CAREER: Understanding Grain Boundary Strength via Adaptive Electron Backscatter Diffraction and Multiscale Analysis

职业：通过自适应电子背散射衍射和多尺度分析了解晶界强度

• 批准号: 2043264
• 负责人: Josh Kacher
• 金额: $ 54.85万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043264&HistoricalAwards=false
• 关键词: CAREER; Understanding; Grain; Boundary; Strength

NON-TECHNICAL SUMMARYPeople have been using metals and alloys for thousands of years and have constantly improved their strength and performance through developing new ways to make them. This process of improving the metals has relied largely on guess and check. Although people have become better at guessing and faster at checking, the foundational knowledge to intelligently design stronger, safer, more resilient metals is still missing. Part of the challenge in improving metals and alloys is that their performance is dependent on the type, density, and distribution of trillions of defects that are not much bigger than a few atoms. Two important types of these defects are dislocations, which allow metals to deform, and grain boundaries, which act as barriers to dislocations as they move through the metal. The character of a grain boundary influences how easily dislocations can move through the material, and in turn, affects the strength of the metal. However, a direct link between the character of a grain boundary and how strong of a barrier it is to dislocation motion has not been established. By looking at grain boundaries at ultra-small length scales of one millionth of a meter and smaller, this project will establish that direct link. To do so, electron microscopes, capable of imaging materials down to the level of individual atoms, will be used to see how dislocations accumulate in the material near grain boundaries while the material is being bent. Artificial intelligence (AI) will be built into the electron microscopes in order to rapidly and automatically explore tens of thousands of grain boundaries to obtain a statistical understanding of how grain boundary character is connected to its strength. This understanding will be instrumental in guiding the development of new metals and alloys that are stronger, safer, and longer lasting in application. The broader outreach of this work includes integrating high school students from underrepresented communities in a summer internship research program. These interns will work closely with graduate students supported by the program to investigate the strength of metals and will also develop lesson plans incorporating virtual reality elements to take back to their classes in the following school year. The summer internship will also include visits to the Novelis research center, a global Al company with research headquarters near Atlanta.TECHNICAL SUMMARYThe central role of grain boundaries has long been recognized in dictating the mechanical behavior and failure susceptibility of metals and alloys. However, efforts to understand how variations in grain boundary characteristics affect material properties have been hampered by an incomplete understanding of what determines the strength of individual grain boundaries. The purpose of this project is to determine the characteristics that dictate grain boundary strength, here defined as the barrier strength that grain boundaries pose to dislocation propagation. A new adaptive remeshing electron backscatter diffraction (AR-EBSD)-based approach will be developed, combining in-line processing and automated adaptive grid remeshing to rapidly sample the tens of thousands of grain boundaries needed to build a library to which machine learning approaches can be applied. This approach will be coupled with transmission electron microscopy (TEM) characterization and atomistic simulations to correlate grain boundary strength with dislocation transfer mechanisms. This coupled approach will facilitate an unprecedented exploration of grain boundary space in terms of the number of grain boundaries investigated, allowing rigorous grain boundary strength functions to be established. In addition, the multiscale electron microscopy techniques developed over the course of the proposed work will be a widely applicable addition to the materials characterization toolbox in investigating material deformation under ambient and extreme conditions. Furthermore, a pipeline for underrepresented minorities to engage in STEM research will be created by a “visualizing science” summer internship program for high school students from underrepresented communities. These interns will work with graduate students supported by this program to investigate the ductile fracture behavior of metals, learn mechanical testing and characterization techniques, and visit the Novelis research center, a global Al company with research headquarters near Atlanta. To enhance the broader impact of this program, the interns will also develop lesson plans incorporating virtual reality elements to take back to their high school classes.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.

非技术摘要人们已经使用金属和合金已有数千年了，并通过开发新方法来不断提高其力量和性能。尽管人们在检查方面变得更好，但智能设计更强大，更安全，更弹性的金属的基础知识仍然缺失。改善金属和合金的挑战的一部分是，它们的性能取决于数万亿个缺陷的类型，密度和分布，这些缺陷并不大于几个原子。这些缺陷的两种重要类型是脱位，这些缺口使金属变形和晶界，它们在金属中移动时充当脱位的障碍。晶界的特征会影响脱位如何在材料中移动，从而影响金属的强度。但是，尚未建立晶界特征与脱位运动的障碍的强度之间的直接联系。通过查看超小长度尺度的晶粒边界的一​​百万米和较小，该项目将建立直接链接。为此，能够将材料成像至单个原子水平的电子显微镜将用于查看位错是如何在材料弯曲时近乎晶界的材料中积累的。人工智能（AI）将内置在电子显微镜中，以快速并自动探索成千上万的晶界，以获得对晶界特征如何连接其强度的统计理解。这种理解将有助于指导强大，更安全且持久的新金属和合金的开发。这项工作的更广泛的宣传包括将来自代表性不足社区的高中学生纳入暑期实习研究计划。这些实习生将与该计划支持的研究生紧密合作，以调查金属的实力，还将制定课程计划包含虚拟现实元素，以便在接下来的学年中重新上课。暑期实习还将包括访问Novelis Research Center，这是一家全球AL公司，该公司在亚特兰大附近拥有研究总部。技术摘要谷物边界的核心作用在决定金属和合金的机械行为和失败敏感性方面已被认可。然而，通过对决定单个晶界强度的原因，不完全理解晶粒边界特征的变化如何影响材料特性的努力受到阻碍。该项目的目的是确定决定晶界强度的特征，在这里定义为晶界构成脱位传播的屏障强度。将开发一种新的自适应重新捕获电子反向散射（AR-EBSD）的方法，将在线处理和自动化自适应网格相结合，以快速对建立一个可以应用机器学习方法的库所需的数以万计的晶粒边界进行采样。这种方法将与透射电子显微镜（TEM）表征和原子模拟相结合，以将晶界强度与脱位转移机制相关联。这种耦合方法将根据研究的晶界数量来支持对晶界空间的前所未有的探索，从而确定了严格的晶界强度函数。此外，在拟议的工作过程中开发的多尺度电子显微镜技术将是对材料表征工具箱的广泛适用，用于研究在环境和极端条件下材料变形。此外，由代表性不足的少数群体从事STEM研究的管道将由“可视化科学”暑期实习计划为来自代表性不足的社区的高中生而创建。这些实习生将与该计划支持的研究生合作，研究金属的延性裂缝行为，学习机械测试和表征技术，并参观Novelis Research Center，这是一家全球AL AL公司，并在亚特兰大附近拥有研究总部。为了增强该计划的更广泛的影响，实习生还将制定课程计划，以继承虚拟现实要素，以重新上学课程。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响审查标准来评估NSF的法定任务。

项目成果

Multiscale computational and experimental analysis of slip-GB reactions: In situ high-resolution electron backscattered diffraction and concurrent atomistic-continuum simulations

• DOI: 10.1016/j.scriptamat.2023.115500
• 发表时间: 2023-07
• 期刊: Scripta Materialia
• 影响因子: 6
• 作者: Yang Su;T. Phan;Liming Xiong;J. Kacher