Development and characterization of glass/polyester composites filled with industrial wastes using statistical techniques

Abstract

A new class of polyester composites reinforced with E-glass fiber and filled with different weight proportions of granite and fly ash are fabricated. Mechanical and tribological properties of the composites are evaluated and compared. Between the two fillers chosen, fly ash filled composites shows better tensile, flexural, impact and erosion resistance than that of granite filled composites. Erosion wear experiments are conducted as per Taguchi design of experiment. The results indicate that impact velocity, filler content and impingement angle influence the wear rate significantly. Steady-state erosion experiment indicates improvement in erosion resistance of the unfilled samples with filler addition. Two predictive models, one based on artificial neural network and the other on mathematical approach are suggested. The predicted and experimental values of erosion rate are showing good agreement, and are confirming the remarkable ability of a well-trained neural network.