【摘要】 材料表面抗氧化涂层的质量是限制碳／碳复合材料作为高温结构材料使用的关键．本文运用人工神经网络技术建立了CVD－SiC涂层制备工艺的过程模型，以解决该过程影响因素众多、相互作用关系复杂、难以对制备过程进行有效的预测和控制的问题．研究结果表明：所建立的神经网络模型，可以比较准确和全面地反映各工艺因素对SiC-CVD过程的影响大小及内在规律；模型对工艺参数与沉积速率之间关系的预测与实验结果相吻合；证实了将人工神经网络模型应用于抗氧化涂层的制备过程的控制和工艺优化是有效和可行的．更多还原

【Abstract】 The quality of silicon carbide coating is the key factor for the utilization of carboncarbon composites as thermal structural materials. In order to optimize and control the deposit process, a neural network(NN) model for SiC-CVD process was developed. The outputs of NN model were found to be best fit with the sample data’s (<0.35%). With the help of the NN model,a series of predictions about the deposit condition-results were made. Finally, the predication about the influence of Ar on the deposit rate predicted by ANN model was identical well with the experimental results, which shows that the model established by ANN can mirror the parameters results relationships and inner regularity in the SiC-CVD process.更多还原