Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Martinez, Carianne; Bolintineanu, Dan S.; Olson, Aaron; Rodgers, Theron; Donohoe, Brendan; Potter, Kevin M.; Roberts, Scott A.; Pokharel, Reeju; Forrest, Stephanie; Moore, Nathan W.

doi:10.1007/s00466-023-02345-4

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Journal Article · Wed May 31 00:00:00 EDT 2023 · Computational Mechanics

DOI:https://doi.org/10.1007/s00466-023-02345-4· OSTI ID:1975896

Martinez, Carianne; Bolintineanu, Dan S.; Olson, Aaron; ; Donohoe, Brendan; Potter, Kevin M.; Roberts, Scott A.; Pokharel, Reeju; Forrest, Stephanie; Moore, Nathan W.

Abstract

Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NA0003525; AC02-06CH11357

OSTI ID:: 1975896

Journal Information:: Computational Mechanics, Journal Name: Computational Mechanics Journal Issue: 3 Vol. 72; ISSN 0178-7675

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: Germany

Language:: English

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