Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids
Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids
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DOI：
10.1007/s00466-020-01845-x
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发表时间：
2019-05
影响因子：
4.1
通讯作者：
S. Papanikolaou
中科院分区：
文献类型：
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作者：
S. Papanikolaou
固体中的非弹性力学响应，如塑性、损伤和裂纹萌生，通常以显示微观结构和载荷依赖性的本构方式建模。然而，在无限小变形的线性弹性用于微观结构特性。我们展示了一个框架，该框架建立在微观结构图像序列的基础上，以开发非弹性趋势的指纹，然后使用它们来预测故障时的机械响应。类似于常见的指纹，我们表明这些二维不稳定性前兆签名可用于重建未知样品微结构的完整机械响应;这一壮举是通过在深度卷积神经网络的帮助下重建适当的平均行为来实现的，该深度卷积神经网络经过微调以用于图像识别。我们展示了基本方面的微观结构指纹的玩具模型的位错塑性，然后，我们说明了该方法的可扩展性和鲁棒性相场模拟模型二元合金下模式-I断裂载荷。
Inelastic mechanical responses in solids, such as plasticity, damage and crack initiation, are typically modeled in constitutive ways that display microstructural and loading dependence. Nevertheless, linear elasticity at infinitesimal deformations is used for microstructural properties. We demonstrate a framework that builds on sequences of microstructural images to develop fingerprints of inelastic tendencies, and then use them for data-rich predictions of mechanical responses up to failure. In analogy to common fingerprints, we show that these two-dimensional instability-precursor signatures may be used to reconstruct the full mechanical response of unknown sample microstructures; this feat is achieved by reconstructing appropriate average behaviors with the assistance of a deep convolutional neural network that is fine-tuned for image recognition. We demonstrate basic aspects of microstructural fingerprinting in a toy model of dislocation plasticity and then, we illustrate the method’s scalability and robustness in phase field simulations of model binary alloys under mode-I fracture loading.