Flaw Identification in Additively Manufactured Parts Using X-ray Computed Tomography and Destructive Serial Sectioning

Veeraraghavan Sundar, Zackary Snow, Jayme Keist, Griffin Jones, Rachel Reed, Edward Reutzel

Research output: Contribution to journalArticlepeer-review

Abstract

In additive manufacturing (AM), internal flaws that form during processing can have a detrimental impact on the resulting fatigue behavior of the component. Nondestructive x-ray computed tomography (XCT) has been routinely used to inspect AM components. This technique, however, is limited by what is resolvable as well as the automated procedures available to analyze the data. In this study, we compared XCT scans and automated flaw recognition analysis of the corresponding data to results obtained from an automated mechanical polishing-based serial sectioning system. Although internal porosity and surface roughness were easily observed by serial sectioning with bright-field optical microscopy, the same level of information could not be obtained from the XCT data. For the acquisition parameters used, XCT had only a 15.7% detection rate compared to that of serial sectioning. The results point to the need to recognize the limitations of XCT and for supplementary XCT scan quality metrics in addition to the voxel size.

Original languageEnglish (US)
Pages (from-to)4958-4964
Number of pages7
JournalJournal of Materials Engineering and Performance
Volume30
Issue number7
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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