Deep learning predicts path-dependent plasticity
Deep learning predicts path-dependent plasticity
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DOI：
10.1073/pnas.1911815116
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发表时间：
2019-12-26
影响因子：
11.1
通讯作者：
Bessa, M. A.
中科院分区：
文献类型：
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作者：
Mozaffar, M.;Bostanabad, R.;Bessa, M. A.
塑性理论旨在描述材料在一般变形状态下的屈服轨迹和加工硬化。它的复杂性大部分来自于屈服轨迹对材料及其微观结构的完整应变历史的非平凡依赖性。这激发了3个巧妙的简化，巩固了世纪的发展，在这一领域：1）屈服准则描述屈服点的位置; 2）关联或非关联流动规则定义的方向塑性流动;和3）有效的应力应变法律符合塑性功等效原理。然而，这些简化产生了两个关键的复杂性。首先，找到描述具有复杂微观结构的材料的这3个假设的方程并不容易。其次，屈服面演化需要迭代地跟踪，即，通过返回映射算法。在这里，我们证明了在使用递归神经网络时，在序列学习的背景下不需要这些假设，从而转移了上述复杂性。这项工作为目前建立的塑性公式提供了一种替代方案，为通过深度学习找到历史和微观结构相关的本构模型提供了基础。
Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of developments in this field: 1) yield criteria describing yield loci location; 2) associative or nonassociative flow rules defining the direction of plastic flow; and 3) effective stress-strain laws consistent with the plastic work equivalence principle. However, 2 key complications arise from these simplifications. First, finding equations that describe these 3 assumptions for materials with complex microstructures is not trivial. Second, yield surface evolution needs to be traced iteratively, i.e., through a return mapping algorithm. Here, we show that these assumptions are not needed in the context of sequence learning when using recurrent neural networks, diverting the above-mentioned complications. This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning.