This study proposes a machine learning (ML) based approach for optimizing fiber orientations of variable stiffness carbon fiber reinforced plastic (CFRP) structures, where neural networks are developed to estimate the objective function and analytical sensitivities with respect to design variables as a substitute for finite element analysis (FEA). To reduce the number of training samples and improve the regression accuracy, an active learning strategy is implemented by successively supplying effective samples along with the suboptimal process. After proper training of neural networks, a quasi-global search strategy can be applied by implementing a large number of initial designs as starting points in the optimization. In this article, a mathematical example is first presented to show the superiority of the active learning strategy. Then a benchmark design example of a CFRP plate is scrutinized to compare the proposed ML-based with the conventional FEA-based discrete material optimization (DMO) method. Finally, topology optimization of fiber orientations is performed for design of a CFRP engine hood, in which the structural performance generated from the proposed ML-based approach achieves 12.62% improvement compared with that obtained from the conventional single-initial design method. This article is anticipated to demonstrate a new alternative for design of fiber-reinforced composite structures.

中文翻译：

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基于机器学习的变刚度复合结构纤维取向拓扑优化

本研究提出了一种基于机器学习 (ML) 的方法来优化可变刚度碳纤维增强塑料 (CFRP) 结构的纤维取向，其中开发了神经网络来估计与设计变量相关的目标函数和分析灵敏度，以替代有限的元素分析（FEA）。为了减少训练样本的数量并提高回归精度，通过连续提供有效样本以及次优过程来实施主动学习策略。在对神经网络进行适当的训练后，可以通过实施大量初始设计作为优化的起点来应用准全局搜索策略。在本文中，首先通过一个数学例子来展示主动学习策略的优越性。然后仔细检查 CFRP 板的基准设计示例，以将所提出的基于 ML 的方法与传统的基于 FEA 的离散材料优化 (DMO) 方法进行比较。最后，对CFRP发动机罩的设计进行了纤维取向的拓扑优化，与传统的单一初始设计方法相比，基于ML的方法产生的结构性能提高了12.62%。预计本文将展示设计纤维增强复合材料结构的新替代方案。其中，与传统的单一初始设计方法相比，所提出的基于 ML 的方法产生的结构性能提高了 12.62%。预计本文将展示设计纤维增强复合材料结构的新替代方案。其中，与传统的单一初始设计方法相比，所提出的基于 ML 的方法产生的结构性能提高了 12.62%。预计本文将展示设计纤维增强复合材料结构的新替代方案。