Projects

© RUB

Project team:

Daniel Balzani
Yuki Nishizawa
Chair of Continuum Mechanics , Ruhr-Universität Bochum

Arne Röttger 
Johannes Kleine
Chair of New Manufacturing Technologies and Materials, Bergische Universität Wuppertal

The primary objective of this project is to develop methods for designing new lean-alloy high-speed steels (HSS) with maximized performance properties, thereby increasing recyclability and, consequently, sustainability. As the service life of HSS in operation is relatively short, resulting in numerous recycling cycles per year, the amounts of recycled material per year and thus the potential benefits of the new HSS are substantial. Furthermore, these steels share a similar alloy and material design; therefore, it may be possible to reduce the number of alloy variations by deriving suitable substitution alloys. Reducing the number of alloying elements, especially those that are critical, i.e., limited or costly ones (lean-alloy approach), would further enhance recyclability. In this project, methods will be developed to quantify the complex interactions between chemical composition, microstructure morphology, and microscopic phase properties, as well as their impact on overall performance properties. Starting from a specific, new alloying concept, CALPHAD and phase-field simulations will be employed to obtain information on the microstructure and chemical composition of individual phases as a function of manufacturing process parameters. To quantify mechanical properties, both ab initio calculations and experimental testing will be considered, which will inform the computer simulation of large sets of microstructures required as training data to construct an ML-based surrogate for describing structure-property linkages. This surrogate is then used within an automated optimization of microstructure morphology and single-phase properties by incorporating tolerable microstructure variations and additional constraints to reflect a sufficient manufacturability of the HSS. Based on these results, the identified tool steel will be produced, tested, and its real performance and limitations will be assessed, resulting in updated alloying concepts entering the iterative loop of analysis and microstructure optimization.

© RWTH_Aachen

Project team:

Ulrich Krupp
Marion Kreins
Niklas Veltmann
Steel Institute, RWTH Aachen University

Tilmann Beck
Marek Smaga
Piriyanga Thevaruban
Institute of Materials Science and Engineering, RPTU

Electrification and the circular economy demand increased recycling of steel grades, including electrical steels. Designing efficient electric motors for e-mobility requires alloys that balance mechanical fatigue strength, magnetic degradation, and impurity tolerance. As mechanical and magnetic properties are microstructurally linked, both must be considered in alloy development. Precipitates like carbo-nitrides or copper significantly influence these properties depending on composition and heat treatment. To address this complexity, the project aims to develop a digital twin, supported by high-throughput experiments, to enable data-driven design of advanced electrical steel grades. The collaboration between WKK at RPTU Kaiserslautern-Landau and IEHK at RWTH Aachen University covers the entire value chain. IEHK uses advanced simulations for precipitation and recrystallization, informed by varied experimental parameters. WKK applies high throughput methods like Cyclic Indentation Tests (CITs) and 3MA micromagnetic measurements for rapid mechanical and magnetic characterization. Machine learning-assisted metallography and non-destructive techniques such as X-ray diffraction, MOKE microscopy, and magnetic loss testing support microstructure-property correlation and future inverse material design.

© FZ-Juelich

Project team:

Michael Budnitzki
Institute for Advanced Simulations - Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH

Silja-Katharina Rittinghaus
Mohamed Alshahat
Chair of Materials Science and Additive Manufacturing, University of Wuppertal

The IDeAS project is working to design the next generation of lightweight, eco-friendly aluminum alloys for 3D printing. Traditional high-performance alloys often rely on rare or expensive elements, making them costly and difficult to recycle. Our approach is different: we focus on aluminum-calcium alloys that are easier to recycle, more sustainable, and  can be made strong enough for mechanically demanding applications. By combining modern high-throughput 3D-printing technologies with powerful computer models and artificial intelligence, the project explores how to fine-tune alloy recipes and microstructure design quickly and efficiently—potentially cutting down years of trial-and-error development. The result will be new materials that can tolerate impurities from recycled scrap while maintaining excellent mechanical performance. In the long term, IDeAS aims not only to deliver greener metals but also to provide a blueprint for accelerating alloy development and supporting a truly circular, resource-efficient economy.

