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| Main Task Area: TA-OMS Other related Task Areas: TA-WSD |
| Possible connections within NFDI: NFDI4Ing |
| Material/Data: high strength structural materials / time-temperature-transformation relations to microstructures |
| Main Success Scenario: Users can utilize a knowledge graph populated by process and material data to predict microstructures and their alteration by thermochemical heat treatments. Using a graph based experimental data structure enables a FAIR datastorage to allow for a new level of reproducibility and comparability of experimental results from different experimental setups and groups. |
| Added value for the MatWerk community: Concepts for the combined ontologies will be developed, combining thermo-chemical processing and materials transformation. General framework for heat treatment representation and resulting erroneous and intended experimental impact on materials microstructure. |
Main requirements
- Ontology for representation of heat treatment courses and transformation in structural materials
- Workflow management systems that can be adapted during use (e.g., primary data acquisition, assertion augmentation, heterogeneous post-processing)
- Tools for automated data assembly
- Data augmentation and condensation for advanced data handling
Related Participant Projects
- PP17 Leibniz-Institut für Werkstofforientierte Technologien (IWT)
- PP20 Mat-o-Lab – Materials-open-Laboratory
Description
In structural materials the microstructure formation is triggered using specific thermo- or thermos-chemical heat treatment cycles. The final microstructure derived from a heat treatment is a result of subsequent and parallel transformations which mostly are transient and/or temperature driven. In this IUC, a research data management framework for experimental data with domain-specific semantics is to be designed. Semantic structured data processed in the framework is based on experimental data from different transformation characterizing schemes. Workflows for experimental procedure and data acquisition to data representation will be developed. A ontological representation encompassing both heat treatment courses and material transformation is designed in order to provide a common and unified description. Moreover, scalability from lab size experimental data derived from, e.g., dilatometry and technical furnace equipment is represented.