In the present work, we use artificial neural network (ANN) approach to develop a tool for prediction of the effective viscoelastic properties - storage and loss moduli - of vinyl ester reinforced with graphite nanoplatelets. Explicit results are obtained in terms of the constituents’ volume fractions, temperature, and loading frequency. The experimental data for ANN training and testing ware obtained using a Dynamic Mechanical Analyzer (DMA) and contains 153 data sets; the training and testing sets consisted of randomly selected 131 and 22 sets, respectively. The good accuracy of the model demonstrates that ANN is efficient for predicting viscoelastic properties in terms of three independent parameters.

在当前的工作中，我们使用人工神经网络（ANN）方法来开发一种工具，用于预测石墨纳米片增强的乙烯基酯的有效粘弹性质-储能和损耗模量。根据成分的体积分数，温度和加载频率获得了明确的结果。使用动态机械分析仪（DMA）获得的ANN培训和测试产品的实验数据，其中包含153个数据集；训练和测试集分别由随机选择的131套和22套组成。该模型的良好准确性表明，就三个独立参数而言，人工神经网络可有效预测粘弹性。