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.

中文翻译：

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微观结构非弹性指纹和固体塑性和损伤的数据丰富的预测

固体中的非弹性机械响应，例如塑性、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，微结构特性使用了无穷小变形下的线性弹性。我们展示了一个框架，该框架建立在微观结构图像序列上，以开发非弹性趋势的指纹，然后将它们用于对机械响应直至失效的数据丰富的预测。与普通指纹类似，我们表明这些二维不稳定性前体特征可用于重建未知样品微观结构的完整机械响应；这一壮举是通过在为图像识别进行微调的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。我们展示了位错塑性玩具模型中微观结构指纹的基本方面，然后我们说明了该方法在 I 型断裂载荷下模型二元合金的相场模拟中的可扩展性和稳健性。