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

doi:10.1007/s11665-021-05567-w

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 journal › Article › peer-review

19 Scopus citations

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 language	English
Pages (from-to)	4958-4964
Number of pages	7
Journal	Journal of Materials Engineering and Performance
Volume	30
Issue number	7
DOIs	https://doi.org/10.1007/s11665-021-05567-w
State	Published - Jul 2021
Externally published	Yes

Funding

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. 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.

Keywords

additive manufacturing
advanced characterization
nondestructive testing
optical microscopy

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Cite this

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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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AU - Keist, Jayme

AU - Jones, Griffin

AU - Reed, Rachel

AU - Reutzel, Edward

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N2 - 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.

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

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