Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs)

This paper aims to strengthen composites by treated and untreated date palm fibers (PDF), with sodium hydroxide (NaOH), for light applications. With 75% cellulose content and a density of 1.2 g/cm(3), the palm fibers were exposed to a preparatory treatment with 1.5% NaOH for 24 h prior to integration into a polyester. Four polyester samples comprising 30% of palm fiber were manufactured. Additionally, the palm fiber interface was evaluated using scanning electron microscopy (SEM) and optical microscopy. The specimens underwent mechanical testing and it shows that tensile (18% increase in stress and 1.2% increase in Young's modulus) and flexural properties (20% increase in strength and 10% increase in Young's modulus) of treated composites as compared with untreated fibers. A MATLAB-based Artificial Neural Network (ANN) model was applied to estimate stress and strain at break as well as the Young's modulus, based on three input characteristics: section, sample length, and chemical treatment. It was obtained that the polyester reinforced by NaOH-treated palm fibers increased the mechanical characteristics relative to the untreated fibers. The coefficient of determination R-2 in the ANN models is 0.87. These results suggest that the ANN model is a useful tool for predicting mechanical properties.