Prediction and Optimization of Mechanical Properties of Polypropylene/Waste Tire Powder Blends using a Hybrid Artificial Neural Network-Genetic Algorithm (GA-ANN)

Abstract

Blends of Polypropylene (PP) and waste ground rubber tire powder are studied with respect to the effect of ethylene—propylene—diene monomer (EPDM) and polypropylene grafted maleic anhydride (PP-g-MA) compatibilizer content by using the Design of Experiments methodology, whereby the effect of the four polymers content on the final mechanical properties are predicted. Uniform design method is especially adopted for its advantages. Optimization is done using hybrid Artificial Neural Network-Genetic Algorithm technique. A rubber formulary with respect to the four ingredients are optimized having maximum tensile strength and then compared with a blend predicted to have maximum elongation at break. It is concluded that the blends show fairly good properties provided that it has a relatively higher concentration of PP-g-MA and EPDM content. SEM investigations also corroborates with the observed mechanical properties. A quantitative relationship is then shown between the material concentration and the mechanical properties as a set of contour plots, which are then tested and confirmed experimentally to conform to the optimum blend ratio.