Bastos, A.; Raabe, D.; Zaefferer, S.; Schuh, C.: Characterization of Nanostructured Electrodeposited NiCo Samples by use of Electron Backscatter Diffraction (EBSD). Mater. Res. Soc. Sympos. Proc. 880E, BB1.3. (2005)
Godara, A.; Raabe, D.: Mesoscale simulation of the kinetics and topology of spherulite growth during crystallization of isotactic polypropylen (iPP) by using a cellular automaton. (2005)
Huh, M.-Y.; Lee, J.-H.; Park, S. H.; Engler, O.; Raabe, D.: Effect of Through-Thickness Macro and Micro-Texture Gradients on Ridging of 17%Cr Ferritic Stainless Steel Sheet. Steel Research Int. 76, 11, pp. 797 - 806 (2005)
Raabe, D.; Hantcherli, L.: 2D cellular automaton simulation of the recrystallization texture of an IF sheet steel under consideration of Zener pinning. Computational Materials Science 34, pp. 299 - 313 (2005)
Raabe, D.; Romano, P.; Al-Sawalmih, A.; Sachs, C.; Servos, G.; Hartwig, H. G.: Mesostructure of the Exoskeleton of the Lobster Homarus Americanus. Mater. Res. Soc. Sympos. Proc. 874, pp. 155 - 160 (2005)
Raabe, D.; Romano, P.; Sachs, C.; Al-Sawalmih, A.; Brokmeier, H. G.; Yi, S. B.; Servos, G.; Hartwig, H. G.: Discovery of a honeycomb structure in the twisted plywood patterns of fibrous biological nano-composite tissue. Journal of Crystal Growth 283, 1-2, pp. 1 - 7 (2005)
Raabe, D.; Sachs, C.; Romano, P.: The crustacean exoskeleton as an example of a structurally and mechanically graded biological nanocomposite material. Acta Materialia 53, pp. 4281 - 4292 (2005)
Raabe, D.; Wang, Y.; Roters, F.: Crystal plasticity simulation study on the influence of texture on earing in steel. Computational Materials Science 34, pp. 221 - 234 (2005)
Storojeva, L.; Ponge, D.; Raabe, D.; Kaspar, R.: On the influence of heavy warm reduction on the microstructure and mechanical properties of a medium-carbon ferritic steel. Zeitschrift für Metallkunde 95/12, pp. 1108 - 1114 (2004)
Storojeva, L.; Ponge, D.; Kaspar, R.; Raabe, D.: Development of Microstructure and Texture of Medium Carbon Steel during Heavy Warm Deformation. Acta Materialia 52/8, pp. 2209 - 2220 (2004)
Kobayashi, S.; Zaefferer, S.; Schneider, A.; Raabe, D.; Frommeyer, G.: Slip system determination by rolling texture measurements around the strength peak temperature in a Fe3Al-based alloy. Materials Science and Engineering A 387–389, pp. 950 - 954 (2004)
Ma, A.; Roters, F.; Raabe, D.: Numerical study of textures and Lankford values for FCC polycrystals by use of a modified Taylor model. Computational Materials Science 29, 3, pp. 259 - 395 (2004)
Raabe, D.: Overview on the Lattice Boltzmann Method for Nano- and Microscale Fluid Dynamics in Materials Science and Engineering. Modelling and Simulation in Materials Science and Engineering 12, pp. R13 - R46 (2004)
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
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…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.