished - Oct 2021

Bibliographical note

Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marshall V. Johnson reports financial support was provided by National Science Foundation. Marshall V. Johnson reports financial support and travel were provided by Air Force Research Lab. Dr. Surya R. Kalidindi reports financial support was provided by Office of Naval Research.

Funding Information:
The authors are grateful for the support of the Air Force Research Lab Minority Leader-Research Collaboration Program (UTC/AFRL) Contract FA8650-19-F-5830 , the National Science Foundation Graduate Research Fellowship Program Grant DGE-1650044 and the Office of Naval Research Grant N00014-18-1-2879 .

Publisher Copyright:
© 2021 Elsevier B.V.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Closed loop automation
  • Direct write
  • Machine learning
  • Parameter search
  • Self-supporting structures

ASJC Scopus subject areas

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

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