以凝胶注模法制备多孔氮化硅陶瓷正交试验结果作为样本,建立3层Back Pmpagation(BP)神经网络,并进行训练以预测陶瓷性能.通过附加试验值对建立的神经网络预测能力进行验证,证明该BP神经网络模型是有效的,能准确预测多孔氮化硅陶瓷性能.通过BP神经网络模型研究多孔氮化硅陶瓷性能的结果表明,随着固含量的增加,气孔率单调下降;固含量存在一优化值,此时陶瓷抗弯强度最大;单体含量越大,气孔率越大,而抗弯强度降低.
Based on orthogonal experimental results of porous SigN4 ceramics by gel casting preparation, a three-layer back propagation (BP) artificial neural network (BP ANN) was developed for prediction of the flexnral strength and porosity. The BP ANN is composed of three neurons in the input layer, two neurons in the output layer and six neurons the hidden layer. This study demonstrates that the proposed neural network approach can predict the performances of porous Si_3N_4 ceramics by gel casting preparation to a high degree of accuracy, and the neural network is a very useful and accurate tool for performances analysis of porous Si_3N_4 ceramics. By the proposed neural network prediction and analysis, the results suggest that the porosity monotonically decreases with the increase of solid loading, flexural strength is low when solid loading was too low or too high, and flexural strength has an optimum value.
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