A5115528204- Machine Learning in Materials Science
- Image Processing and 3D Reconstruction
- Research Data Management Practices
- Scientific Computing and Data Management
- Conservation Techniques and Studies
- Additive Manufacturing and 3D Printing Technologies
Fraunhofer Institute for Casting, Composite and Processing Technology IGCV
2024-2025
The development of an ontology‐based approach for generating Findable, Accessible, Interoperable, Reusable ( FAIR ) data powder bed fusion, a representative additive manufacturing process, is explored. Addressing key aspects part design, parameter selection, and processing history, the study identifies both advantages disadvantages using ontologies to manage utilize distributed heterogeneous from effectively. Critical this establishment unique digital physical identifiers objects, which...
This article describes advancements in the ongoing digital transformation materials science and engineering. It is driven by domain‐specific successes development of specialized data spaces. There an evident increasing need for standardization across various subdomains to support exchange entities. The MaterialDigital Initiative, funded German Federal Ministry Education Research, takes on a key role this context, fostering collaborative efforts establish unified space. implementation...