Artificial neural network approach for the prediction of wear for Al6061 with reinforcements

Abstract

Abstract In the prospect of finding a lightweight and wear-resistant materials, researchers have considered aluminium-based metal matrix composites (MMC), as aluminium has a wide variety of applications but possesses low wear resistance properties. To enhance the wear resistance of aluminium alloys, ceramic particles are reinforced. In this endeavour, commercially available aluminium alloy is reinforced with 2, 4 and 6 wt% of silicon carbide (SiC) and Vanadium pentoxide (V2O5) powder to improve its wear resistance. The intensity of reinforcement in the matrix was uniform, and the Scanning Electron Microscope image showed the grain refinement and grain boundary of the MMC’s. Wear tests were performed for L16 array set, uncertainty analysis of wear measurement is evaluated, and data were used to develop Artificial Neural Network (ANN) model. The efficient ANN model with a regression coefficient of 0.999 was used to make predictions for remaining sets. Experimental and predicted wear results were analysed; it is observed that higher wt% reinforcement of V2O5 increased wear resistance of aluminium compared to SiC. The methodology adapted using ANN for prediction of wear using meagre experimentation, will lay a path for tribologists to predict the wear of novel metal matrix composites in their endeavour of finding wear-resistant materials.