Presentation + Paper
9 May 2024 Assessment of segmentation-induced deviations of porosity metrics in powder bed fusion additively manufactured components
Author Affiliations +
Abstract
Post processing X-ray computational tomography (CT) inspection data for additively manufactured (AM) components can induce deviations in defect quantification, affecting subsequent fatigue and failure predictions. To assess the influence and potential impact of segmentation-induced measurement deviations, this paper applies several segmentation techniques to X-ray CT data for powder bed fusion Ti-6Al-4V specimens exhibiting porosity conditions. X-ray CT reconstructions were segmented with varying techniques including Otsu’s thresholding, random forest, k-nearest neighbors, and the multilayer perceptron. Metrics such as pore size and global porosity were compared for internal validity. Then, top-down X-ray CT measurements of surface-breaking porosity were compared to optical profilometry for cross-validation.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Peter W. Spaeth, Erik L. Frankforter, Samuel J. Hocker, and Joseph N. Zalameda "Assessment of segmentation-induced deviations of porosity metrics in powder bed fusion additively manufactured components", Proc. SPIE 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII, 1295003 (9 May 2024); https://doi.org/10.1117/12.3010907
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KEYWORDS
X-rays

Image segmentation

Porosity

X-ray computed tomography

X-ray imaging

Education and training

Additive manufacturing

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