Artificial Generation of 2-D Fiber Reinforced Composite Microstructures With Statistically Equivalent Features

Abstract

Fiber reinforced composites are used widely for their high strength and low weight advantages in various aerospace and automotive applications. While their use may be sought after, modeling of these material requires increasing fidelity at the lower scales to capture accurate material behavior under loading. The first steps in creating statistically equivalent models to real life cases is developing a method of rapid evaluation and artificial microstructure generation. The outlined work is capable of tracking microscale fiber positions and determining regions of localized volume fraction extrema (high and low end). Groupings of high and low volume fraction regions are called clusters and their geometry is used to characterize the microstructure. These cluster features can be evaluated for both artificial models and actual scans, allowing correlation to be established which can ultimately be used to regenerate statistically equivalent models. The results of this work show that if one feature is to be correlated, a model can be generated which matches almost exactly. But once more features are equally taken into account, the regeneration loses accuracy.

Multiscale Process Modeling of a Carbon Fiber/Epoxy Composite for Predicting Residual Stress and Strength

“…Five periodic RVEs were generated comprised of a random dispersion of IM7 carbon fibers (fiber diameter of 6 𝜇m) in the epoxy matrix. The five microstructure renditions (Figure 3) were produced at a fiber volume fraction of 60% with the aid of a random RVE generator developed by Stapleton et al [46,47]. Perfect bonding was assumed between the fiber and the matrix.…”