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，其中固体由许多具有不同取向的微小晶体（“晶粒”）组成。在某些情况下，当材料以单晶形式制成时，可以实现上级性能，如喷气发动机涡轮机叶片、形状记忆合金和太阳能电池的情况。然而，单晶体的生产昂贵且耗时。最近研究人员发现，单晶可以通过循环热处理诱导的异常晶粒生长来产生，其中金属合金被反复加热和冷却。在异常晶粒生长期间，少数晶粒通过吞噬相邻晶粒而优先生长。这个项目的目标是发现为什么以及如何发生这个过程。该项目整合了实验表征和模拟晶粒生长过程的新兴研究，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导致单晶材料制造的范式转变，因此它促进了制造业和技术的全球竞争力以及国家繁荣。该项目还促进了训练有素的未来劳动力的发展;两名研究生在实验，建模，模拟和数据分析方面接受了最先进的技术培训。外展活动通过女性在数学，工程和科学计划中表现更出色进行，并包括虚拟现实演示，让学生在热处理过程中通过不断发展的微观结构。技术总结循环热处理，其中沉淀物反复形成和溶解的热循环，持有的单晶和其他大晶粒材料通过异常晶粒生长的固态加工的承诺。然而，由于对这一进程的基本机制缺乏了解，其潜力尚未得到充分发挥。本计画的目的是为了增进非等温退火过程中异常晶粒成长的科学。下面的基本问题是解决：什么是机制，异常晶粒生长的动态热环境下发起的？哪些晶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结构邻域的？为了回答这些问题，在结构表征，相场和相场晶体建模和图论方法的新兴研究协同集成。基于高分辨率同步加速器的X射线衍射显微镜用于实时可视化和量化晶粒和亚晶粒网络的演变，并基于测量的空间，时间和方向分辨数据集作为初始条件进行微观结构演变模拟。所得的高维数据，然后提炼成一个网络模型，简洁地描述了颗粒和晶界运动的局部驱动力，这显着降低了预测的微观结构演变的计算成本。对热循环过程中异常晶粒生长的科学理解最终为单晶制造的工艺设计提供了信息。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。

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.