C／C复合材料等温CVI工艺模糊神经网络建模

C／C复合材料等温CVI工艺影响因素繁多，而效率极低，造成C／C复合材料的生产周期极长、制造成本极高。基于模糊神经网络技术及遗传算法，以有限元数值模拟结果作为网络训练的虚拟样本，建立了C／C复合材料等温CVI工艺预测系统，利用该系统挖掘得到了等温CVI工艺主要影响因素，如沉积温度，前驱气体组份比与流速等，对制件密度的作用规律，为CVI工艺的综合设计与优化提供了理论依据，提高了CVI工艺的设计水平，以期达到降低C／C复合材料制造成本的目的。

Modeling of Isothermal CVI Process of C/C Composites by Fuzzy Neural Network

The isothermal CVI (chemical vapor infiltration) process of carbon/carbon composites is controlled by many factors and its efficiency is very low. Manufacturing of C/C composites with isothermal CVI processes is costly and thus limits commercial applications of C/C composites. Based on the fuzzy neural network (FNN) technique and genetic algorithm, a predicting system for isothermal CVI process was proposed and established. The simulation results of FEM called virtual samples were selected as the network's training samples. Based on the FNN system, the influences of main infiltration parameters, such as infiltration temperature, precursor gas flow ratio and rate, had been studied; and they are good instructions for the design and optimization of CVI process. Using this FNN system, we expect that we can reduce the time of development and densification, thus reducing manufacturing cost.