Hierarchical Materials Informatics: Novel Analytics for Materials Data

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9780124103948: Hierarchical Materials Informatics: Novel Analytics for Materials Data

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 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)

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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)

About the Author:

Surya R. Kalidindi earned a B.Tech. in Civil Engineering from Indian Institute of Technology, Madras, an M.S. in Civil Engineering from Case Western Reserve University, and a Ph.D. in Mechanical Engineering from Massachusetts Institute of Technology. After his graduation from MIT in 1992, Surya joined the Department of Materials Science and Engineering at Drexel University as an Assistant Professor. In just eight years, he was promoted to the position of the Department Head. Although he was the youngest person to serve as the Department Head, the department experienced tremendous growth during his tenure. The growth of the department was recognized in the annual rankings by Academic Analysts, where it was ranked #10 nationally among Materials Science and Engineering programs in 2006. During 2008-2010, Surya served as the Department Head of Mechanical Engineering at Drexel University. He has also served as the Director of the Centralized Research Facilities in the College of Engineering at Drexel University during 2000-2010. Surya's research efforts over the past two decades have made seminal contributions to the fields of crystal plasticity and microstructure design. His work has already produced about 30 PhDs, over 150 articles in refereed journals, four book chapters, and a new book on Microstructure Sensitive Design to be published by Elsevier in 2012. His work is well cited by peer researchers as reflected by a h-index of 34 and a current citation rate of over 500 citations/year (based on Web of Science). Surya serves as the Director of an NSF REU Site and a DoEd GAANN program at Drexel University. Surya served as a co-Editor-in-Chief of Computers, Materials & Continua (CMC) published by Tech Science Press from 2007-2011. Surya has been elected as a Fellow of ASM International, ASME, and Alpha Sigma Mu (Materials Honors Society).

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United Kingdom, 2015. Hardback. Book Condition: New. 235 x 190 mm. Language: English . Brand New Book. 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 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. Bookseller Inventory # AAZ9780124103948

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United Kingdom, 2015. Hardback. Book Condition: New. 235 x 190 mm. Language: English . Brand New Book. 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 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. Bookseller Inventory # AAZ9780124103948

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Book Description Elsevier Science & Technology. Hardback. Book Condition: new. BRAND NEW, Hierarchical Materials Informatics: Novel Analytics for Materials Data, Surya R. Kalidindi, Stephen R. Niezgoda, 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 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). Bookseller Inventory # B9780124103948

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