Artificial neural networks applied to epoxy composites reinforced with carbon and E-glass fibers: Analysis of the shear mechanical properties

Inspired by the biological nerve system, artificial neural networks (ANN) have been tools of artificial intelligence for data classification and pattern recognition, and can be used to simulate a wide variety of non-linear complex scientific systems. Artificial neural networks are being used in medical applications; image recognition and control of dynamic systems, but only recently have been considered for the prediction of the mechanical behavior of materials and particularly composites. In this work, ANNs were considered specifically to predict the shear stress–strain behavior from carbon fiber/epoxy and glass fiber/epoxy composites. A multilayered neural network perceptron (MLP) architecture was used, and the results showed that the application of the Levenberg–Marquardt learning algorithm leads to a high predictive quality to epoxy composites, i.e. nearly 80% of standard error of prediction was found to be ≥0.9. The initial tests considered a simple architecture 3-[3-3]2-1 resulting in low predictive quality. However, increasing the number of neurons in the hidden layers and the number of training instances resulted in an enhancement of the neural network predictive quality.