Semantic integration of diverse data in materials science: Assessing Orowan strengthening

Bernd Bayerlein; Markus Schilling; Philipp von Hartrott; Jörg Waitelonis

doi:10.1038/s41597-024-03169-4

Semantic integration of diverse data in materials science: Assessing Orowan strengthening

Sci Data. 2024 Apr 30;11(1):434. doi: 10.1038/s41597-024-03169-4.

Authors

Bernd Bayerlein¹, Markus Schilling², Philipp von Hartrott³, Jörg Waitelonis⁴

Affiliations

¹ Bundesanstalt für Materialforschung- und prüfung (BAM), Unter den Eichen 87, Berlin, 12205, Germany. bernd.bayerlein@bam.de.
² Bundesanstalt für Materialforschung- und prüfung (BAM), Unter den Eichen 87, Berlin, 12205, Germany.
³ Fraunhofer Institute for Mechanics of Materials IWM, Wöhlerstrasse 11, Freiburg, 79108, Germany.
⁴ Leibniz Institute for Information Infrastructure (FIZ Karlsruhe), Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany.

Abstract

This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.

Publication types

Dataset
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Abstract

Publication types

Grants and funding