The knowledge to develop novel steel materials is distributed in various data silos and the data itself is often structured heterogeneously. Ontology-based data access systems are regarded as efficient ways to connect different data sources through a knowledge system that allows for a semantical description of the data. The BMBF-funded project StahlDigital (SteelDigital) aims to utilize such a system for the use case of steel material optimization.  

It is envisioned to demonstrate the complete workflow of steel development including manufacturing, experimental characterization, and simulation. The project investigates methods for the connection of ontology-based process descriptions with related data sets via semantic transformation. Therefore, concepts and tools are developed, that allow for automatic annotations of experimental and simulation data sets. In the proposed concept, the raw data are mapped via a data model with ontological terms. The result is a semantic graph that will be able to integrate with a knowledge graph to link the data to the overall knowledge. A use case based on tabular tensile test data is demonstrated. The use case shows the development of a tensile test ontology as well as the mapping of experimental data.  

The connection of the process steps via an ontology allows for the generation of complex queries over the complete workflow. The data retrieved from the queries can be used for visualization, statistics, process optimization and machine learning applications. Moreover, further operations can be performed on the semantic level. The advantage of our procedure is that isolated opaque data is linked and enriched with meaning, that allows for interpretation by humans as well as automatic processing by machines.