Deep material network with cohesive layers: Multi-stage training and interfacial failure analysis
Deep material network with cohesive layers: Multi-stage training and interfacial failure analysis
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DOI：
10.1016/j.cma.2020.112913
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发表时间：
2020-05-01
影响因子：
7.2
通讯作者：
Liu, Zeliang
中科院分区：
文献类型：
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作者：
Liu, Zeliang
多尺度材料建模和设计的一个基本问题是考虑界面处的牵引-分离行为。通过用内聚层丰富深层材料网络（DMN），本文提出了一种新的数据驱动材料模型，该模型能够准确有效地预测具有界面效应的非均质材料的多尺度响应。在新调用的内聚构建块中，拟合参数具有与内聚层的长度、尺度和方向相关的物理意义。结果表明，在只使用线性弹性直接数值模拟（DNS）生成训练数据的情况下，通过多阶段训练策略可以有效地优化富集材料网络。通过对单向纤维增强复合材料的剥离分析，证明了该方法对未知材料和加载空间的外推能力，其中界面行为受不可逆软化混合模式内聚规律支配。通过非线性路径相关的DNS结果验证了其预测精度，计算时间的减少尤为显著。(C) 2020 Elsevier B.V.版权所有
A fundamental issue in multiscale materials modeling and design is the consideration of traction-separation behavior at the interface. By enriching the deep material network (DMN) with cohesive layers, the paper presents a novel data-driven material model which enables accurate and efficient prediction of multiscale responses for heterogeneous materials with interfacial effect. In the newly invoked cohesive building block, the fitting parameters have physical meanings related to the length scale and orientation of the cohesive layer. It is shown that the enriched material network can be effectively optimized via a multi-stage training strategy, with training data generated only from linear elastic direct numerical simulation (DNS). The extrapolation capability of the method to unknown material and loading spaces is demonstrated through the debonding analysis of a unidirectional fiber-reinforced composite, where the interface behavior is governed by an irreversible softening mixed-mode cohesive law. Its predictive accuracy is validated against the nonlinear path-dependent DNS results, and the reduction in computational time is particularly significant. (C) 2020 Elsevier B.V. All rights reserved.