© project_4.png

Project team:

Oana Cojocaru-Mirédin
Naveen Karri
INATECH, University of Freiburg

Dirk Helm
Lukas Morand
Yoav Nahshon
Fraunhofer Institute for Mechanics of Materials IWM

The research project focuses on enhancing sustainability in aluminum alloy production through digital methods for optimizing composition-process-structure-property (CPSP) relationships. The secondary raw material route for aluminum alloys is particularly important for both industry and society to pave the way for a green future. Despite advances in separation technologies, reproducing exact alloy mixtures and avoiding impurities in the material cycle remains a challenge. This necessitates the design of impurity-resistant alloys suited to current material flows, along with tailored lean alloys for long-term sustainability. To address this challenge, CPSP relationships must be modeled using data from multiscale characterization techniques, employing physics-based approaches to represent thermo-chemo-mechanical coupling phenomena, followed by optimization using machine learning for materials design. The integration of advanced correlative microscopy techniques, such as atom probe tomography supports the modelling tasks by providing data and enhancing the understanding of the CPSP relationships. Correlative microscopy with aluminum alloys, becomes possible with new cryo-FIB (focused ion beam) approaches, allowing for a deeper understanding of CPSP relationships in aluminum alloys.

© NEFM/TUD

Project team:

Markus Kästner
Tom Schneider 
Professur für Numerische und Experimentelle Festkörpermechanik, TU Dresden

Dierk Raabe
Mohammed Waleed
Max-Planck-Institute for Sustainable Materials (MPI-SusMat), Düsseldorf

Gerhard Dehm
Structure and Nano-/ Micromechanics of Materials, MPI-SusMat

Anwesha Kanjilal
Nicolo Maria della Ventura
Thermo-Chemomechanics and Interfaces, MPI-SusMat

The objective of this project is to design novel sustainable wrought Al alloys with maximum scrap content while ensuring moderate formability in the range of 20% elongation under tensile loading and to understand which second-phase particles can be tolerated under which conditions. To achieve this goal, we develop a data-driven inverse design approach that combines knowledge-guided experimental investigations with high-fidelity modeling and simulation to set up, analyze and invert Composition-Process-(Micro)Structure-Property (CPSP) linkages that will be represented in terms of surrogate models using microstructural descriptors.

Projektteam

© TUB

Project team:

Christian Haase
Soudip Basu
Chair for Materials for Additive Manufacturing, TU Berlin

Jaan-Willem Simon
Finnja Jellen
Chair of Computational Applied Mechanics, Universität Wuppertal

Two aspects are addressed in this project: (i) the resulting chemical composition(s) of recycling-based crossover austenitic stainless steels (ASS) will deviate strongly from currently standardized grades, and (ii) the amount of tramp elements (e.g., P, S, Cu) is inevitably enhanced. Profound investigation of these aspects, specifically the influence of varying chemical compositions on the resulting process-structure-properties (P-S-P) relationships will be quantitatively assessed by combined experimental and numerical approaches. Based on these P-S-P linkages, it is our overall objective to develop a data-driven inverse design strategy to discover novel mixtures of various sorts of stainless steel scrap, which enable fabrication based on 100% recycling material and thus, enhance the sustainability of these steels. Furthermore, we aim to investigate the influence of impurities in the compositions on microstructure and mechanical properties. As one of the main novelties of this project, we plan to address not only stiffness or plastic deformation, but also hardening and strength, i.e., the coupling between damage and plasticity as well as debonding in grain boundaries of novel crossover ASS. Integrated in the overall comprehensive design framework (high-throughput screening, synthesis, and characterization combined with machine learning-based inverse design), this allows us to understand and evaluate the role of scrap mixtures and contained tramp elements on the mechanical properties. In particular, the considered effective properties include damage-resistance, which is prerequisite for designing robust alloys with enhanced sustainability.

