Abrasive Wear Model for Al2O3 Particle Reinforced MMCs Using Genetic Expression Programming

摘要

In this investigation, a new model was developed to predict the wear rate of Al_2O_3 particle-reinforced aluminum alloy composites by Genetic Expression Programming (GEP). The training and testing data sets were obtained from the well established abrasive wear test results. The volume fraction of particle, particle size of reinforcement, abrasive grain size and sliding distance were used as independent input variables, while wear rate (WR) as dependent output variable. Different models for wear rate were predicted on the basis of training data set using genetic programming and accuracy of the best model was proved with testing data set. The two-body abrasive wear tests of the specimens was performed using a pin-on-disc abrasion test apparatus where the sample slid against different SiC abrasives under the loads of 2N at the room conditions. The test results showed that GEP model has produced correlation coefficient (R) values about 0.988 for the training data and 0.987 for the test data. The predicted wear rate results were compared with experimental results and found to be in good agreement with the experimentally observed ones.