The optimal stochastic distribution of carbon nanotubes (CNTs) in nanoreinforced polymer composite of a cantilevered microbeam is investigated. Finite-element simulations of the CNT-reinforced microbeams were conducted to obtain data for training an artificial neural network to construct a surrogate model. This model was then used in an optimization routine to determine the optimal CNT distribution in the microbeam with inclusion of an uncertainty in the dispersion of CNTs across the microbeam. The results obtained within the framework of this model showed an improvement compared with those reported in the literature.

中文翻译：

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使用人工神经网络优化悬臂聚合物微梁中CNTS的随机分布

研究了悬臂微梁的纳米增强聚合物复合材料中碳纳米管 (CNT) 的最佳随机分布。进行了 CNT 增强微梁的有限元模拟，以获得用于训练人工神经网络以构建替代模型的数据。然后在优化程序中使用该模型来确定微束中的最佳 CNT 分布，其中包括在整个微束中 CNT 分散的不确定性。与文献报道的结果相比，在该模型框架内获得的结果显示出改进。