Solid Particle Erosion Response Simulation of Alumina Reinforced ZA-27 Metal Matrix Composites

Abstract

Inspired by the biological nervous system, an artificial neural network (ANN) approach is a fascinating computational tool, which can be used to simulate a wide variety of complex engineering problems such as tribo-performance of metal matrix composites (MMCs). In the present investigation, ANN approach is used to predict the solid particle erosion wear behaviour of alumina (Al2O3) reinforced of ZA-27 MMCs. Composites of different compositions with Al2O3 particle (0, 3, 6 and 9 wt %) reinforced in ZA-27 matrix are prepared by stir casting method. Solid particle erosion wear trials are conducted following a well planned experimental schedule based on design-of-experiments (DOE). Significant control factors influencing the wear rate are identified. An ANN approach taking into account training and test procedure is implemented to predict the dependence of wear behaviour on various control factors. The effects of the impact velocity and alumina content on the erosion rate are studied and predicted using this ANN model