Microstructural Evolution via Stochastic Morphology Reconstruction from Limited Tomography Data: Modeling, Simulation, and Experimental Verification

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Technical Abstract. The intent of this project is to dramatically improve our ability to quantitatively characterize and predict real-time microstructural evolution of heterogeneous materials including metallic alloys, ceramics, composites and granular media under various conditions, based on the morphological information contained in limited tomography data. An intrinsic understanding and knowledge of complex microstructures and how they evolve under various conditions is extremely important to the design of novel materials and achieving optimal material performance. The large volume of data (several 100 GB data for a single static microstructure) required in traditional tomography reconstructions significantly limits their application in characterizing dynamically evolving microstructures. We are thus motivated to find alternative methods to statistically characterize and predict in situ microstructure evolution with a minimal set of tomography data that can be obtained in a few measurements. The underlying theme of the proposed research is to systematically investigate and quantify the information content of the tomography data obtainable, via current experimental procedures, in order to improve the utility of such data for microstructural characterization and prediction, provide efficient protocols for data analysis and management, and suggest novel experimental procedures for data collection. We propose to quantify structural information using time-dependent spatial correlation functions. We propose novel mathematical, computational, and physical approaches to modeling, predicting, and experimentally verifying material microstructure evolution from limited tomography data via stochastic morphology reconstructions, which will lead to the development of freely available integrated software package for efficient quantitative 4D (3D + temporal) microstructure characterization and prediction based on available tomography data.Enhancing materials education and public awareness of the importance of imaging and visualization in material research is a crucial component of our proposal. We propose a diverse educational and outreach program that is integrated with the research program, which includes creation of interactive microstructure visualization software for K-12 students, recruitment and involvement of underrepresented female and minorities, project-based activities and research opportunities for students at ASU, and creation of a website on dynamical microstructure visualization. These activities will serve to disseminate, educate, and involve students at all levels as well as the public-at large.Non-technical Abstract. Heterogeneous materials abound in nature and man-made structures. Examples include composites, ceramics, alloys, stand stone, and bone. Such materials usually exhibit complex microstructures on both large and small scales, and the microstructures determine the macroscopic properties and performance of the materials. The design of novel materials and achieving optimal material performance rely on our ability to characterize and modify material properties and behaviors under a myriad of external stimuli, such as thermal, mechanical, and electrical. Thus, an intrinsic understanding and knowledge of complex microstructures and how they evolve under various conditions is extremely important. Traditional imaging techniques (such as x-ray tomography) usually require a large amount of data to render a single snapshot of the microstructure. This significantly limits their application to capture the entire evolution process of the material of interest. In the proposed project, we will investigate how much useful structural information is contained in typical tomography data and whether methods can be devised that can smartly utilize the most crucial structural information to accurately and rapidly render snapshots of material microstructures. We propose novel mathematical, computational, and physical approaches to modeling, predicting, and experimentally verifying material microstructure evolution from limited experimental data. Enhancing materials education and public awareness of the importance of imaging and visualization in material research is a crucial component of our proposal. We propose a diverse educational and outreach program that is integrated with the research program, which includes creation of interactive microstructure visualization software for K-12 students, recruitment and involvement of underrepresented female and minorities, project-based activities and research opportunities for students at ASU, and creation of a website on dynamical microstructure visualization. These activities will serve to disseminate, educate, and involve students at all levels as well as the public-at large.