000			02031 a2200217 4500
003			OSt
005			20220104125056.0
008			211224b xxu||||| |||| 00| 0 eng d
020			_a9780124103948
040			_cIIT Kanpur
041			_aeng
082			_a620.11 _bK124h
100			_aKalidindi, Surya R.
245			_aHierarchical materials informatics _bnovel analytics for materials data _cSurya R. Kalidindi
260			_bButterworth-Heinemann (Elsevier) _c2015 _aAmsterdam
300			_aix, 219p
520			_aCustom 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 linkages presents a major challenge because of the need to cover unimaginably large dimensional spaces. Hierarchical Materials Informatics addresses objective, computationally efficient, mining of large ensembles of experimental and modeling datasets to extract this core materials knowledge. Furthermore, it aims to organize and present this high value knowledge in highly accessible forms to end users engaged in product design and design for manufacturing efforts. As such, this emerging field has a pivotal role in realizing the goals outlined in current strategic national initiatives such as the Materials Genome Initiative (MGI) and the Advanced Manufacturing Partnership (AMP). This book presents the foundational elements of this new discipline as it relates to the design, development, and deployment of hierarchical materials critical to advanced technologies. 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)
650			_aMaterials science -- Data processing
650			_aInformatics
942			_cBK
999			_c564953 _d564953