基于BP神经网络的含褶皱复合材料强度预测

利用BP(Back propagation)神经网络处理多参数问题具有的非线性映射及泛化能力,构建了具有3层隐藏层的神经网络,对含纤维褶皱复合材料层合板的压缩强度进行预测。基于LaRC失效准则建立三维损伤模型,对含褶皱复合材料的压缩失效进行数值分析。将数值分析结果作为数据样本对神经网络进行训练。采用黄金分割法快速确定最佳隐藏层神经元数量区间范围,并通过分析对比不同数量神经元模型的强度预测结果及评价指标,确定具有高预测精度的隐藏层神经元数量。结果表明,所构建的神经网络预测最大褶皱角为5.6°、9.9°和11.4°的3种层合板失效强度误差分别为3.4%、4.6%和-0.01%。本文所发展的基于BP神经网络对复合材料强度进行预测的方法,为工程应用中复合材料强度评估提供了一种有效的途径。

Strength Prediction of Laminates Containing Embedded Fiber Wrinkles Using BP Neural Networks

Taking advantages of the BP neural network in nonlinear mapping and generalization capabilities for multi-parameter problems, the BP neural networks with three hidden layers were constructed to predict the compressive strength of laminates containing embedded fiber wrinkles. The compressive failure was numerically simulated based on a three-dimensional damage model with the LaRC criterion. The numerical results were used as data samples for the networks training. An algorithm based on the golden section method was proposed to quickly determine the range of the neurons number in the hidden layer of the BP neural networks. Then the best number of the neurons was finally determined by comparing the prediction results and the assessment indicators of different cases. The results show that, the error of the strengths of the laminates with the maximum wrinkle angles of 5.6°,9.9° and 11.4° predicted by the developed BP neural networks are 3.4%, 4.6%, and -0.01%, respectively. The approach developed in the present work to predict the strength of composite materials based on the BP neural networks provides an effective way for the strength evaluation of composite materials in application.