基于遗传算法和神经网络的C/C复合材料等温CVI工艺参数优化模型  

Optimization model for isothermal CVI process parameters for C/C composites based on genetic algorithm and neural network

作　　者：李妙玲[1] 仝军锋[2] 赵红霞[1] LI Miaoling TONG Junfeng ZHAO Hongxia(Sehool of Mechanical Engineering, Luoyang Institute of Seience and Technology, Luoyang 471023, China College of Electrical Engineering and Automation, Luoyang Institute of Science and Technology, Luoyang 471023, China)

机构地区：[1]洛阳理工学院机械工程学院,洛阳471023 [2]洛阳理工学院电气工程与自动化学院,洛阳471023

出　　处：《复合材料学报》2016年第11期2666-2673,共8页Acta Materiae Compositae Sinica

基　　金：国家自然科学基金(51472203);河南省科技攻关计划(132102210136)

摘　　要：建立了基于遗传算法和误差反传(GA-BP)神经网络的化学气相渗透(CVI)工艺参数优化模型。以新型等温CVI工艺制备C/C复合材料时采集的实验数据作为模型评价样本,分析了主要可控影响因素(沉积温度、前驱气体分压与滞留时间等)对C/C复合材料制件密度及其密度均匀性的作用规律。在该模型指导下,样本的期望密度和实测密度最大误差不超过6.2%,密度差最大误差不超过8.2%。实验结果也证明了该模型具有较高的精度和良好的泛化能力,可以用于CVI工艺参数的优化。An optimization model of the process parameters during a chemical vapor infiltration（CVI）was established based on genetic algorithm and back propagation（GA-BP）neural network.The experimental data from the novel isothermal CVI process of carbon/carbon（C/C）composites were selected as the samples to evaluate the model.The effect of the main controllable factors,such as infiltration temperature,part pressure of precursor gas and resident time etc,on the density and uniformity of C/C composites were analyzed.Under the guidance of the model,the maximum errors between the desired densities and the tested densities of the experiment samples are not larger than 6.2% and those between their density differences were not larger than 8.2%.The results show that the established optimization model has high precision and good generalization.It can be efficiently applied for optimizing CVI process parameters.

关 键 词：C/C复合材料 化学气相渗透 BP神经网络 遗传算法 参数优化 

分 类 号：TB330.1[一般工业技术—材料科学与工程]