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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…