Probing the Evolution of Granular Microstructures during Dynamic Annealing via Integrated Three-Dimensional Experiments and Simulations

许多金属材料是多晶的，其中固体由许多不同取向的微小晶体(“颗粒”)组成。在某些情况下，当材料以单晶形式制造时，可以获得更好的性能，例如喷气式发动机涡轮叶片、形状记忆合金和太阳能电池。然而，单晶的生产既昂贵又耗时。近年来，研究人员发现，金属合金在反复加热和冷却的过程中，通过循环热处理引起的异常晶粒长大，可以产生单晶。在异常生长过程中，少数颗粒通过吞噬相邻颗粒优先生长。这个项目的目标是发现这一过程发生的原因和方式。该项目集成了对颗粒生长过程的实验表征和模拟的紧急研究，利用实时观察多晶微观结构演变的能力，并利用高性能计算来模拟微观结构演变。发展对异常晶粒生长的基本理解可能会导致单晶材料制造的范式转变，从而促进制造和技术方面的全球竞争力和国家繁荣。该项目还促进了训练有素的未来劳动力的发展；两名研究生接受了实验、建模、模拟和数据分析方面的最新技术培训。外展活动是通过在数学、工程和科学项目以及华盛顿初级科学奥林匹克竞赛中表现更出色的女性来进行的，其中包括一个虚拟现实演示，允许学生在热处理过程中浏览不断演变的微观结构。技术概述一种循环热处理，通过热循环反复形成和溶解析出物，有望通过异常的晶粒长大实现单晶和其他大颗粒材料的固态加工。然而，由于对这一进程背后的机制了解不深，它的全部潜力尚未实现。本项目的目的是为了促进非等温退火过程中异常晶粒长大的科学研究。提出了以下基本问题：在动态热环境下，启动异常晶粒长大的机制是什么？哪些颗粒最有可能变得异常，它们的微观结构特征是什么？异常颗粒是如何生长成新的微观结构的？为了回答这些问题，结构表征、相场和相场-晶体建模以及图论方法的新兴研究被协同地整合在一起。基于同步加速器的高分辨率X射线衍射显微镜被用来实时可视化和量化颗粒和亚颗粒网络的演变，并能够基于测量的空间、时间和取向分辨的数据集作为初始条件来模拟微观结构的演变。由此产生的高维数据被提炼成一个网络模型，该模型简洁地描述了晶界运动的颗粒和局部驱动力，大大降低了预测微观结构演变的计算成本。对热循环过程中异常颗粒生长的科学理解最终将指导单晶制造的工艺设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Non-technical SummaryMany metallic materials are polycrystalline, in which a solid consists of many tiny crystals (“grains”) with different orientations. In some cases, superior properties can be achieved when the material is made in a single-crystal form, as in the case for jet-engine turbine blades, shape-memory alloys, and solar cells. However, single crystals are expensive and time-consuming to produce. Recently researchers have found that single crystals can be produced through abnormal grain growth induced by cyclic heat treatment, where the metal alloy is heated and cooled repeatedly. During abnormal grain growth, a few grains preferentially grow by engulfing the neighboring grains. The goal of this project is to discover why and how this process occurs. The project integrates emergent research in experimental characterization and simulations of the grain growth process, capitalizing on the ability to watch the evolution of the polycrystalline microstructure in real-time and leveraging high-performance computing to simulate microstructural evolution. Developing the fundamental understanding of abnormal grain growth could lead to a paradigm shift in the manufacture of single-crystalline materials, and therefore it promotes global competitiveness in manufacturing and technology and national prosperity. The project also promotes the development of a highly trained future workforce; two graduate students are trained in state-of-the-art techniques in experiments, modeling, simulations, and data analysis. Outreach activities are carried out through the Females Excelling More in the Math, Engineering, and the Sciences program and Washtenaw Elementary Science Olympiad and include a virtual reality demonstration that allows students to walk through an evolving microstructure during heat treatment. Technical SummaryA cyclic heat treatment, in which precipitates are repeatedly formed and dissolved by thermal cycling, holds promise for solid-state processing of single crystals and otherwise large-grained materials via abnormal grain growth. However, its full potential has not been realized due to the poor understanding of the mechanisms underlying the process. The objective of this project is to advance the science governing the growth of the abnormal grains during non-isothermal annealing. The following fundamental questions are addressed: What is the mechanism by which abnormal grain growth is initiated under dynamic thermal environment? Which grains are most likely to become abnormal and what are their microstructural signatures? How does the abnormal grain grow into new microstructural neighborhoods? To answer these questions, emergent research in structural characterization, phase-field and phase-field-crystal modeling, and graph theory methods are synergistically integrated. High-resolution synchrotron-based X-ray diffraction microscopy is utilized to visualize and quantify the evolution of the grain and subgrain network in real-time and also enable microstructural evolution simulations based on the measured space-, time-, and orientation-resolved datasets as initial conditions. The resulting high-dimensional data is then distilled into a network model that succinctly describes the granular and the local driving forces for grain boundary motion, which significantly reduces the computational cost of predicting the microstructural evolution. Scientific understanding of abnormal grain growth upon thermal cycling ultimately informs the process design of single-crystal fabrication.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.