Prediction of Mechanical and Fracture Properties of Lightweight Polyurethane Composites Using Machine Learning Methods

This study aims to investigate the effectiveness of two machine learning methods for the prediction of the mechanical and fracture properties of Cenosphere-reinforced lightweight thermoset polyurethane composites. To evaluate the effectiveness of the models, datasets from our experimental study of composites made of five different volume fractions (0% to 40%) of Cenospheres (hollow Aluminum Silicate particles) in increments of 10% are fabricated. Experiments are conducted to determine the effect of the volume fraction of Cenospheres on Young’s modulus (both in tension and compression), percentage elongation at break, tensile strength, specific tensile strength, and fracture toughness of the composites. Two machine learning models, shallow artificial neural network (ANN) and the non-linear deep neural network (DNN), are employed to predict the above properties. A parametric study was performed for each model and optimized parameters were identified and later used to predict the properties beyond 40% volume fraction of Cenospheres. The predictions of non-linear DNN demonstrated less slope than shallow ANN and, for mass density, the non-linear DNN had unexpected predictions of increasing mass density with the addition of lighter Cenospheres. Hence, a double-hidden-layer DNN is used to predict the mass density beyond 40%, which provides the expected behavior.