Project description

The object of the project is to investigate a 4-stage process chain for the production of a lightweight helmet made of an Al/Mg/Al sandwich structure with properties that can be specifically adjusted. The process chain consists of roll bonding, blank cutting, deep drawing and trimming. The properties that can be adjusted within technological limits are weight, rigidity, shape and dimensional accuracy, and the resulting CO2 footprint.

The process chain is modeled both forward and backward, optimized with respect to the aforementioned properties, and designed. This requires hybrid modeling that combines a generalized layer model for roll bonding with FE-based approaches for the subsequent processing steps. On this basis, a data model will be created that can be generalized for the process chain and enables the exchange even between fundamentally different modeling approaches. A voxel-based modeling strategy in combination with an octree approach will be applied.

In this context, the required (soft) sensors and mathematical methods for hybrid process modeling, optimization and uncertainty quantification in high-dimensional parameter spaces will be developed. In addition to typical measured variables, indirect data will also be collected, such as temperatures in the laminate based on surface measurements and the associated layer thicknesses to derive the remaining formability.

The coupling of models along the process chain, i.e. forward-oriented, enables an assessment of the effects on the accuracy of the final product, which can be verified by experimental investigations. From the synergy of the fully coupled process models in backward design with defined boundary conditions, the best possible process routes can finally be predicted and implemented, taking uncertainties into account. 

Process chain