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Hierarchical Materials Informatics
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482 b
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Custom design, manufacture, and deployment of new high performance materials for advanced technologies is critically dependent on the availability of invertible, high fidelity, structure-property-processing (SPP) linkages. Establishing these presents a major challenge because these linkages need to cover unimaginably large dimensional spaces. Materials Informatics demonstrates how it is now possible to automate the acquisition of fairly large experimental datasets and apply sophisticated numerical models on these datasets to examine their response during various processing (manufacturing) steps and/or service conditions, thereby helping to speed up the time needed for the design, development and deployment of important new materials. * Addresses a critical gap in new materials research and development by presenting a rigorous statistical framework for the quantification of microstructure * Contains several case studies illustrating the use of modern data analytic tools on microstructure datasets (both experimental and modeling)
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