Prediction of microstructural representativity from a single image
Abstract
In this study, we present a method for predicting the representativity of the phase fraction observed in a single image (2D or 3D) of a material. Traditional approaches often require large datasets and extensive statistical analysis to estimate the Integral Range, a key factor in determining the variance of microstructural properties. Our method leverages the Two-Point Correlation function to directly estimate the variance from a single image, thereby enabling phase fraction prediction with associated confidence levels. We validate our approach using open-source datasets, demonstrating its efficacy across diverse microstructures. This technique significantly reduces the data requirements for representativity analysis, providing a practical tool for material scientists and engineers working with limited microstructural data. To make the method easily accessible, we have created a web-application, www.imagerep.io, for quick, simple and informative use of the method.
Cite
@article{arxiv.2410.19568,
title = {Prediction of microstructural representativity from a single image},
author = {Amir Dahari and Ronan Docherty and Steve Kench and Samuel J. Cooper},
journal= {arXiv preprint arXiv:2410.19568},
year = {2025}
}