A Generalizable and Interpretable Deep Learning Model to Improve the Prediction Accuracy of Strain Fields in Grid Composites
A Generalizable and Interpretable Deep Learning Model to Improve the Prediction Accuracy of Strain Fields in Grid Composites
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DOI：
10.1016/j.matdes.2022.111192
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发表时间：
2022-09
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影响因子：
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通讯作者：
Dong-Oh Park;Jiyoung Jung;Grace X. Gu;Seunghwa Ryu
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文献类型：
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作者：
Dong-Oh Park;Jiyoung Jung;Grace X. Gu;Seunghwa Ryu
近年来，具有优异力学性能的网格复合材料的设计作为基于深度神经网络（DNN）的优化方法的试验台受到了极大的关注。然而，目前设计的深度神经网络架构并不是专门为网格复合而定制的，因此在探索来自训练数据集的未知配置时表现出较弱的泛化能力。本文提出了一种多尺度核神经网络（MNet），可以有效地预测网格复合材料在外加载荷作用下的应变场。预测复合材料的应变场对于理解材料在载荷作用下的表现尤为重要。MNet能够准确预测未知领域全新配置的应变场，与基准U-Net相比，MNet的平均绝对百分比误差（MAPE）降低了50%，是目前最先进的深度神经网络架构。此外，结果表明，MNet在不到三分之一的数据集上保持了出色的性能，并且可以应用于比初始训练使用的复合配置更大的网格复合配置。通过研究多尺度核的推理机制，我们的工作表明MNet可以有效地从材料分布中提取各种空间相关性。
Recently, the design of grid composites with superior mechanical properties has gained significant attention as a testbed for deep neural network (DNN)-based optimization methods. However, current designed DNN architectures are not specifically tailored for grid composites and thus show weak generalizability in exploring unseen configurations that stem away from the training datasets. Here, a multiscale kernel neural network (MNet) is proposed that can efficiently predict the strain field within a grid composite subject to an external loading. Predicting the strain field of a composite is especially important when it comes to understanding how the material will behave under loading. MNet enables accurate predictions of the strain field for completely new configurations in unseen domain, with a reduced mean absolute percentage error (MAPE) by 50% compared to a benchmark, U-Net as current state-of-the arts DNN architectures. In addition, results showed that MNet maintained superb performances with less than one-third of dataset, and can be applied to grid composites larger than the composite configurations used for the initial training. By investigating the inference mechanisms from the kernels of multiple sizes, our work revealed that the MNet can efficiently extract various spatial correlations from the material distribution.