DMREF: Real Time Control of Grain Growth in Metals

DMREF：金属晶粒生长的实时控制

• 批准号: 1334283
• 负责人: Robert Hull
• 金额: $ 128.52万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334283&HistoricalAwards=false
• 关键词: DMREF; Real; Time; Control; Grain

This project is developing a new methodology to control adaptively the evolution of materials microstructure during thermal processing, through integration of simulation, real-time measurement, control algorithms, and simulated and experimental data sets. The specific implementation focuses on the spatial control of grain microstructure during thermal processing of polycrystalline metals. Monte Carlo and phase field simulations are used to predict the trajectory of microstructure evolution, and real-time measurements of local microstructure, crystallography and temperature using scanning electron microscopy methods are used to track these trajectories experimentally. When measurement shows significant deviation from the planned trajectory, a new trajectory is simulated and implemented through control algorithms. The experimental implementation comprises a multi-zone resistive heating array that enables controlled temperature distributions to be programmed across a macroscopic polycrystalline metal sample in-situ to a scanning electron microscope. Secondary electron imaging and electron backscattered diffraction (EBSD) are employed to monitor the evolution of grain structure and crystallography, while a new method, based upon quantification of diffuse scattering in the EBSD patterns, is employed to measure local temperature distributions using the same electron beam. The resulting real time data streams are compared to the predictions from simulation, and as deviations emerge, the temperature distribution across the sample is adjusted to steer micro-structural evolution back towards the desired state. The control algorithms employed to achieve this are developed based on the linearized phase field models. When the deviations between measurement and simulation become excessive, re-optimization using full Monte-Carlo simulation between intermediate and final states is required. As these methods develop, implementation of feedback control without excessive use of such re-optimization is expected as the libraries of correlated experimental and simulated data sets develop. While these methods inherently measure two-dimensional distributions of grain parameters at the surface, integrated focused ion beam tomography is used to correlate the surface grain microstructure to three dimensional distributions within the bulk, and also to compare to two dimensional ? three dimensional correlations from the simulations.This project focuses on developing a major new capability for control of materials processing: the ability to monitor, evaluate and control the local materials structure of a system as it evolves during thermal processing in order to obtain optimal properties. This is enabled through new methodologies for integrating experiment, simulation, and data management. The specific project implementation comprises control of grain growth during annealing of polycrystalline metals. However, the methodologies developed should be extendible to acceleration of a wide range of process development cycles where specific internal materials structures are known to correlate to optimal materials properties and performance. The research project is integrated with a broad set of educational and outreach activities. High school, undergraduate and graduate students are involved throughout the project. In particular, multiple graduate students who are trained through this research are becoming expert in the integrated application of experimental, simulation and data sciences to the acceleration of materials processing technologies. This is preparing them for future leadership in the development of new materials manufacturing technologies.

该项目正在开发一种新的方法，通过集成模拟、实时测量、控制算法以及模拟和实验数据集，自适应地控制热加工过程中材料微观结构的演变。 具体实施重点是多晶金属热加工过程中晶粒微观结构的空间控制。 蒙特卡罗和相场模拟被用来预测微观结构演变的轨迹，和实时测量的局部微观结构，晶体学和温度，使用扫描电子显微镜的方法被用来跟踪这些轨迹实验。 当测量结果显示出与计划轨迹的显著偏差时，通过控制算法模拟和实施新的轨迹。 实验实施包括多区域电阻加热阵列，其使得受控温度分布能够在宏观多晶金属样品上原位编程到扫描电子显微镜。 采用二次电子成像和电子背散射衍射（EBSD）来监测晶粒结构和晶体学的演变，而一种新的方法，基于量化的漫散射的EBSD图案，使用相同的电子束来测量局部温度分布。将所得到的真实的时间数据流与来自模拟的预测进行比较，并且当出现偏差时，调整样品上的温度分布以将微观结构演变引导回到期望的状态。用于实现这一目标的控制算法是基于线性化相场模型开发的。当测量和模拟之间的偏差变得过大时，需要使用中间状态和最终状态之间的完全蒙特-卡罗模拟进行重新优化。 随着这些方法的发展，随着相关实验和模拟数据集库的发展，预期在不过度使用这种重新优化的情况下实现反馈控制。 虽然这些方法固有地测量在表面处的晶粒参数的二维分布，集成的聚焦离子束断层扫描被用来关联的表面晶粒微观结构的三维分布内的散装，并且还比较到二维？该项目的重点是开发一种新的材料加工控制能力：监测、评估和控制系统在热加工过程中演变的局部材料结构的能力，以获得最佳性能。 这是通过集成实验，模拟和数据管理的新方法实现的。具体的项目实施包括控制多晶金属退火过程中的晶粒生长。 然而，开发的方法应该是可扩展的，以加速广泛的工艺开发周期，其中特定的内部材料结构已知与最佳材料性能和性能相关。 该研究项目与一系列广泛的教育和外联活动相结合。高中生、本科生和研究生都参与了整个项目。 特别是，通过这项研究培训的多名研究生正在成为实验，模拟和数据科学综合应用的专家，以加速材料加工技术。 这为他们未来在新材料制造技术开发方面的领导地位做好了准备。