Numerical generation of random fiber bundles and the influence of microstructural properties on mechanical behavior

Abstract

Understanding the mechanical behavior of quasi-parallel fiber networks is essential for improving the manufacturing processes of fiber-reinforced composites. Mesoscale models of dry yarns and reinforcements require constitutive laws that accurately reflect the heterogeneous microstructure of fiber bundles. This study aims to develop a numerical generator of random fiber bundles for microscopic parametric studies of compaction behavior. A real fiber bundle was first reconstructed from X-ray microtomography data, and the numerical strategy was validated by tracking fiber cross-sections along the bundle length, with a fiber-position error of 5.2%. Based on this validated framework, an experiment-independent generator was established to create parameterized fiber bundles. The generated bundles reproduced the experimental compaction response with good agreement. Parametric results showed that increasing fiber waviness enhances inter-fiber interactions, increases transverse stiffness, and requires a higher load to reach the same fiber volume fraction. This framework provides a useful microscopic basis for studying fiber-bundle deformation mechanisms and for developing future mesoscopic constitutive laws.