Metin Kök1,2, Erdogan Kanca3
CMC-Computers, Materials & Continua, Vol.18, No.3, pp. 213-236, 2010, DOI:10.3970/cmc.2010.018.213
Abstract In this investigation, a new model was developed to predict the wear rate of Al2O3 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… More >
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