Abstract
An accurate knowledge of the 3D polycrystalline microstructure of a material is crucial to its property prediction, performance optimization, and design. Here, we present a multi-scale computational scheme that allows one to stochastically reconstruct the 3D microstructure of a highly heterogeneous polycrystalline material with large variation in grain size, morphology, and spatial distribution, as well as the distribution of second-phase particles, from single-2D electron back-scattered diffraction (EBSD) micrograph. Specifically, the two-point correlation functions S2 are employed to statistically characterize grain morphology, orientation, and spatial distribution and are incorporated into the simulated annealing procedure for microstructure reconstruction. During the reconstruction, the original polycrystalline microstructure is coarsened such that the large grains are reconstructed first and the smaller ones are generated later. The second-phase particles are then inserted into the reconstructed polycrystalline material based on the pair-correlation function g2 sampled from the 2D back-scattered electron micrograph. The utility of our multi-scale scheme is demonstrated by successfully reconstructing a highly heterogeneous polycrystalline Sn-rich solder joint with Cu6Sn5 intermetallic particles. The accuracy of our reconstruction is ascertained by comparing the virtual microstructure with the actual 3D structure of the joint obtained via serial sectioning techniques.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1440-1450 |
| Number of pages | 11 |
| Journal | Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science |
| Volume | 47 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 1 2016 |
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ASJC Scopus subject areas
- Condensed Matter Physics
- Metals and Alloys
- Mechanics of Materials
Cite this
Stochastic Multi-Scale Reconstruction of 3D Microstructure Consisting of Polycrystalline Grains and Second-Phase Particles from 2D Micrographs. / Chen, Shaohua; Kirubanandham, Antony; Chawla, Nikhilesh; Jiao, Yang.
In: Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, Vol. 47, No. 3, 01.03.2016, p. 1440-1450.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Stochastic Multi-Scale Reconstruction of 3D Microstructure Consisting of Polycrystalline Grains and Second-Phase Particles from 2D Micrographs
AU - Chen, Shaohua
AU - Kirubanandham, Antony
AU - Chawla, Nikhilesh
AU - Jiao, Yang
PY - 2016/3/1
Y1 - 2016/3/1
N2 - An accurate knowledge of the 3D polycrystalline microstructure of a material is crucial to its property prediction, performance optimization, and design. Here, we present a multi-scale computational scheme that allows one to stochastically reconstruct the 3D microstructure of a highly heterogeneous polycrystalline material with large variation in grain size, morphology, and spatial distribution, as well as the distribution of second-phase particles, from single-2D electron back-scattered diffraction (EBSD) micrograph. Specifically, the two-point correlation functions S2 are employed to statistically characterize grain morphology, orientation, and spatial distribution and are incorporated into the simulated annealing procedure for microstructure reconstruction. During the reconstruction, the original polycrystalline microstructure is coarsened such that the large grains are reconstructed first and the smaller ones are generated later. The second-phase particles are then inserted into the reconstructed polycrystalline material based on the pair-correlation function g2 sampled from the 2D back-scattered electron micrograph. The utility of our multi-scale scheme is demonstrated by successfully reconstructing a highly heterogeneous polycrystalline Sn-rich solder joint with Cu6Sn5 intermetallic particles. The accuracy of our reconstruction is ascertained by comparing the virtual microstructure with the actual 3D structure of the joint obtained via serial sectioning techniques.
AB - An accurate knowledge of the 3D polycrystalline microstructure of a material is crucial to its property prediction, performance optimization, and design. Here, we present a multi-scale computational scheme that allows one to stochastically reconstruct the 3D microstructure of a highly heterogeneous polycrystalline material with large variation in grain size, morphology, and spatial distribution, as well as the distribution of second-phase particles, from single-2D electron back-scattered diffraction (EBSD) micrograph. Specifically, the two-point correlation functions S2 are employed to statistically characterize grain morphology, orientation, and spatial distribution and are incorporated into the simulated annealing procedure for microstructure reconstruction. During the reconstruction, the original polycrystalline microstructure is coarsened such that the large grains are reconstructed first and the smaller ones are generated later. The second-phase particles are then inserted into the reconstructed polycrystalline material based on the pair-correlation function g2 sampled from the 2D back-scattered electron micrograph. The utility of our multi-scale scheme is demonstrated by successfully reconstructing a highly heterogeneous polycrystalline Sn-rich solder joint with Cu6Sn5 intermetallic particles. The accuracy of our reconstruction is ascertained by comparing the virtual microstructure with the actual 3D structure of the joint obtained via serial sectioning techniques.
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UR - http://www.scopus.com/inward/citedby.url?scp=84957439112&partnerID=8YFLogxK
U2 - 10.1007/s11661-015-3283-8
DO - 10.1007/s11661-015-3283-8
M3 - Article
AN - SCOPUS:84957439112
VL - 47
SP - 1440
EP - 1450
JO - Metallurgical and Materials Transactions B
JF - Metallurgical and Materials Transactions B
SN - 0360-2141
IS - 3
ER -