Predicting the Elastic Moduli of Unidirectional Composite Materials Using Deep Feed Forward Neural Network

Abstract

Elastic moduli are important mechanical properties that describe a material’s stiffness and its deformation under elastic loading. In addition to experimental techniques, computational homogenization is commonly used for composite materials to calculate their elastic moduli. This research employs a deep learning algorithm, specifically a Feedforward Neural Network (FNN), to predict the longitudinal and transverse Young’s modulus, shear modulus, and Poisson’s ratio of various unidirectional (UD) composites. The predictions are based on several features, including the names of the composites, Young’s moduli and Poisson’s ratios of the fibers and matrices, and the fiber volume fraction. Initially, 20 different UD composites were selected from the existing literature. ANSYS-19 Material Designer was then utilized to calculate the elastic moduli of these materials while varying the fiber volume fraction from 0.2 to 0.7. This process generated a dataset of 1948 samples, with 80% of the data allocated for training the FNN model and the remaining 20% used to evaluate performance metrics of the test data. These metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R2 score. The results indicate that, with optimized hyperparameters, the FNN model can accurately predict the elastic moduli, demonstrating its effectiveness as a tool for calculating the elastic properties of UD composites.