The influence of internal defects on the fatigue behavior of 4032D polylactic acid in fused deposition modeling

Abstract

The impact of microstructural defects on the service performance of structural components produced via additive manufacturing is a critical challenge. This study introduces an innovative method for quantitatively characterizing internal defects and predicting the fatigue performance of polylactic acid (PLA) parts fabricated by Fused Deposition Modeling (FDM). High-resolution computed tomography (CT) is employed to accurately map the location, size, and morphology of internal defects in FDM-processed PLA. Monotonic tensile and fatigue tests are conducted to assess the influence of PLA specimens on fatigue behavior. A novel fatigue life prediction framework is developed by integrating defect size and structural characteristics, utilizing both extreme value statistics and the Murakami model to predict the fatigue limit. The results show that the Gumbel distribution effectively describes the internal defect size distribution, with the largest detected defect measuring up to 4.09 μm. The fatigue limit prediction exhibits only a 6.77 % discrepancy between model calculations and experimental results. Additionally, an enhanced X-parameter-based fatigue life assessment model is proposed, which significantly improves the reliability of fatigue life predictions for FDM-fabricated 4032D PLA specimens.