Project Details
Projekt
A combined Inverse Design and Alloy Simplification approach for alloys with a small ecological footprint based on 3D-printing of Al-Ca-X - IDeAS
Subject Area
Mechanics
Thermodynamics and Kinetics as well as Properties of Phases and Microstructure of Materials
Thermodynamics and Kinetics as well as Properties of Phases and Microstructure of Materials
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 562154592
Aluminum can be recycled almost infinitely with minimal energy consumption. However, a major challenge lies in the introduction of tramp elements such as Fe and Mn through contaminated scrap. These elements form brittle intermetallic compounds (IMCs) that degrade material properties and complicate recycling efforts. To address this, the development of high-performance aluminum alloys with enhanced contamination tolerance is essential. High cooling rates, as provided by Additive Manufacturing (AM), can significantly improve impurity tolerance. As demand for AM alloys continues to grow, processing conventional wrought materials via AM remains difficult due to hot-cracking related to large solidification intervals. Thus, one approach to improving hot-cracking resistance is the use of (near-)eutectic alloys. Calcium, with its low density of 1.55 g/cm³, is a highly promising candidate in this regard. It is abundant and can be effectively removed during recycling. However, pure Al-Ca alloys have only moderate strength. Interestingly, recent studies have shown that Fe and Mn not only avoid degrading Al-Ca alloys’ mechanical properties but can even improve them. Nonetheless, developing optimized alloys typically requires extensive iterative testing, highlighting the need for new methods to reduce experimental efforts. Directed Energy Deposition offers a high-throughput pathway for alloy development, enabling the creation of samples with spatially varying compositions. Crystal plasticity models can be used to predict effective mechanical properties, enabling virtual testing. Machine learning-based inverse design approaches leveraging Bayesian optimization with Gaussian Processes make use of limited experimental data, supplemented by in-silico generated structures and computed properties, to efficiently explore the design space. By combining high-throughput experiments with inverse design, this project is well-suited to quantify the effects of impurity elements and identify how compositional deviations can be balanced through microstructure design by process control to achieve target properties. One key hypothesis is that in (near-)eutectic Al-Ca alloys, the distribution and morphology of intermetallic phases can be controlled by adjusting chemistry and process parameters. The project aims to close scientific gaps by exploring the contamination tolerance of Al-Ca (near-)eutectic compositions in rapid solidification processes, while also developing a data-driven high-throughput workflow to design alloys with high mechanical properties. It will create a contamination-resistant Al-Ca alloy designed specifically for AM, validated through an evaluation of key structural and mechanical properties. Ultimately, we will create (inverse) design workflows that will lay the foundation for a new class of sustainable, high-strength aluminum alloys optimized for rapid solidification.
DFG Programme
Priority Programmes
