Khorashadizadeh, A.; Raabe, D.; Winning, M.; Pippan, R.: Recrystallization and Grain Growth in Ultrafine-Grained Materials Produced by High Pressure Torsion. Advanced Engineering Materials 13, pp. 245 - 250 (2011)
Khorashadizadeh, A.; Raabe, D.; Zaefferer, S.; Rohrer, G. S.; Rollett, A. D.; Winning, M.: Five-Parameter Grain Boundary Analysis by 3D EBSD of an Ultra Fine Grained CuZr Alloy Processed by Equal Channel Angular Pressing. Advanced Engineering Materials 13, pp. 237 - 244 (2011)
Winning, M.; Raabe, D.: Fast, Physically-Based Algorithms for Online Calculations of Texture and Anisotropy during Fabrication of Steel Sheets. Advanced Engineering Materials 12, pp. 1206 - 1211 (2010)
Winning, M.; Brahme, A.; Raabe, D.: Prediction of cold rolling textures of steels using an artificial neural network. Computational Materials Science 46, pp. 800 - 804 (2009)
Khorashadizadeh, A.; Winning, M.; Raabe, D.: 3D tomographic EBSD measurements of heavily deformed ultra fine grained Cu-0.17wt%Zr obtained from ECAP. Materials Science Forum 584-586, pp. 434 - 439 (2008)
Molodova, X.; Gottstein, G.; Winning, M.; Hellmig, R. J.: Thermal stability of ECAP processed pure Copper. Materials Science & Engineering A 460 / 461, pp. 204 - 213 (2007)
Molodova, X.; Khorashadizadeh, A.; Gottstein, G.; Winning, M.; Hellmig, R. J.: Thermal Stability of ECAP Processed Pure Cu and CuZr. Inter. Journal of Materials Research 98, pp. 269 - 275 (2007)
Winning, M.; Raabe, D.; Brahme, A.: A texture component model for predicting recrystallization textures. Materials Science Forum 558 / 559, pp. 1035 - 1042 (2007)
Eisenlohr, P.; Winning, M.; Blum, W.: Migration of subgrain boundaries under stress in bi- and multi-granular structures. Physica Status Solidi 200 (2), pp. 339 - 345 (2003)
Zaefferer, S.; Kuo, J. C.; Zhao, Z.; Winning, M.; Raabe, D.: On the influence of the grain boundary misorientation on the plastic deformation of aluminum bicrystals. Acta Materialia 51, pp. 4719 - 4735 (2003)
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
Photovoltaic materials have seen rapid development in the past decades, propelling the global transition towards a sustainable and CO2-free economy. Storing the day-time energy for night-time usage has become a major challenge to integrate sizeable solar farms into the electrical grid. Developing technologies to convert solar energy directly into…
It is very challenging to simulate electron-transfer reactions under potential control within high-level electronic structure theory, e. g. to study electrochemical and electrocatalytic reaction mechanisms. We develop a novel method to sample the canonical NVTΦ or NpTΦ ensemble at constant electrode potential in ab initio molecular dynamics…
The field of micromechanics has seen a large progress in the past two decades, enabled by the development of instrumented nanoindentation. Consequently, diverse methodologies have been tested to extract fundamental properties of materials related to their plastic and elastic behaviour and fracture toughness. Established experimental protocols are…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Electron microscopes offer unique capabilities to probe materials with extremely high spatial resolution. Recent advancements in in situ platforms and electron detectors have opened novel pathways to explore local properties and the dynamic behaviour of materials.