Citation information: Xu, Hongyi, et al. "Descriptor-based methodology for statistical characterization and 3D reconstruction of microstructural materials.
McCormick Robert R. McCormick School of Engineering and Applied Science
Descriptor-based methodology for statistical characterization and 3D reconstruction of microstructural materials Hongyi Xu
Citation information: Xu, Hongyi, et al. "Descriptor-based methodology for statistical characterization and 3D reconstruction of microstructural materials." Computational Materials Science 85 (2014): 206-216. 1
Overview of Characterization and Reconstruction Image pre-processing
Dispersion ο± Mean of Nearest Distances, ππ ο± Variance of Nearest Distances, ππ π―ππ« rd
Geometry ο± Average Radius, rc ο± Elongation ration, el ο± Orientation (random)
ππ = ππ π ππ = π
a
b
ο±: orientation 3
Microstructure Characterization (1)
Original SEM/TEM image (1)
Composition Dispersion
Pre-defined volume fraction VF
Aggregateβs mean radius rc
Geometry
(2) Binary image (2)
(3)
Matrix-shielded grey scale image (3)
Aggregates counting Nearest distance nd
(4)
Mark aggregateβs centers (4) Distribution of aggregatesβ aspect ratio (Ο)
(5)
Isolated-aggregate image (5)
Distribution of aggregatesβ radius rc
4
Composition: VF ο± Binary image is obtained by setting a greyscale threshold ο± Binary image corresponds to a thin top layer of the material, with thickness of π· = 2 Γ ππ , D is filler diameter *
Dispersion: Nearest Center Distances ο± Screen out the unnecessary information (greyscale values of the matrix phase), get matrix-shielded image
Original Image
x binary image matrixshielded image
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Finding Aggregatesβ Centers
ο± Calculate the dispersion status (distribution of nearest center distance). 7
Geometry: Aspect Ratio & Equivalent Radius ο± Objective: characterize aggregatesβ geometry accurately ο± Method: pick out the βsingle aggregate clusterβ(marked by green circles), to calculate the particle shape descriptors.
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Summary of Microstructure Descriptors Category
Descriptors
Predefined descriptors
π½π ππ_ππ«
2D descriptors characterized from 2D images
πππ« π¨π
π΅ππ« ππ_ππ« 3D descriptors to be predicted
πππ« π½π π΅ππ« 9
Predicting Aggregate Number in 3D Space
Num = π2π· Γ
πΏ π·
L: side length of the 3D cube D: depth of the binary 2D image (D)
D
D
L
D: diameter Observed Particle Centers
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Predicting Aspect Ratio in 3D Space π 3π·
π3π·
90Β°
D π 2π· π2π·
0Β° 2D projection: 2Γ π 2π·
Assumptions: 1. Isotropic material 2. Ellipsoidal Geometry. The two short semi-axis is equal length. 3D-to-2D projection:
Summary ο± Developed methods of characterization based on 2D SEM images ο± Developed fast 3D descriptor-based microstructure reconstruction algorithm and applied it on polymer nanocomposites
ο± Verified the accuracy of the developed method using two case studies ο± Reconstruct 3D polymer nanocomposites microstructures based on only one 2D microscopic image
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McCormick Robert R. McCormick School of Engineering and Applied Science
NU-GT Carbon Black Project
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Matrix-shielded Image for Aggregate Center Marking 2. Binary image