DMREF: Real Time Control of Grain Growth in Metals

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

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.