The transition to renewable energy sources and the introduction of electric vehicles place high demands on the materials used in electrical components. The inherent trade-off between mechanical properties (especially strength and ductility) and physical properties (thermal and electrical conductivity) prevents the use of solid aluminium alloys in such components. To resolve this conflict, the microstructure and texture are being investigated based on changes in composition (alloy design) and process steps (process chain design). Here, the tolerance to Fe and Cu impurities that occur due to the recycling of post-consumer aluminium scrap is investigated. The aim is to determine the extent to which these impurities can be accepted without compromising the required properties. In addition, the extent to which the alloy design allows for an improvement in tolerance as well as microstructure and texture development is examined.
Furthermore, the combined effect of the process chain is being investigated. In continuous casting, controlling the effective casting speed can influence the positioning of the solidification front and thus change the microstructure formation in the strand. In extrusion, the targeted design of the tool geometry allows the state variables acting in the forming zone to be adjusted, which influences microstructure and texture development. Finally, bending qualifies the mechanical properties of the microstructure and texture for different bending states and is taken into account in process control to reduce springback.
A special feature of the holistic view of the process chain is the consideration of the influences of upstream processes in order to reveal path dependency due to preset material properties. To this end, stochastic fluctuations in the processes and their influence on the downstream process steps must be taken into account, simulated and predicted. This requires a path-dependent process chain model that is capable of quantifying the effects of changed input variables on the basis of experimental and numerical data.

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