Prediction of Microstructural Representativity From A Single Image

Abstract In this study, a method is presented 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. The 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. The approach is validated 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, a web‐application has been created, https://www.imagerep.io , for quick, simple and informative use of the method.