Project Details
Projekt
Method based multi-criteria optimization of a process chain for hybrid lightweight component production
Subject Area
Primary Shaping and Reshaping Technology, Additive Manufacturing
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 558607696
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 properties that can be adjusted within technological limits are weight, rigidity, shape and dimensional accuracy, and the resulting CO2 footprint. The synergy of the complete coupling of all process models in the backward design with defined boundary conditions should be used to predict the best possible process routes and implement them, taking uncertainties into account. The necessary (soft) sensors and mathematical methods in the field of hybrid process modeling and optimization, as well as the quantification of uncertainties in the existing high-dimensional parameter spaces, will be developed. In the first project phase, the model coupling in the process flow direction, i.e. forward-oriented, and its effect on the accuracy of the final product are to be carried out and verified by experimental investigations. Initially, a hybrid modeling between generalized layer models for roll cladding and FE approaches for the subsequent processing steps of blanking, deep drawing, trimming and testing is carried out. The aim is to create a data model that can be generalized for interlinked processes and that allows the exchange between fundamentally different model approaches. Based on these models, the reverse design and optimization of the entire process chain is carried out as a central sub-goal of the overall project. The optimization should initially be Pareto-optimized on the basis of the MOR-optimized simulation models and taking into account the model uncertainties, in order to select solutions in post-processing for scalar, weighting-based objective functions. Furthermore, the optimization result should be methodically evaluated here based on a holistic and a cascaded process chain consideration. The process chain is to be equipped with in situ (inline) and ex situ sensors. In addition to the typical measured variables of the sub-processes, indirect data is to be collected, such as temperatures in the layer composite based on surface measurements and the associated layer thicknesses to derive the remaining formability. The uncertainties are determined on the one hand from the measurement data of the process sensors over repeated tests and on the other hand by the numerical process simulation via Monte Carlo simulation. Due to the complex relationships along the process chain and the large number of influencing parameters, a completely new approach is pursued. The uncertainty analysis is carried out both along the graph – this is possible in both forward and backward directions – and via a Monte Carlo approach.
DFG Programme
Priority Programmes
