Publications Details

Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation

Martinez, Carianne M.; Bolintineanu, Dan S.; Olson, Aaron J.; Rodgers, Theron R.; Donohoe, Brendan D.; Potter, Kevin M.; Roberts, Scott A.; Moore, Nathan W.

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) machines enable 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) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide 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.