Experimental and Prediction of Abrasive Wear Behavior of Sintered Cu-SiC Composites Containing Graphite by Using Artificial Neural Networks

Abstract

Experimental investigations were undertaken to determine the abrasive wear behavior of various percentages of Cu-SiC-Gr hybrid composites. Wear tests were carried out using a pin-on-disc type machine using various input parameters like load, sliding distance, and sliding velocity with various SiC abrasive papers of grit size 80, 220, and 400, having an average particle size of 192, 102, and 45 μm. Neural networks are employed to study the tribological behavior of sintered Cu-SiC-Gr hybrid composites. Optical microscope, scanning electron microscope (SEM), X-ray diffraction (XRD), and energy-dispersive spectral observations are used to evaluate the characteristics. The proposed neural network model used the measured parameters, namely, the weight percentage of graphite, abrasive size, sliding speed, load, and sliding distance, to predict the wear loss of the composite. In order to improve the accuracy and obtain better results, an artificial neural network (ANN) with a genetic algorithm (GA) function was used. Optimization of the training process of the ANN using a GA is performed and the results are compared with the ANN trained without a GA. The predicted values from the proposed networks coincide with the experimental values.