Template-bayesian approach for the evaluation of melt pool shape and dimension of a DED-process from in-situ X-ray images

Adrian Lindenmeyer, Samantha Webster, Michael F. Zaeh, Kornel F. Ehmann, Jian Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Directed Energy Deposition (DED) is a highly localized process in which metal powders are added to a laser-induced molten pool. The shape and size of the melt pool ultimately determine the local cooling/solidification rate and, thus, the material's microstructure and properties. To study melt pool shape, in situ X-ray imaging techniques have been used. However, the data afterwards typically are manually analysed, which creates a bottleneck in understanding fundamental phenomena in DED. Here, a promising method to automatically extract melt pool shape and dimensions from in situ X-ray DED melt pool images using templates and Bayesian reasoning is proposed.

Original languageEnglish (US)
JournalCIRP Annals
DOIs
StateAccepted/In press - 2021

Keywords

  • Additive manufacturing
  • Bayesian image recognition
  • Methodology

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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