Knowing friction coefficient is important for determination of wear loss conditions at Al-Si alloys. Tribological events that influence wear and its variations affect experimental results. Artificial neural network (ANN) is a new information processing system based on the neural system of human brain. The potential of using feed forward backpropagation neural network in prediction of some physical properties of aluminium–silicon alloys synthesized by compocasting method has been studied in the present work. Al-Si alloys specimens were subjected to dry sliding wear tests using pin-on-disc apparatus at room conditions. Effects of load, sliding velocity and sliding duration on the wear loss of the alloy have been investigated. The experimental results were used to train the ANN program and the results were compared with experimental values. It was observed that the experimental results are very close to ANN’s results.