© Leuphana

Project team:
Benjamin Klusemann
Usman Aziz
Institute for Production Technology and Systems, Leuphana University Lüneburg

Volker Schmidt
Léon Schöder
Institute of Stochastics, Ulm University

Uceu Suhuddin
Helmholtz-Zentrum hereon

In the present project, we will leverage experimentally measured data of processed recycled aluminium chips via friction extrusion, parametric stochastic 3D modeling and micromechanical modeling on basis of crystal plasticity to quantify process-structure-property relationships. In particular, the project aims to establish an experimental basis for friction extrusion of Al chips with varying oxide content to directly enhance the recyclability and sustainability of Al chips, while also utilizing Al oxide as a reinforcing agent to produce value-added materials such as metal matrix composites. We pursue a quantitative characterization and prediction of 3D microstructures via data-driven stochastic microstructure modeling approaches in combination with generative adversarial networks to predict 3D microstructure based on experimental 2D image data. Furthermore, after establishing quantitative process-structure-property relationships by combining experiments with data-driven stochastic and micromechanical modeling, an inverse design strategy will be pursued to identify process parameters that lead to desirable microstructures, and to determine microstructures with tailored mechanical properties.

© project_9.png

Project team:

Martina Zimmermann
Sebastian Biastoch
Chair of Mechanics of Materials and Failure Analysis, TU Dresden

Lisa Scheunemann
Ahmad Awad
Institute of Applied Mechanics, RWTH Aachen University

Austenitic stainless-steels (ASS) used as sheet-metal in many industry sectors, obtain their final shape through metal forming. Among them, metastable ASS show deformation-induced austenite-to-martensite transformation, the amount of which strongly varies depending on the stacking fault energy (SFE). The volume fraction of austenite and martensite in the microstructure, especially in the interplay with nonmetallic inclusions, plays a major role for mechanical behavior, static and cyclic strength. The SFE is, however, strongly influenced by the chemical composition of the material, with ASS having a distinct valid window of alloying elements. Classical sorting techniques during recycling cannot ideally distinguish minor differences in chemical composition, leading to variations in alloy elements and thus SFE. The assessment of the relationship between process parameters (deformation history), microstructure (austenite-martensite microstructure with non-metallic inclusions) and property (static and cyclic mechanical behavior) is realized in this project through a data-based approach, combining expertise from material science and mechanics. The goal is to identify microstructure-property linkages to be used in an inverse approach. Based on experimental micrographs and strength measurements, microstructural and material data for numerical simulation is provided. Through quasistatic numerical simulation of different microstructure representations, a training database is gained. Suitable in the case of sparse data, a physics-enhanced neural operator framework is trained to describe the relation between microstructural descriptors and microstructurally re-lated stress response. These stress maps are analyzed regarding indicators for static and fatigue strength. The inverse approach identifies micro-structural descriptors of distinct indicators through optimization and creates a reversed structure-property link. A further link to process parameters and -history can be obtained through knowledge based evaluation of the microstructure. Thereby, loss of strength due to repeated recycling and poor scrap sorting can be counteracted.

© TUD / NEFM

Project team:

Wenwen Song
Xu Hanyu
https://www.uni-kassel.de/maschinenbau/institute/werkstofftechnik/fachgebiete/granularitaet-werkstofftechnischer-strukturinformation.html

Enzo Liotti
Aggarwal Akash
https://www.materials.ox.ac.uk/peoplepages/liotti.html

In this proposal, we aim at developing a data-driven methodology to tackle harmful tramp element segregation problem at hot working temperatures, with a special focus on the Cu contamination-induced hot shortness and Cu segregation on grain boundary during scrap-based steelmaking. With joint expertise from materials science for the Process – Structure (PS) link (Dr. Liotti’s group at University of Oxford) and materials mechanics for the Structure – Property (SP) link (Prof. Song’s group at University of Kassel), we aim to establish a comprehensive PSP forward and inverse design framework. The forward semicircle is dedicated to augment the tolerance and manage the segregation of tramp element during solidification and homogenization processes, together with the simulation of tensile strength via training machine-learning based interatomic potentials. The backward semicircle is, on the contrary, devoted to establishing an inverse design PSP correlation that takes user requests and suggests promising microstructure of the material and a series of experimental parameters to achieve the suggested microstructures experimentally. The inverse design will be implemented via Bayesian optimization algorithm. A combination of high-throughput experimental (i.e. in-situ X-ray imaging, micromechanical testing) and theoretical techniques (i.e. phase-field, DFT and MD) will be carried out to monitor and simulate the microstructure evolution, element segregation and the consequent mechanical property.