Attaining material sustainability by incorporating nanoparticles additives to improve the mechanical properties of polypropylene composites: data driven modelling

Abstract

Abstract Polypropylene is commonly employed in several industrial applications such as packaging, cable insulation and automotive. Research interest has focused on how to improve its mechanical properties to reduce the effect of low impact toughness of polypropylene. One of the sustainable ways to achieve this is by incorporating graphene nanoplatelets to form a composite. This study investigates the application of a hybrid support vector machine (SVM) and artificial neural networks (ANN) model to predict the effect of incorporating graphene on the mechanical properties of polyproline composites. The effect of parameters such as maleic anhydride grafted polypropylene (MAPP), Talc, and exfoliated graphene nanoplatelets on the tensile strength and modulus of the polypropylene composites was modelled by using ANN. Testing various topologies was accomplished. An optimized ANN structure of 3-7-2 indicating 3 input-layer, 7 hidden layer, and 2 output-layer was tested. Both the SVM and the ANN predict well the mechanical properties of polyproline composites. However, the ANN with R2 of 0.999 offers the best predictions.