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, S. 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, S. 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, S. 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, S. 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, S. 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, S. 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, S. 269 - 275 (2007)
Winning, M.; Raabe, D.; Brahme, A.: A texture component model for predicting recrystallization textures. Materials Science Forum 558 / 559, S. 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), S. 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, S. 4719 - 4735 (2003)
Wissenschaftler des Max-Planck-Instituts für Eisenforschung entwickeln ein neues maschinelles Lernmodell für korrosionsresistente Legierungen. Und veröffentlichen ihre Ergebnisse in der Fachzeitschrift Science Advances
Düsseldorfer Max-Planck-Wissenschaftler diskutieren den Einsatz künstlicher Intelligenz in der Materialwissenschaft und veröffentlichen Review-Artikel in der Fachzeitschrift Nature Computational Science