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Prediction and optimization of mechanical properties of composites using convolutional neural networks

机译：使用卷积神经网络预测和优化复合材料的力学性能

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摘要

In this paper, we develop a convolutional neural network model to predict the mechanical properties of a twodimensional checkerboard composite quantitatively. The checkerboard composite possesses two phases: one phase is soft and ductile while the other is stiff and brittle. The ground-truth data used in the training process are obtained from finite element analyses under the assumption of plane stress. Monte Carlo simulations and central limit theorem are used to find the size of the dataset needed. Once the training process is completed, the developed model is validated using data unseen during training. The developed neural network model captures the stiffness, strength, and toughness of checkerboard composites with high accuracy. Also, we integrate the developed model with a genetic algorithm (GA) optimizer to identify the optimal microstructural designs. The genetic algorithm optimizer adopted here has several operators: selection, crossover, mutation, and elitism. The optimizer converges to configurations with highly enhanced properties. For the case of the modulus and starting from randomly-initialized generation, the GA optimizer converges to the global maximum which involves no soft elements. Also, the GA optimizers, when used to maximize strength and toughness, tend towards having soft elements in the region next to the crack tip.
机译：在本文中，我们开发了卷积神经网络模型来定量预测二维棋盘格复合材料的力学性能。棋盘状复合材料具有两个阶段：一个阶段是软且易延展的，而另一阶段则是硬而脆的。训练过程中使用的地面数据是在假设平面应力的情况下通过有限元分析获得的。使用蒙特卡洛模拟和中心极限定理来找到所需数据集的大小。训练过程完成后，将使用训练过程中看不到的数据验证开发的模型。所开发的神经网络模型可以高精度地捕获棋盘状复合材料的刚度，强度和韧性。此外，我们将开发的模型与遗传算法（GA）优化器集成在一起，以识别最佳的微结构设计。这里采用的遗传算法优化器具有多个运算符：选择，交叉，变异和精英。优化器收敛到具有高度增强的属性的配置。对于模数并从随机初始化的生成开始，GA优化器会收敛到不涉及软元素的全局最大值。同样，GA优化程序用于最大化强度和韧性时，倾向于在裂纹尖端附近的区域中包含软元素。

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