Applications of neural networks and genetic algorithms to CVI processes in carbon/carbon composites

A model of artificial neural networks and genetic algorithms is developed for the analysis and prediction of the correlation between CVI processing parameters and physical properties in carbon/carbon composites (C/C). The input parameters of the artificial neural network (ANN) are the infiltration temperature, the pressure in furnaces, the volume ratio of propylene, and the fiber volume fraction. The outputs of the ANN model are the two most important physical properties, namely, the density and density distribution of workpieces. After the ANN model based on BP algorithms is trained successfully, genetic algorithms (GAs) are used to optimize the input parameters of the model and select perfect combinations of CVI processing parameters. A good generalization performance of the model is achieved. Moreover, some explanations of those predicted results from the physical and chemical viewpoints are given. A graphical user interface is also developed for the integrated model.