三维针刺C/C-SiC复合材料预制体工艺参数优化EI北大核心CSCD

基于误差反向传播(BP)神经网络与改进的遗传算法建立三维针刺C/C-SiC复合材料预制体工艺优化的代理模型,获得针刺工艺参数与复合材料刚度性能之间的关系。利用BP网络实现复合材料刚度性能预测,BP网络的预测值与有限元计算结果吻合程度较好,模型训练误差最大为0.526%,测试数据误差最大为0.454%,BP网络预测精度高。对传统遗传算法的遗传策略和优化策略进行改进,利用两种改进的遗传算法对针刺工艺参数进行优化。优化后的工艺参数显著提高了材料的刚度性能,其中面内拉伸模量分别提高了11.07%和11.48%,面外拉伸模量分别提高了49.64%和48.13%,复合材料的综合刚度性能分别提高18.17%和18.21%。

Optimization of process parameters of three-dimensional needled preforms for C/C-SiC composites

A surrogate model was established to optimize needling process parameters of three dimensional needled C/C-SiC composites by using back propagation(BP)neural network and improved genetic algorithm.The relationship between needling process parameters and composites stiffness was obtained.The stiffness prediction obtained by BP neural network is in good agreement with the finite element calculated results.The maximum error of training data is 0.526%,and the maximum error of test data is 0.454%.Thus,the BP neural network model exhibits the high prediction accuracy.The genetic and optimization strategies of genetic algorithm were improved to optimize the needling process parameters.The calculated needling process parameters by the model can significantly improve the stiffness of the C/C-SiC composites.The in-plane tensile modulus increase by 11.07%and 11.48%,and the out-of-plane tensile modulus increase by 49.64%and 48.13%,respectively.The comprehensive stiffness performance of composite material increase by 18.17%and 18.21%,respectively.