Model-based property design along the continuous casting, extrusion and bending process chain
Project 2 - DFG GEPRIS 558587135
Project description
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 using high-strength aluminum alloys in such components.
To resolve this conflict, the microstructure and texture are analyzed based on the changes in composition (alloy design) and process steps (process chain design). Here, the tolerance to iron and copper impurities that occur due to the recycling of post-consumer aluminum scrap is investigated. The aim is to determine how these impurities can be accepted without impairing the required properties. In addition, the extent to which the alloy design allows an improvement in tolerance as well as microstructure and texture development will be examined.
The combined effect of the process chain is also investigated. In continuous casting, the positioning of the solidification front can be influenced by controlling the effective casting speed, thereby changing the microstructure formation in the strand. In extrusion, the die design enables the state variables acting in the forming zone to be adapted, thereby influencing the microstructure and texture development. Finally, bending qualifies the mechanical properties of the microstructure and texture for different bending states and takes them into account in the process control to reduce spring back.
A special feature of the holistic view of the process chain is the consideration of the influences of upstream processes to uncover the path dependency due to preset material properties. For this purpose, stochastic fluctuations in the processes and their influence on the downstream process steps must be considered, modeled, and predicted. This requires a path-dependent process chain model that can quantify the effects of changing input variables based on experimental and numerical data.
Contact
Professorship for Manufacturing – Innovative Manufacturing, Leuphana University Lüneburg
Project management: Prof. Dr.-Ing. Noomane Ben Khalifa; noomane.ben_khalifa(at)leuphana.de
Project team: Dr.-Ing. Sebastian Thiery; sebastian.thiery(at)leuphana.de
Institute for Physical Metallurgy and Materials Physics, RWTH Aachen
Project management: Dr.-Ing. Stefanie Sandlöbes-Haut
Project team: t.b.d.
Chair of metal Forming and Casting, Technical University of Munich
Project management: Prof. Dr.-Ing. Wolfram Volk
Project team: Simon Kammerloher, simon.kammerloher(at)tum.de
