This study aims to experiment with the mechanical properties of polypropylene (PP)/thermoplastic elastomer/nano-silica/compatibilizer nanocomposite using the melt mixing method. The addition of polyolefin elastomers has proved to be an approachable solution for low impact strength of PP, while it would also reduce the Young’s modulus and tensile strength. That is why reinforcement would be applied to this combination to enhance the elastic modulus. The mechanical properties of the prepared composites were devised to train an artificial neural network to predict these properties of the system in 6256 unknown points. Therefore, the sensitivity analysis was performed and the share of each input parameter on the respective output values was calculated. Additionally, a novel parameter called nanocomposite evaluation criterion (NEC) is introduced to analyze the suitability of the nanocomposites considering the mechanical properties. Accordingly, the formulation with optimal mechanical properties of toughness, elongation at break, tensile strength, Young’s modulus, and impact strength was obtained.

本研究旨在通过熔融混合法对聚丙烯（PP）/热塑性弹性体/纳米二氧化硅/增容剂纳米复合材料的力学性能进行实验。事实证明，添加聚烯烃弹性体是降低PP冲击强度的一种可行的解决方案，同时还会降低杨氏模量和拉伸强度。这就是为什么将增强作用应用于该组合以增强弹性模量的原因。设计了制备的复合材料的机械性能，以训练人工神经网络，以预测6256个未知点中系统的这些性能。因此，进行了灵敏度分析，并且计算了每个输入参数在各个输出值上的份额。另外，引入了一种新的参数，称为纳米复合材料评价标准（NEC），以考虑机械性能来分析纳米复合材料的适用性。因此，获得了具有最佳机械性能的韧性，断裂伸长率，拉伸强度，杨氏模量和冲击强度的制剂。