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Integrating Materials and Manufacturing Innovation
Erschienen in: Integrating Materials and Manufacturing Innovation 1/2017

15.03.2017 | Research

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

verfasst von: David B Brough, Daniel Wheeler, Surya R. Kalidindi

Erschienen in: Integrating Materials and Manufacturing Innovation | Ausgabe 1/2017

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Abstract

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data-driven process-structure-property (PSP) linkages provide a systemic, modular, and hierarchical framework for community-driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open-source materials data science framework that can be used to create high-value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning, and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.
Vorheriger Artikel Application-Specific Computational Materials Design via Multiscale Modeling and the Inductive Design Exploration Method (IDEM)
Nächster Artikel Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

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Metadaten
Titel
Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials
verfasst von
David B Brough
Daniel Wheeler
Surya R. Kalidindi
Publikationsdatum
15.03.2017
Verlag
Springer International Publishing
Erschienen in
Integrating Materials and Manufacturing Innovation / Ausgabe 1/2017
Print ISSN: 2193-9764
Elektronische ISSN: 2193-9772
DOI
https://doi.org/10.1007/s40192-017-0089-0

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