Projects

The aim of the digiMass project is to establish a digital process chain, from bulk forming to structural integrity assessment using deformation history, in such a way that it can be utilized based on existing simulation and analysis tools, thereby promoting energy-efficient component design. This approach integrates material requirements, manufacturing and component functionality into a single optimization loop. Coupled modeling enables a simulation-based determination of the local structural integrity of components, which was previously not possible. This allows components to be designed with significantly lower safety margins, thereby harnessing the aluminum’s high lightweight construction potential to conserve resources.

ClaimsBERT develops a foundation model for routine data from statutory health and long-term care insurance. It is designed to identify patterns in diagnoses, treatments, and medication data in order to predict health risks at an early stage and support more targeted care.

The ACCESS-AD Project is a collaborative initiative that exemplifies the public-private partnership model. It brings together government agencies and private sector organizations to address this unmet need. The project translates advanced diagnostics and monitoring approaches – such as blood-based and imaging biomarkers, as well as digital and AI tools – into coordinated, equitable, and scalable care in real-world clinical settings across Europe.

The project KI-Lectrolyte develops new approaches for creating high-performance, safe electrolytes for next-generation batteries. By combining experimental data with simulations and AI methods, promising electrolyte formulations can be identified much faster. A central database integrates measurement data, simulation results, and AI-based predictions of material properties, enabling targeted analysis for different battery chemistries and supporting data-driven development of new electrolytes. The result is a digital tool that transforms chemical knowledge directly into technological innovation. Fraunhofer SCAI contributes its expertise in AI-driven materials research, data integration, and modeling to the project.

In vehicle development, accurately predicting crash behavior poses a major challenge, as the forming process of lightweight sheet metal components can lead to local material damage in areas of high internal stress, which has so far been insufficiently accounted for. The research project MapUmCra develops new methods to realistically and efficiently transfer such damage from forming simulations to crash simulations. This creates a continuous digital process chain from sheet metal forming to crash testing, enabling more accurate predictions of structural behavior, simplifying material modeling, and significantly reducing development times.