Crack Path Predictions in Heterogeneous Media by Machine Learning
Crack Path Predictions in Heterogeneous Media by Machine Learning
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DOI：
10.1016/j.jmps.2022.105188
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发表时间：
2022-12
影响因子：
5.3
通讯作者：
M. Worthington;H. Chew
中科院分区：
文献类型：
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作者：
M. Worthington;H. Chew
裂纹尖端应力场与附近微观组织非均质性之间的相互作用影响裂纹路径，进而影响材料的有效断裂韧性。在本文中，我们评估了人工神经网络（ann）的机器学习能力，以预测材料中给定的初始空洞缺陷分布的裂纹路径，并提供了对潜在裂纹扩展机制的见解。我们的人工神经网络准确地捕获了过程区大小，裂纹扩展顺序，以及在简单情况下产生的裂纹模式，其中孔洞与裂纹尖端的接近程度形成了裂纹推进的标准。在延性介质中，先前存在的空洞随着变形而扩大，并通过形成多个不相连的损伤区或连续地与主裂纹连接起来。在包含两种尺寸孔洞的基于微力学的延性断裂模型中，通过对裂纹序列的训练，人工神经网络成功捕获了这两种韧性断裂过程的复杂裂纹模式。此外，人工神经网络架构能够预测随机裂纹扩展，通过提供可能的裂纹路径的多样性，以及每个路径的量化可能性。结果进一步证明了自主裂缝路径预测在实现裂缝设计中的实用性。
The interaction between stress fields at the crack-tip and nearby microstructural heterogeneities influences the crack path, and in turn, the effective fracture toughness of a material. In this paper, we assess the ability of artificial neural networks (ANNs) for machine learning to predict the crack paths from given initial void defect distributions in the material, and to provide insights into the underlying crack growth mechanics. Our ANN accurately captures the process zone size, crack growth sequence, and resulting crack patterns in the simplistic case where the proximity of voids to the crack-tip forms the criterion for crack advance. In a ductile medium, pre-existing voids grow with deformation and link up with the primary crack either contiguously or through the formation of multiple unconnected damage zones. The complex crack patterns for both these ductile fracture processes are successfully captured by an ANN, trained on the cracking sequences in a micromechanics-based ductile fracture model containing two size-scales of voids. In addition, the ANN architecture is capable of predicting stochastic crack growth, by providing a multiplicity of possible crack paths, along with a quantified likelihood of each path. Results further demonstrate the utility of autonomous crack path predictions in enabling fracture-by-design.