Prediction of Wear Resistance of In-situ Zinc Matrix Composites Reinforced by Silicon Phase Based on Neural Network

Abstract

Abstract Silicon phase reinforced zinc-aluminium alloy composites were prepared by in-situ method. The effect of silicon content and external load on the wear resistance of in-situ zinc--aluminium composites was studied by BP artificial neural network. The test results show that the silicon phase, as a hard material, plays the role of supporting load and improves the wear resistance of the alloy. With the increase of Si content, the wear resistance of in-situ zinc--aluminium composites increased first and then decreased. When the amount of silicon added exceeds 2.5-3%, the wear resistance will be reduced, which is related to the silicon phase aggregation and the easy separation of large silicon phases from the matrix. With the increase of external load, the relative wear continues to increase, which is related to the increase of friction. The neural network platform can successfully predict the general trend of wear resistance with the change of silicon content and load.