Prediction of microstructural representativity from a single image

TL;DR

This paper introduces a novel method that predicts the microstructural representativity of a material from a single image using the Two-Point Correlation function, reducing data needs and aiding material analysis.

Contribution

The authors develop a direct variance estimation technique from a single microstructural image, bypassing extensive datasets required by traditional methods.

Findings

Effective variance prediction from single images

Validated across diverse microstructures

Accessible web tool for practical use

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.