Abstract:
In this paper, several predictive models are tested for material property predictions of two-ply homogenized carbon fiber
composites. The uncertainty of the constituent material properties yields uncertainties in the entries of the ABD stiffness
matrix. These variations in the ABD stiffness entries are attempted to be captured using predictive models, namely, artificial
neural networks and polynomial regression. Using Latin-Hypercube sampling technique, the constituent (fiber and resin)
parameters are randomly sampled in the input space. For each entry in the input space, an ABD stiffness matrix is generated
using a multiscale modeling technique and stored in the database as the output. Based on error estimates, the accuracy of
predictions is evaluated using cross-validation on test folds. The non-zero entries in the A and D submatrices are observed to
have very small prediction errors, whereas very small values appearing in B submatrix due to non-symmetric tow material
properties are ignored. It is found that for the composite considered in this work, the linear regression model yields the
highest accuracy whereas Neural network predictions are ranked second. This observation is justified as model training and
testing were performed with less than a thousand data points, which is comparatively a low number for an artificial neural
network model.
