A multi-scale digital image correlation framework for large-strain fabric deformation measurement

Characterizing large-strain, anisotropic fabric deformation is challenging due to complex texture evolution and localized strain gradients. This study proposes a Fabric-Adaptive Digital Image Correlation (FA-DIC) framework to address these issues. FA-DIC integrates three key innovations: a hierarchical multi-scale strategy to resolve texture-related ambiguities, an adaptive regularization driven by strain gradients to preserve local features while suppressing noise, and an anisotropy-aware strain computation that incorporates fabric principal directions to correct isotropic bias. Validated through synthetic and experimental uniaxial tension tests on woven and knitted fabrics, FA-DIC demonstrates superior performance over reference methods, delivering more consistent full-field strain maps with reduced displacement errors. The framework provides a reliable and robust approach for the mechanical characterization of soft, deformable materials.