Project Details
Projekt
Data- and Theory-guided Microstructure Design of High-quality HPDC Secondary Alumi-num Alloys by Micro-alloying and Melt Conditioning
Applicants
Professor Dr.-Ing. Karsten Durst; Professorin Dr.-Ing. Carolin Körner; Professorin Dr.-Ing. Bai-Xiang Xu
Subject Area
Mechanics
Mechanical Properties of Metallic Materials and their Microstructural Origins
Mechanical Properties of Metallic Materials and their Microstructural Origins
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 562095270
In this project, we aim at a theory- and data-guided microstructure optimization and implementation of high-quality secondary Al alloys in high-pressure die casting. We consider the prototype alloy system AlSi10MnMg(+Fe,+Sr), which is in primary quality an alloy for high end structural components. One main issue of the secondary alloys is the increased Fe content through contamination from equipment during processing and recycling, which leads to needle-like, brittle Fe-containing intermetallic phases (IPs) and decreases thus the mechanical properties. We first optimize the morphology of IPs, e.g. the influential descriptors such as size, shape and position of IPs, in order to maintain or improve the mechanical properties e.g. the strain to failure and the yield strength. With our complimentary expertise, we aim to develop first a data-driven Machine-Learning-supported framework for inverse design of IP microstructures, leveraging both experimental and simulated microstructure-property data and their interrelationships. Thereby both the indirect inverse design approach e.g. Bayesian Optimization and the direct inverse design by gradient-based methods will be explored. It should allow us to pinpoint optimized microstructural features that align with desired material properties. Furthermore, to implement the designed optimal microstructure, we will explore two experimental approaches jointly: micro-alloying (refining) through Sr additions and melt conditioning. To guide the efficient search of Fe-/Sr-contents and parameter sets of melt conditioning, the indirect method based on Bayesian Optimization and Gaussian Process on the experimental data will be employed. The overall vision of the project is, to enable foundries to use secondary alloys of this type for producing safety-relevant structural parts for automotive.
DFG Programme
Priority Programmes
