Property prediction of carbon-ceramics composite material based on artificial neutral network

Models were established to predict the relation between components and properties of carbon-ceramics composite material based on the back propagation (BP) algorithm of the artificial neural network (ANN). The prediction models are composed of three neuron layers, i.e. input layer, hidden layer and output layer, to simulate the real structure of human brain. The volume percentages of the components are regarded as input parameters and the resistivity and antiflex strength of the composite material after graphitizing are regarded as output parameters. The selected thirty samples are considered as the data of the study and the random seven samples are predicted and assessed in the artificial neutral network of BP. On condition that the training data are precise enough, the models provide good results for the relation between components and properties of carbon-ceramics composite material. The electric resistance and benging strength error are respectively within 8% and 12% compared with experimental data. Therefore the models proposed are helpful to the design of carbon-ceramics composite material systems.