The field of materials science and engineering (MSE) is currently experiencing a significant paradigm shift towards enhanced digitalization which imposes a huge challenge on researchers, scientists, engineers, and technicians. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web technologies (SWT) are used as they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use.

The collaborative project ‘platform MaterialDigital’ (PMD) aims to solve digitalization tasks and implement digital solutions in the field of MSE in a prototypical manner. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. In this respect, selected MSE methods are semantically represented which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data.

Accordingly, this presentation shows the efforts taken within PMD to store data in accordance with a testing standard compliant semantic representation of a tensile test of metals at room temperature (ISO 6892-1:2019-11). A semantic link of the corresponding tensile test ontology (TTO) and data leads to enhanced data useability and interoperability. The PMD core ontology (PMDco) , developed in PMD and used as mid-level ontology in TTO, is also presented briefly. Moreover, as a best practice example, the acquisition of tensile test data with subsequent generation of knowledge graph data by semantic interconnection using TTO was realized by applying an electronic laboratory notebook (ELN). Corresponding tensile tests were performed by undergraduate MSE students at the Technical University of Darmstadt. The resulting data pipeline, also illustrated in the presentation, enabled a fully-fledged digitally integrated experimental procedure, the approach of which may be transferred to other test series and experiments. In addition to facilitating the acquisition, analysis, processing, and (re)usability of data, this also raises the awareness of students with respect to data structuring and semantic technologies in terms of education and training.