(Georgia Institute of Technology, 2017-08-02)
Cecen, Ahmet
The direct influence of spatial and structural arrangement in various length scales to the performance characteristics of materials is a core premise of materials science. Spatial correlations in the form of n-point statistics have been shown to be very effective in robustly describing the structural features of a plethora of materials systems, with a high number of cases where the obtained futures were successfully used to establish highly accurate and precise relationships to performance measures and manufacturing parameters. This work addresses issues in calculation, representation, inference and utilization of spatial statistics under practical considerations to the materials researcher. Modifications are presented to the theory and algorithms of the existing convolution based computation framework in order to accommodate deformed, irregular, rotated, missing or degenerate data, with complex or non-probabilistic state definitions. Memory efficient personal computer oriented implementations are discussed for the extended framework. A universal microstructure generation framework with the ability to efficiently address a vast variety of geometric or statistical constraints including those imposed by spatial statistics is assembled while maintaining scalability, and compatibility with structure generators in literature.
(Georgia Institute of Technology, 2016-09-09)
Brough, David
The search for optimal manufacturing process routes that results in the combination of desired properties for any application is a highly dimensional optimization problem due to the hierarchical nature of material structure. Yet, this problem is a key component to materials design. Customized materials data analytics provides a new avenue of research in the efforts to address the challenge described above, while accounting for the inherently stochastic nature of the available data. The analytics mine and curate transferable, high value, materials knowledge at multiple length and time scales. More specifically, this materials knowledge is cast in the form of Process-Structure-Property (PSP) linkages of interest to the design/manufacturing experts. The extension of the novel Materials Knowledge Systems (MKS) framework to Process-Structure linkages holds the exciting potential to development full PSP linkages that can be can be leveraged by experts in data science, manufacturing and materials science and engineering communities. PSP linkages are an essential component in the to realize a modern accelerated materials innovation ecosystem. This work describes the methodologies used to extend the MKS framework to Process-Structure linkages and demonstrates their utility.