Research Projects

Internal weld defects such as porosity, inclusions, and lack of fusion cause costly rework in industry—even though many of these imperfections are actually inconsequential for fatigue strength. Modern ultrasonic methods (PAUT, TOFD) can, for the first time, precisely determine the size and position of internal defects, but current standards (ISO 5817, IIW) do not account for edge distance. LIMPER closes this gap: For the first time, scientifically sound, notch class-dependent limit values for internal irregularities are being developed, taking into account location, type, size, and material influence. The results are incorporated directly into ISO 5817 and the IIW recommendations. For steel construction, wind energy, and offshore companies, this means fewer unnecessary repairs, lower manufacturing costs, and better utilization of the actual joint strength.

Blasting processes such as shot blasting are essential for the service life of springs, gears, turbine blades, and welded structures—yet for 80 years, quality control has been based on the simple Almen test, which offers little potential for optimization. For the first time, OptiPeen links the entire process chain—from machine parameters to abrasive dynamics and surface layer condition to component service life—using physics-informed machine learning methods. This enables blasting processes to be optimized in real time: either to maximize service life or to minimize energy and resource consumption while maintaining consistent performance. For industry, this means: up to 20% lower energy consumption in blasting processes, up to a 10% reduction in weight for spring products, and—for the first time—a digital twin of blasted components. The methodology can be applied across industries—from gear manufacturing and spring technology to aerospace.

The steel industry is under enormous pressure to drastically reduce CO₂ emissions while simultaneously bringing high-performance materials to market faster. AID4GREENEST is developing six innovative AI-powered tools that replace the conventional trial-and-error approach in steel development and manufacturing with digital predictive models—from microstructure characterization in under a minute instead of over an hour to creep life prediction without thousand-hour tests. For companies across the entire steel value chain, this opens up opportunities for cost and time savings while simultaneously reducing material scrap. The AI models developed and an open data platform lay the foundation for model-based innovation that can be transferred to other energy-intensive sectors.

As part of the AdHyBau2 joint project, Fraunhofer IWM will investigate additive materials under the influence of hydrogen and cryogenic temperatures using hollow specimens and develop mechanism-based service life models. The aim of the AdHyBau2 project is to develop a hydrogen-electric powertrain for aircraft and to test a cryogenically cooled electric motor.

The design of electric motors is a determining factor in power density. Additive manufacturing processes and hybrid material solutions can open new design possibilities in this area. The aim of the AdHyBau project was to develop powerful, environmentally friendly, and low-noise drives for the mobility of the future.

In the “nanoAR” research project, nine partners from industry and research are working on innovative solutions for optimizing anti-reflective technologies for laser fusion power plants. The aim is to adapt the optical components so that they can withstand the extreme requirements of high laser power and continuous operation.

In the flagship project KSE (Criticality of Rare Earth Elements), Fraunhofer researchers worked on technologies to process and recycle rare earth elements more efficiently or to find substitute materials.

How can manufacturing costs for the support structures of offshore wind turbines be reduced? WeldScanPro-LP is developing an AI-based system that automatically digitizes welds on monopiles via 3D scanning and evaluates their local fatigue strength with spatial resolution using artificial neural networks. For the first time, weld geometry, residual stresses, and microstructure are combined in a data-driven model to systematically reduce conservative design margins. The result replaces the previous manual spot-check inspection with a comprehensive, automated inline inspection. Companies benefit from reduced wall thicknesses, less rework, and digitized quality documentation across the entire weld.

The Baden-Württemberg Quantum Computing Competence Center (KQCBW) aims to further develop quantum computing - a key technology - in Germany and to implement practical applications across various industries.

Quantum computers promise groundbreaking advantages in the simulation of molecules, materials and chemical processes - and the project clarifies when and for which applications organizations can benefit from early engagement. The project establishes the scientific foundation and the transfer infrastructure so that companies can set the course for entering the field of quantum computing in a timely manner. A particular focus is on quantum chemistry and materials science. Through a dedicated quantum cloud, a comprehensive training program and an industry-oriented market analysis, the project empowers companies to tap into the potential of quantum computing at an early stage.