Multi-scale correlation of impact-induced defects in carbon fiber composites using X-ray scattering and machine learning.

ABSTRACT: Impact-induced defects in carbon fiber-reinforced polymers (CFRPs)-spanning from nanometer to macroscopic length scales-can be monitored using an aggregate of X-ray-based methods, but this is impractical in typical field conditions. We report on a low-velocity impacted CFRP, which is mapped using small- and wide-angle X-ray scattering and X-ray computed tomography, and employ machine learning for correlating material parameterizations derived from these techniques. The observed 1 μ m to 1 mm-sized defects are parameterized in terms of relative density and fiber orientation indicative of fiber failures (kink bands), and the nanometer sized defects in terms of crystal size and unit cell frustration. The 30 to 300 nm defects are parameterized by a power-law scattering decay, differentiating f

SUBMITTER: Sexton AH 

PROVIDER: S-EPMC11487195 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature