Prediction of wear properties in A356 matrix composite reinforced with B4C particulates

In the present study, wear properties of A356 unreinforced alloy and composites with different vol.% of boron carbide particles were investigated. It is noted that composites exhibit better wear resistance compared to unreinforced alloy. According to the differences in wear rates of the composites, two separate wear rate were identified as low and high wear rate regimes. A combination of artificial neural network (ANN) and finite element technique (FEM) was implemented in order to predict the composites wear behavior. The FEM method is used for discretization and to calculate the transient temperature field of quenching. It is observed that predictions of ANN are consistent with experimental measurements for A356 composite and considerable savings in terms of cost and time could be obtained by using neural network model.