2019
DOI: 10.3390/ma12244085
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Three-Dimensional Digital Reconstruction of Ti2AlC Ceramic Foams Produced by the Gelcast Method

Abstract: A digital reconstruction technique is presented that generates three-dimensional (3D) digital representations of ceramic foams created by the foam-gelcasting technique. The reconstruction process uses information that is directly extracted from Scanning Electron Microscopy (SEM) images and offers a 3D representation of the physical sample accounting for the typically large pore cavities and interconnecting windows that are formed during the preparation process. Contrary to typical tessellation-based foam treat… Show more

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Cited by 4 publications
(3 citation statements)
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“…These meshes offer high orderliness and prove efficient for certain geometries, particularly those with well-defined boundaries. They were created using preprocessing tools such as snappyHexMesh and blockMesh in OpenFOAM, allowing for mesh generation and manipulation according to specific requirements [76].…”
Section: Ventilation Analysismentioning
confidence: 99%
“…These meshes offer high orderliness and prove efficient for certain geometries, particularly those with well-defined boundaries. They were created using preprocessing tools such as snappyHexMesh and blockMesh in OpenFOAM, allowing for mesh generation and manipulation according to specific requirements [76].…”
Section: Ventilation Analysismentioning
confidence: 99%
“…Among the various methods of reconstruction can be mentioned characteristic approaches, e.g., using support vector machines [22], based on the genetic algorithm (GA) compared with the simulated annealing (SA) and with the maximum entropy (MaxEnt) technique [23], statistical entropic descriptors (ED) [24][25][26][27], composition, dispersion, and geometry descriptors [28], two-point correlation functions and cellular automaton [29], multi-point statistics [30,31], texture synthesis [32], watershed transform and cross-correlation function [33], supervised, generative, transfer or deep learning [34][35][36][37][38], a shape library containing morphologies of heterogeneities extracted from micro-computed tomography images [39], a morphological completeness analysis [40], a successive calculation of conditional probability for multi-phase materials with any level of complexity [41], the Laguerre tessellation for ceramic foams preferably with not spherical cells [42] or using a single SEM foam image and a hybrid algorithm to the pore-sphere packing problem [43], to name just a few of them.…”
Section: Introductionmentioning
confidence: 99%
“…texture synthesis [32], watershed transform and cross-correlation function [33], supervised, generative, transfer or deep learning [3438], a shape library containing morphologies of heterogeneities extracted from micro-CT images [39], a morphological completeness analysis [40], a successive calculation of conditional probability for multi-phase materials with any level of complexity [41], the Laguerre tessellation for ceramic foams preferably with not spherical cells [42] or using a single SEM foam image and a hybrid algorithm to the pore-sphere packing problem [43], to name just a few of them.…”
Section: Introductionmentioning
confidence: 99%