Effective behavior of constituent materials of in-plane braided C/C composites

The microscopic braid structure and size of the in-plane braided C/C composites were measured by optical microscope observation. The finite element model of the braided composites was established. After the change ranges of effective properties of the constituent materials were provided, the macroscopic effective behavior corresponding relation between in-plane braided C/C composites and constituent material was constructed. Then, the nonlinear corresponding relation was trained by the radial basis function (RBF) artificial neural network (ANN) method. The effective properties of the constituents were predicted by the network combining the macroscopic experimental results of the braided composites. The results show that the in-plane elastic properties of the braided composites are about the same, which can be measured along the axial direction. The network is dependent on the training samples, but reasonable predictive results can be obtained by constructing a number of random sample training network. The network has a good fault tolerance and can predict the effective behavior of constituents of the braided composites accurately.