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Deepak S, Balasubramanya H S, Nandeesha H L, Veeresh Kumar G B, Divakara Shetty A S
This paper analyses wear behavior of Al6061 alloy reinforced with Tungsten carbide WC and compare it with the base Al6061 alloy. The selected reinforcement material has an average particle of size 5 microns. The reinforcement content was varied in the range 1-4% by weight in the steps of 1%. Dry sliding wear studies on composites were carried out using pin-on-disc testing machine for varying loads. The experiments were conducted using four different loading levels of 10, 20, 30 and 40 N at constant sliding distance of 3000 m and constant disc speed of 500 rpm under multi-pass condition. Height loss method has been considered for the analysis. The height loss of the composites was found to increase with the increase in normal load. Reduction in height loss and were noticed after WC reinforcements additions to the composite. The result indicates that WC has an influence on the wear properties of the composite. Height loss prediction was done using artificial neural network (ANN). The obtained results are used to develop and train an artificial neural network, which can predict the wear behavior of aluminum metal matrix composite. It was found that predicted wear rate using ANN technique was in good agreement with the experimental values.
TRAINLM function; epochs; height loss; NN training module