Prediction and optimisation of abrasive wear model for particle reinforced MMCs using statistical analysis

Abstract

In the present study, the wear weight loss model of aluminium alloy composites reinforced with 16 μm Al2O3 particles using varying volume fraction of particles up to 23·3 vol.-% fabricated by molten metal mixing method was developed in terms of volume fraction of reinforcement, abrasive grain size and sliding distance based on the Taguchi method. The two-body abrasive wear behaviour of the specimens was investigated using a pin-on-disc abrasion test apparatus against different SiC abrasives at room temperature. The orthogonal array, signal to noise ratio and analysis of variance were employed to find out the optimal testing parameters. The test results showed that the volume fraction of reinforcement was found to be the most effective factor among other control parameters in abrasive wear, followed by sliding distance. However, abrasive grain size and sliding distance had a much smaller effect compared to the volume fraction of reinforcement. The predicted weight loss of the composites was found to be in good agreement with the experimentally observed ones.