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)
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
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.