Work

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

Year: 2024

Type: data-paper

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.

Source: Scientific Data

DOI: 10.1038/s41597-024-03169-4

Authors: Bernd Bayerlein, Markus Schilling, Philipp von Hartrott, Jörg Waitelonis

Institutions: Federal Institute For Materials Research and Testing, Fraunhofer Institute for Mechanics of Materials, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funder: Bundesministerium für Bildung und Forschung