.png){kind=icon}
Return
Microstructure classification and the polycrystalline microstructure state space
.png){kind=icon}
PRE
AI
DOI：10.1016/j.actamat.2025.121219.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A cornerstone of materials science is that material properties are determined by their microstructure. While the community has already developed a wide variety of approaches to describe microstructure, most of these are tailored to specific material systems or classes. This work proposes a way to quantitatively measure the similarity of microstructures based on the geometry of the grain boundary network, a feature which is fundamental to and characteristic of all polycrystalline materials. Specifically, a distance on all single-phase polycrystalline microstructures is proposed such that two microstructures that are close with regard to the distance have grain boundary networks that are statistically similar in all geometric respects below a user-specified length scale. Given a pair of micrographs, the distance is approximated by sampling windows from the micrographs, defining a distance between pairs of windows, and finding a window matching that minimizes the sum of the pairwise window distances. The approach is used to compare a variety of synthetic microstructures and to develop a procedure to query a proof-of-concept database suitable for general single-phase polycrystalline microstructures.
Keywords:
Microstructure
Grain boundaries
Wasserstein metric
Journal
IF:
9.3
Papers:
2.0W
Citations:
12.9W
Organization
Cited Papers
ShareSaveEvolutionary Dynamics and Age-Dependent Pathogenesis of Sub-Genotype VI.2.1.1.2.2 PPMV-1 in Pigeons.png)
VIRUSES-BASEL
OAAI
VIRUSES-BASELIF3.5
OAAIShareSaveOverview: Machine Learning for Segmentation and Classification of Complex Steel MicrostructuresMetals
OAAI
MetalsIF0
OAAIShareSaveShareSaveShareSave
View more