The dynamic mechanical behavior (storage modulus, loss modulus, and tan δ) of hybrid sisal/glass composites was investigated in the temperature range of 30–210 °C, for two different volume percentages of reinforcement along with the different ratios of sisal and glass fibers. Based on the experimental outcome, an artificial neural network (ANN) approach was used to predict the dynamic mechanical properties followed by a surface response methodology (SRM). The ANN analysis showed an excellent fit with the storage modulus, loss modulus, and tan δ experimental data. In addition, the fitted curves with the ANN approach were used to propose equations based on SRM. The simulation result has shown that the ANN is a potential mathematical tool for the structure–property correlation for polymer composites and may help researchers in the development and application of their data, reducing the need for long experimental campaigns.

研究了混合剑麻/玻璃复合材料在 30-210 °C 温度范围内的动态力学行为（储能模量、损耗模量和 tanδ），两种不同体积百分比的增强材料以及不同比例的剑麻和玻璃纤维。基于实验结果，人工神经网络 (ANN) 方法用于预测动态机械性能，然后是表面响应方法 (SRM)。ANN 分析显示与储能模量、损耗模量和 tan δ 实验数据非常吻合。此外，使用 ANN 方法拟合的曲线被用于提出基于 SRM 的方程。