5 Werkstofftechnik
Filtern
Dokumenttyp
- Zeitschriftenartikel (1)
- Vortrag (1)
Sprache
- Englisch (2)
Schlagworte
- Ontology (2) (entfernen)
Organisationseinheit der BAM
- 8.0 Abteilungsleitung und andere (2) (entfernen)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (1)
The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.
Towards Interoperability: Digital Representation of a Material Specific Characterization Method
(2022)
Certain metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which represent obstacles to dislocation movement. Precise tuning of the material structure is critical for optimal mechanical behavior in the application. Therefore, analysis of the microstructure and especially the precipitates is essential to determine the ideal parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in a first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and to quantify their relevant dimensions.
The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, an automatable, digital approach is demonstrated. Based on DF-TEM images of different precipitation states of a wrought aluminum alloy, a modularizable digital workflow for quantitative precipitation analysis is presented. The integration of this workflow into a data pipeline concept will also be discussed. Thus, by using ontologies, the raw image data, their respective contextual information, and the resulting output data from the quantitative precipitation analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure the reproducibility of the data. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
The presented work is part of two digitization initiatives, the Platform MaterialDigital (PMD, materialdigital.de) and Materials-open-Laboratory (Mat-o-Lab).