Khorashadizadeh, A.; Winning, M.; Zaefferer, S.; Raabe, D.: Recrystallization and grain growth in ultra fine grained CuZr alloy processed by high pressure torsion. Materials Science and Engineering MSE 2010, Darmstadt, Germany (2010)
Winning, M.; Khorashadizadeh, A.; Raabe, D.: Characterization of the microstructure of ultra fine-grained materials processed by severe plastic deformation methods in the deformed and the annealed state. Materials Science and Engineering MSE 2010, Darmstadt, Germany (2010)
Winning, M.; Raabe, D.: Fast, physically-based algorithms for on-line calculations of texture and anisotropy during fabrication of steel sheets. Materials Science and Engineering MSE 2010, Darmstadt, Germany (2010)
Winning, M.; Khorashadizadeh, A.; Raabe, D.; Zaefferer, S.: Recrystallization and grain growth in ultra fine grained materials produced by high pressure torsion. Recrystallization & Grain Growth 4 RX&GG, Sheffield, UK (2010)
Uyar, F.; Wilson, S.; Winning, M.; Rollett, A. D.: Interface Texture Evolution During Grain Growth Under the Effect of Stress. Recrystallization & Grain Growth 4 RX&GG, Sheffield, UK (2010)
Uyar, F.; Gruber, J.; Lee, S.; Winning, M.; Rollett, A. D.: Stagnation of Thin Film Grain Growth under the Effect of a Stress Field. Materials Science & Technology 2009 Conference, Pittsburgh, PA, USA (2009)
Khorashadizadeh, A.; Winning, M.; Raabe, D.: Microstructure and Texture evolution during high pressure torsion of a CuZr alloy. Euromat 2009, Glasgow, UK (2009)
Khorashadizadeh, A.; Winning, M.; Raabe, D.: Microstructure and Texture evolution during high pressure torsion of a CuZr alloy. 15th International Conference on the Strength of Materials ICSMA 2009, Dresden, Germany (2009)
Khorashadizadeh, A.; Winning, M.; Zaefferer, S.; Raabe, D.: 3D tomographic EBSD characterization of crystal topology in a CuZr alloy processed by equal channel angular pressing. Interdisciplinary Symposium on 3D Microscopy, Interlaken, Switzerland (2009)
Khorashadizadeh, A.; Raabe, D.; Winning, M.: Microstructure and texture evolution during high pressure torsion of a Cu0.17wt%Zr alloy. DPG Frühjahrstagung 2009, Dresden, Germany (2009)
Schulz, S.; Winning, M.; Raabe, D.: A modified cellular automaton for the simulation of recrystallization in aluminum. ICAA 11 - International Conference on Aluminium Alloys 2008, Aachen, Germany (2008)
Khorashadizadeh, A.; Raabe, D.; Winning, M.: Three-dimensional tomographic EBSD measurements of the crystal topology in heavily deformed ultra fine grained pure Cu and Cu-0.17wt%Zr obtained from ECAP and HPT. 4th International Conference on Nanomaterials by Severe Plastic Deformation nanoSPD 4, Goslar, Germany (2008)
Brahme, A.; Winning, M.; Raabe, D.: Texture Component Model for Predicting Recrystallization Textures. 15th International Conference on the Texture of Materials (ICOTOM 15), Pittsburgh, PA, USA (2008)
Winning, M.; Raabe, D.: Influence of Grain Boundary Mobility on Texture Evolution during Recrystallization. 15 th International Conference on the Texture of Materials (ICOTOM 15), Pittsburgh, PA, USA (2008)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.