Fast three-dimensional rules-based simulation of thermal-sprayed microstructures

Rodgers, Theron; Mitchell, John; Olson, Aaron; Bolintineanu, Dan; Vackel, Andrew; Moore, Nathan W.

doi:10.1016/j.commatsci.2021.110437

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…• Measuring spatial statistical properties in samples to enable model tuning to produce synthetic realizations including the SPPARKS thermal-spray model [10]. • Establishing efficient simulation of 2D mesostructures [11] to serve as a surrogate of 3D analysis for evaluating various properties that depend on mesostructure.…”

Section: Thermal-sprayed Coatingsmentioning

confidence: 99%

“…Several software packages are publicly available to generate synthetic X-rays and CT scans of numerical objects. For this work, we use the ASTRA Toolbox Python library [27] to generate synthetic X-ray CT scans of the simulated microstructures generated from the SPPARKS thermal spray library [10]. The Python API available in ASTRA toolbox allows for GPU-accelerated image processing and reconstruction with several tunable settings such as detector size, number of X-ray projections, and source and detector position relative to the numerical object.…”

Section: Synthetic Ct Scansmentioning

confidence: 99%

“…The range of total pore fractions in this second set varied between 0.02 and 0.05. All synthetic data was generated using the Thermal Spray app in the SPPARKS microstructure simulation library [10]. The thermal spray model generates synthetic microstructures at experimentally relevant length-scales using a rules-based model of particle incidence, spreading, and solidification during thermal spray processes.…”

Section: Synthetic Training Dataset Developmentmentioning

confidence: 99%

“…With the low, base and high porosity segmentations as bounds, we compute several metrics that quantify the pore morphology. In the present context, the nature of these metrics and their physical implications are not discussed in detail; the interested reader is referred to other works for this discussion [10]. Here, these metrics are only presented to illustrate quantitative differences arising from image segmentation.…”

Section: Analysis Of Pore Characteristicsmentioning

confidence: 99%

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

et al. 2023

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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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Section: Thermal-sprayed Coatingsmentioning

confidence: 99%

Section: Synthetic Ct Scansmentioning

confidence: 99%

Section: Synthetic Training Dataset Developmentmentioning

confidence: 99%

Section: Analysis Of Pore Characteristicsmentioning

confidence: 99%

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