Durability of large-format additively manufactured polymer composite structures with environmental exposure–accelerated water immersion
Published: 2025
Publication Name: Composites Part C: Open Access
Publication URL: https://doi.org/10.1016/j.jcomc.2025.100659
Abstract:
Large-format additive manufacturing (LFAM) of polymer composites enables rapid production of large-scale components for infrastructure, transportation, and defense. As these components see increased outdoor use, understanding their durability under moisture exposure is critical. This study evaluates the effects of water immersion on the durability of LFAM composites using three material systems: carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS), glass fiber reinforced polyethylene terephthalate glycol (GF-PETG), and wood flour reinforced amorphous polylactic acid (WF-aPLA). Specimens were fabricated using a pellet-fed extrusion-based LFAM process and immersed in water for 30, 60, and 90 days at three temperatures. Moisture uptake and mechanical degradation were assessed in both longitudinal and through-thickness orientations to capture the influence of interlayer interfaces. Results show that bio-based WF-aPLA absorbed significantly more moisture than petroleum-based CF-ABS and GF-PETG and exhibited ongoing degradation that prevented saturation. The most severe mechanical losses occurred in the through-thickness direction, where more interbead interfaces and voids were present. Longitudinal specimens showed better retention of strength and stiffness. Mechanical property degradation progressed in two stages: an initial rapid phase following an Arrhenius relationship with inverse temperature, and a slower secondary phase that deviated from this behavior. The findings demonstrate that both material selection and build orientation significantly affect moisture durability. While petroleum-based composites performed better overall, their durability remains influenced by LFAM-induced anisotropy. These results support material selection and predictive modeling for reliable LFAM structures in outdoor environments.