SPP 2489:  DaMic - Data-driven alloy and microstructure design of sustainable structural metals

• Subject Area: Materials Science and Engineering ; Mechanical and Industrial Engineering
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 540866259

The production and processing of metallic materials currently account for 40% of all industrial greenhouse gas emissions. The extraction of the associated minerals also produces several billion tons of by-products every year, some of which are harmful. It is therefore imperative that the metallic materials of the future become more sustainable. The Priority Programme (PP) DaMic presented here is intended to lay the essential scientific foundations for this development and contribute to the establishment of a new field of research at the interface of digitalization and sustainability. The aim of DaMic is to develop digital methods for inverse materials design and to use them to create new, sustainable and recycling-adapted metallic structural materials. In the form of alloys with a reduced number of elements and thus better compatibility, so-called lean alloys, or material systems with a high tolerance to impurities from the use of secondary raw materials in the sense of the science of Dirty Alloys, two fundamental options for improving recyclability and sustainability are the focus of the investigations. In both cases, negative effects of the modified alloy compositions are to be compensated by a targeted design of the microstructure of the material so that the mechanical properties are comparable with currently available structural materials. Understanding and predicting the complex interactions between composition, process, microstructure and resulting properties are crucial to the success of this approach. The projects bundled in DaMic carry out research on the development and application of a data-driven approach for cross-scale exploration and materials design in a coherent manner. In particular, machine learning (ML)-based design approaches based on digital process-structure-property (PSP) linkages will be used. In view of the complexity and the interacting influences on the mechanical properties, only the combination of experiment and simulation in a data-driven framework opens up the possibility of identifying suitable constellations with regard to alloy composition, process parameters, microstructure and properties. The resulting scientific challenges are tackled by interdisciplinary tandem projects that bring together experts from the fields of mechanics and materials science. In the first funding period, the foundations for the prediction and inversion of the PSP linkages for digital materials design will be created. The second funding period will then focus on the development of end-to-end, fully automated workflows for the alloy and microstructure design of sustainable metallic structural materials.

• DFG Programme: Priority Programmes
• International Connection: USA

Projects

• A combined Inverse Design and Alloy Simplification approach for alloys with a small ecological footprint based on 3D-printing of Al-Ca-X - IDeAS (Applicants Budnitzki, Michael ;  Rittinghaus, Silja-Katharina )
• Coordination Funds (Applicant Kästner, Markus )
• Data- and Theory-guided Microstructure Design of High-quality HPDC Secondary Alumi-num Alloys by Micro-alloying and Melt Conditioning (Applicants Durst, Karsten ;  Körner, Carolin ;  Xu, Bai-Xiang )
• Data-driven design of the 3D microstructure and mechanical properties of recycled and upcycled aluminum chips by utilizing aluminum oxide via friction extrusion process (Applicants Klusemann, Benjamin ;  Schmidt, Volker )
• Developing crossover austenitic stainless steel based on scrap recycling via forward experimental and numerical high-throughput assessment of P-S-P linkages and inverse data-driven design (Applicants Haase, Christian ;  Simon, Jaan-Willem )
• Development of a data driven approach for inverse design of microstructure-property linkages of austenitic steels and recycling caused variation in chemical composition (Applicants Scheunemann, Lisa ;  Zimmermann, Martina )
• Digital methods for data-driven inverse materials design by optimizing composition-process-structure-property relationships for aluminum alloys based on semantic data management technologies, physic-based modelling, and correlative microscopy (Applicants Cojocaru-Mirédin, Ph.D., Oana ;  Helm, Dirk )
• Impurity-Tolerant Alloy Design of High-Strength Electrical Steel (Applicants Beck, Tilmann ;  Kreins, Marion Cornelia ;  Krupp, Ulrich ;  Smaga, Marek )
• Inverse design of sustainable wrought aluminum alloys with significant cast scrap content (Applicants Dehm, Gerhard ;  Kästner, Markus ;  Raabe, Dierk )
• Optimizing Recyclability and Sustainability of High-Speed Steels via Data-Driven Microstructural Design (Applicants Balzani, Daniel ;  Röttger, Arne )

• Spokesperson: Professor Dr.-Ing. Markus Kästner