Digital image correlation for the evaluation of pine natural fiber composites in additive manufacturing

Abstract This research presents a protocol based on Digital Image Correlation (DIC) to evaluate the mechanical behavior of Additively Manufactured (AM) composites derived from Natural Fiber Composites (NFCs). These emerging materials, usually layer-by-layer manufactured, are susceptible to defects or inconsistencies, and often exhibit heterogeneous and anisotropic properties due to the presence of natural fibers. For this reason, to ensure quality and safety is necessary not only to know their discrete mechanical characteristics but rather to approach the problems from a stochastic perspective, for which it is necessary to know the probability distribution that can serve as input in the context of Stochastic Finite Element Models (SFEM). Additionally, DIC could enable real-time monitoring of deformation and failure mechanisms under various load conditions. Tensile tests were performed on PLA-based NFC reinforced with pine fibers specimens, using DIC as a non-contact, full-field measurement technique. The main objective of this work was to demonstrate that it is possible to generate Probability Density Functions (PDFs) for key mechanical properties (specifically Young’s Modulus and Tensile Strength), which are essential for stochastic simulations. An in-house DIC-based setup was applied to obtain the PDF for important mechanical features such as Young’s Modulus and the Tensile Stress, since the high spatial resolution of the DIC enabled the identification of deformation patterns. The results confirm that DIC enables reliable, flexible, and high-resolution testing of NFCs for AM, and can effectively support stochastic modeling approaches by providing accurate probabilistic distributions of material properties.