Microstructural measurement and artificial neural network analysis for adhesion of tribolayer during sliding wear of powder-chip reinforcement based composites

Abstract

The influence of powder-chip based reinforced LM6 aluminum alloy fabricated by a consolidated effect of stirring and squeeze process in the semi-solid stage is reported for wear properties. Effect of oxide formation on the worn surfaces due to the processing was noticed and experimental results showing that powder-chip based reinforcement with semi-solid slurry affects and gave excellent resistance against the adhesive wear. Evidences of protective tribo-layers observed from the worn surface investigation, profilometer analysis, EDS and XRD results which provides an appropriate explanation for the drop in the wear rate in alloys. Specific wear rate reduction due to the effect of oxides in mixed tribolayer has been studied by artificial neural network (ANN) with two stage nested analysis which reflects only 1.11% Mean Square Error as compared to experimental values. This model provides better understanding to identify influencing parameter for huge variable set of processing and validate with sufficient accuracy. © 2020 Elsevier Ltd