Abstract
AbstractThe study of microstructure and its relation to properties and performance is the defining concept in the field of materials science and engineering. Despite the paramount importance of microstructure to the field, a rigorous systematic framework for the quantitative comparison of microstructures from different material classes has yet to be adopted. In this paper, the authors develop and present a novel microstructure quantification framework that facilitates the visualization of complex microstructure relationships, both within a material class and across multiple material classes. This framework, based on the stochastic process representation of microstructure, serves as a natural environment for developing relational statistical analyses, for establishing quantitative microstructure descriptors. In addition, it will be shown that this new framework can be used to link microstructure visualizations with properties to develop reduced-order microstructure-property linkages and performance models.
Highlights
It is well understood that advances in the development of materials with enhanced performance characteristics have been critical in the successful development of advanced technology, and are important drivers for continued economic prosperity
2) The representation must be able to be updated in real time as new information or datasets are added to the system 3) The representation must be invertible in real time, so that the microstructure space can be used to explore new microstructures in both an interpolative or extrapolative manner
4) In order to facilitate the computation of descriptive and relational statistics the representation should be an orthogonal decomposition of the data so that each dimension in the reduced frame can be considered as independent variables
Summary
It is well understood that advances in the development of materials with enhanced performance characteristics have been critical in the successful development of advanced technology, and are important drivers for continued economic prosperity. New approaches are required to integrate the looming deluge of data from advanced simulation and characterization techniques into useful materials knowledge While this data crisis is a significant technical challenge, it is an opportunity to explore completely novel inverse approaches to material design and deployment while at the same time reducing our dependency on slow combinatoric experimental approaches. This realization has been highlighted via the Materials Genome Initiative for Global Competiveness [1], the DOE Needs Reports on Computational Materials Science and Chemistry [2] and the continued growth of Integrated Computational Materials Engineering (ICME) [3]. It is widely realized that the community must develop a Materials Innovation Infrastructure (MII) or Materials Innovation Ecosystem [1,2] to exploit fully advanced simulation and coupled experiments, improve predictive capabilities, and provide the design, certification and monitoring tools for rapid and holistic materials development and deployment
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