The wear properties of particulate-reinforced metal matrix has been generally found to be a function of the applied load as well as the reinforcement volume fraction, particle size, and the shape and nature of the reinforcing phase. In the present study, aluminum matrix composites reinforced with boron carbide particles have been fabricated and then abrasive wear rate of unreinforced alloy and composites are studied using pin-on-disc machine. Based on experimental results, composites exhibit better wear resistance compared to unreinforced alloy. The abrasion resistance of the composites increased with increasing the volume fraction. At a constant volume fraction of B4C reinforcement, the abrasion resistance increased with an increase in the reinforcement size within the range studied. Artificial neural network (ANN) models with various training algorithms were performed in order to find the error by comparing the output value of the network with the target value and then minimizing the difference (error) by modifying the weights. The results of algorithm analyses revealed that the best performance was given by Levenberg–Marquardt learning.
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