Modeling and Prediction of Erosion Response of Glass Reinforced Polyester-Flyash Composites

Abstract

Solid particle erosion of polymer composites is a complex surface damage process, strongly affected by material properties and operational conditions. To avoid repeated experimentation, it is important to develop predictive equations to assess material loss due to erosion wear under any impact conditions. This paper presents the development of a mathematical model for estimating erosion damage caused by solid particle impact on flyash filled glass fiber reinforced polyester composites. The model is based upon conservation of particle kinetic energy and relates the erosion rate with composite properties and test conditions. Another correlation derived from the results of Taguchi experimental design is proposed as a predictive equation for erosion wear of these fiber reinforced composites. Further, considering the complexity and high degree of nonlinearity in the erosion process, an artificial neural networks (ANN) technique is implemented as an effective tool for prediction of wear response of these composites in a larger space. Finally, the results of a mathematical model and the ANN model are compared with those obtained from experimentation.