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

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A Correction to this article was published on 14 April 2021

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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.

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Acknowledgment

The authors would like to acknowledge the Office of Naval Research Manufacturing Technology program and the Applied Research Laboratory’s Institute for Manufacturing and Sustainment Technologies, which is funded under the Naval Sea Systems Command (NAVSEA) contract #N00024-12-D-6404, as well as UES SBIR, Phase I Z4.05-6355, NASA Contract: 80NSSC20C0360. The authors also wish to thank the Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) for the use of their equipment and laboratory facilities.

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Authors and Affiliations

  1. UES Inc., Dayton, OH, USA

    Veeraraghavan Sundar & Rachel Reed

  2. The Pennsylvania State University, State College, PA, USA

    Zackary Snow, Jayme Keist, Griffin Jones & Edward Reutzel

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  1. Veeraraghavan Sundar
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  2. Zackary Snow
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Correspondence to Veeraraghavan Sundar.

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This invited article is part of a special topical focus in the Journal of Materials Engineering and Performance on Additive Manufacturing. The issue was organized by Dr. William Frazier, Pilgrim Consulting, LLC; Mr. Rick Russell, NASA; Dr. Yan Lu, NIST; Dr. Brandon D. Ribic, America Makes; and Caroline Vail, NSWC Carderock.

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Sundar, V., Snow, Z., Keist, J. et al. Flaw Identification in Additively Manufactured Parts Using X-ray Computed Tomography and Destructive Serial Sectioning. J. of Materi Eng and Perform 30, 4958–4964 (2021). https://doi.org/10.1007/s11665-021-05567-w

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  • DOI: https://doi.org/10.1007/s11665-021-05567-w

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