Mechanical characterization and crack propagation in Additively Manufactured Polymers using Digital Image Correlation: a review

Abstract

While Additive Manufacturing (AM) of polymers has matured from rapid prototyping to functional production, the layer-wise fabrication process introduces significant mechanical anisotropy and microstructural heterogeneity, which complicates conventional mechanical characterization. This review examines the applicability of Digital Image Correlation (DIC) as a full-field, non-contact metrological tool for mapping strain with sub-pixel precision across three domains: (1) the fundamental metrological principles of DIC applied to anisotropic AM structures, (2) a critical synthesis of DIC applications in tensile, fracture, fatigue, and impact testing, and (3) emerging advances in data acquisition, including in-situ monitoring and AI-driven frameworks. DIC uniquely enables the direct visualization of localized strain concentrations at filament interfaces and non-ideal crack propagation paths that conventional point-wise sensors obscure. Technological maturation is increasingly driven by Deep DIC frameworks and neural operators ( DisplacementNet, StrainNet), which now integrate with automated defect tracking systems. Furthermore, multimodal approaches combining DIC with Acoustic Emission (AE) and Micro-Computed Tomography (µ-CT), alongside volumetric Digital Volume Correlation (DVC), extend damage characterization from surface observations to internal defect evolution. To support industrial certification in safety-critical sectors, the community must adopt standardized metrological baselines, including the Metrological Efficiency Indicator (MEI) and the iDICs Good Practices Guide. These protocols will bridge the gap between as-designed simulations and as-built experimental validation, positioning DIC as a foundational technology for Industry 4.0 and NDE 4.0 paradigms.