Roters, F.; Ma, A.: Ein nicht lokales Versetzungsdichte basiertes konstitutives Gesetz für Kristall-Plastizitäts-Finite-Elemente-Simulationen. Institutsseminar, Fraunhofer-Institut für Werkstoffmechanik IWM, Freiburg (2005)
Roters, F.; Ma, A.: Die Kristall-Plastizitäts-Finite-Elemente-Methode und ihre Anwendung auf Bikristall-Scherversuche. Institutsseminar, Institut für Werkstoffwissenschaften, Universität, Erlangen-Nürnberg (2005)
Roters, F.; Jeon-Haurand, H. S.; Raabe, D.: A texture evolution study using the Texture Component Crystal Plasticity FEM. Plasticity 2005, Kauai, USA (2005)
Raabe, D.; Roters, F.: How do 10^10 crystals co-deform. "Weitab vom Hooksechen Gesetz -- Moderne Ansätze und Ingenieurpraxis großer inelastischer deformation metallischer Werkstoffe'' Symposium der Akademie der Wissenschaften und der Literatur, Mainz, Germany (2004)
Raabe, D.; Roters, F.: Physically-Based Large-Scale Texture and Anisotropy Simulation for Automotive Sheet Forming. TMS Fall meeting, New Orleans, LA, USA (2004)
Roters, F.: Das Anwendungspotential der Kristallplastizitäts-Finite-Elemente-Methode aus Sicht der werkstoffphysikalischen Grundlagen. Werkstoffwoche 2004, München, Germany (2004)
Roters, F.; Ma, A.; Raabe, D.: The Texture Component Crystal Plasticity Finite Element Method. Keynote lecture at the Third GAMM (Society for Mathematics and Mechanics) Seminar on Microstructures, Stuttgart, Germany (2004)
Roters, F.: Numerische Simulation der Metallumformung und Rekristallisation. Workshop, Simulation und numerische Modellierung, Materials Valley e.V., Mainz (2003)
Wang, Y.; Roters, F.; Raabe, D.: Simulation of Texture and Anisotropy during Metal Forming with Respect to Scaling Aspects. 1st Colloquium Process Scaling, Bremen, Germany (2003)
Roters, F.: Crystal plasticity FEM from grain scale plasticity to anisotropic sheet forming behaviour. 13th international Workshop on Computational Modelling of the Mechanical Behaviour of Materials, Magdeburg, Germany (2003)
Raabe, D.; Helming, K.; Roters, F.; Zhao, Z.; Hirsch, J.: A Texture Component Crystal Plasticity Finite Element Method for Scalable Large Strain Anisotropy Simulations. ICOTOM 13, Seoul, South Korea (2002)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
A novel design with independent tip and sample heating is developed to characterize materials at high temperatures. This design is realized by modifying a displacement controlled room temperature micro straining rig with addition of two miniature hot stages.
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
In this project we developed a phase-field model capable of describing multi-component and multi-sublattice ordered phases, by directly incorporating the compound energy CALPHAD formalism based on chemical potentials. We investigated the complex compositional pathway for the formation of the η-phase in Al-Zn-Mg-Cu alloys during commercial…
The project HyWay aims to promote the design of advanced materials that maintain outstanding mechanical properties while mitigating the impact of hydrogen by developing flexible, efficient tools for multiscale material modelling and characterization. These efficient material assessment suites integrate data-driven approaches, advanced…
The Atom Probe Tomography group in the Microstructure Physics and Alloy Design department is developing integrated protocols for ultra-high vacuum cryogenic specimen transfer between platforms without exposure to atmospheric contamination.
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.