From Limited Data to the Deformation Field in Metals: A Machine Learning Driven Approach

从有限数据到金属变形场：机器学习驱动的方法

• 批准号: 2225675
• 负责人: Jaafar El-Awady
• 金额: $ 60万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225675&HistoricalAwards=false
• 关键词: Limited; Data; Deformation; Field; Metals

Having warning before a catastrophic failure of a material occurs can save lives and reduce costs. Prior to failure a material may undergo internal changes which generate high-frequency stress waves, referred to as acoustic emissions. Measurements of acoustic emissions in metals provide a unique approach for quantifying defects and their movements. However, interpreting acoustic emissions is a longstanding challenge. This research will address this challenge by identifying and decoding the distinct acoustic emission signatures of each deformation mechanism with a combination of experiments and machine learning tools. This research will result in a unique method that endows experiments with a window into the fundamental deformation mechanisms that are not currently accessible from surface measurements alone. This will enable new basic knowledge of the behavior of metals during deformation. This research is also integrated with education and outreach. Results from this work will be integrated into a new course on machine learning for solid mechanics and materials engineering. Maryland high-school students from under-served/under-represented groups will also be engaged in research internship opportunities. We will utilize integrated physics-based modeling, machine learning, and experiments to: (1) develop a “digital twin’’ of acoustic emission experiments to forward predict the acoustic emission surface waves associated with complex slip avalanches during the deformation of single crystal Ni micropillars; (2) definitively assess/scrutinize existing phenomenological acoustic emission models in literature, and develop new physics-based theoretical models that identify the interconnections between dislocation-based plasticity and acoustic emission signals; (3) predict the true experimentally observed slip localization in the 3D volume from the surface acoustic emission measurements; (4) train deep operator networks (DeepONets) for forward predictions of acoustic emission and inverse predictions of the underlying deformation mechanisms; and (5) validate the forward and inverse predictions through coupled in situ scanning electron microscopy microcompression experiments and acoustic emission measurements on single-crystal Ni microcrystals. To close the loop between the developed models and the experiments, we will also utilize the trained DeepONets on the experimental results to gain fundamental understanding of the underlying deformation mechanisms during dislocation avalanches in micro-compression experiments, which are currently difficult to interpret based on surface measurements and load-displacement measurements alone.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在材料发生灾难性故障之前发出警告可以挽救生命并降低成本。在失效之前，材料可能会发生内部变化，产生高频应力波，称为声发射。金属中声发射的测量为量化缺陷及其运动提供了一种独特的方法。然而，解释声发射是一个长期的挑战。这项研究将通过结合实验和机器学习工具识别和解码每个变形机制的不同声发射特征来应对这一挑战。这项研究将产生一种独特的方法，赋予实验一个窗口，进入基本的变形机制，目前无法从表面测量单独访问。这将使人们对金属在变形过程中的行为有新的基本认识。这项研究还与教育和外联相结合。这项工作的结果将被整合到固体力学和材料工程的机器学习新课程中。来自服务不足/代表性不足群体的马里兰州高中学生也将参与研究实习机会。我们将利用基于物理的建模、机器学习和实验相结合的方法：（1）开发声发射实验的“数字孪生”，以预测单晶Ni微柱变形过程中与复杂滑动雪崩相关的声发射表面波;（2）明确地评估/审查文献中现有的现象学声发射模型，并建立新的基于物理学的理论模型，以识别基于位错的塑性和声发射信号之间的相互联系：（3）从表面声发射测量结果预测真实的实验观察到的三维体积中的滑移局部化;（4）培养深度运营商网络（DeepONets）用于声发射的正向预测和潜在变形机制的反向预测;以及（5）通过耦合原位扫描电子显微镜微压缩实验和声发射测量验证正向和反向预测，单晶Ni微晶。为了关闭开发的模型和实验之间的循环，我们还将利用经过训练的DeepONets对实验结果进行分析，以获得对微压缩实验中位错雪崩过程中潜在变形机制的基本理解，目前很难根据表面测量和负载来解释，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The three-dimensional elastodynamic solution for dislocation plasticity and its implementation in discrete dislocation dynamics simulations

位错塑性的三维弹动力解及其在离散位错动力学模拟中的实现

• DOI: 10.1016/j.actamat.2023.118945
• 发表时间: 2023
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Yang, Junjie;Rida, Ali;Gu, Yejun;Magagnosc, Daniel;Zaki, Tamer A.;El-Awady, Jaafar A.
• 通讯作者: El-Awady, Jaafar A.