Predicting the wear rate of AA6082 aluminum surface composites produced by friction stir processing via artificial neural network

Abstract

PurposeFriction stir processing (FSP) as a solid-state process has the potential for the production of effective aluminum matrix composites (AMCs). In this investigation, various ceramic particles including B4C, TiC, SiC, Al2O3 and WC were incorporated as the dispersed phase within AA6082 aluminum alloy by FSP. The wear rate of the composite is then investigated experimentally by making use of a design of experiments technique where wear rate is evaluated as the output parameter. The input parameters considered include tool rotational speed, traverse speed, groove width and ceramic particle type. An artificial neural network (ANN) simulation was then used to describe the wear rate of the surface composites. The weights of the network were adjusted to minimize the mean squared error using a feed forward back propagation technique. The effect of the individual input parameters on wear rate was then inferred from the ANN models. Trends are presented and related to the associated microstructures observed. The TiC infused AMC displayed the lowest wear rate whereas the Al2O3 infused AMC displayed the highest, within the scope of the current investigation. The paper aims to discuss these issues.Design/methodology/approachThe paper used ANN for the research study.FindingsThe finding of this paper is that the wear rate of AA6063 aluminum surface composites is influenced remarkably by FSP parameters.Originality/valueOriginal work of authors.