Effect of Microstructural Features’ Volume Fraction and Geometry on Digital Volume Correlation Analysis

Abstract

Digital volume correlation (DVC) is widely used to extract internal displacement and strain fields from X-ray computed tomography (XCT) data, yet the limits imposed by the material’s own texture remain poorly quantified. This paper quantifies the individual and combined effects of particle volume fraction, particle geometry, and imaging noise on DVC accuracy. Sixteen specimens with prescribed volume fractions (0.25–10%) and particle shapes (spherical and angular) were generated using the discrete element method (DEM), loaded elastically in uniaxial compression, and converted into synthetic three-dimensional image datasets with and without additive Gaussian noise. Global DVC was performed in AVIZO and compared against the exact DEM ground truth. Under noise-free conditions, mean nodal displacement errors fall below 5% once the volume fraction exceeds 1%, whereas errors of 10–25% occur below this value; adding Gaussian noise with a variance of 0.001 raises the practical threshold to approximately 4%. Angular particles consistently outperform spherical particles at an equal volume fraction, a difference explained quantitatively by their 1.4-times-larger specific interfacial area and correspondingly higher image gradient. Median filtering favours spherical microstructures, whereas the Non-Local Means filter performs consistently across all microstructures. The results provide a priori guidelines for assessing DVC feasibility directly from microstructural descriptors.