Liu, W. C.; Man, C.-S.; Raabe, D.; Morris, J. G.: Effect of hot and cold deformation on the recrystallization texture of continuous cast AA 5052 aluminum alloy. Scripta Materialia 53 (11), pp. 1273 - 1277 (2005)
Song, R.; Ponge, D.; Raabe, D.: Influence of Mn Content on the Microstructure and Mechanical Properties of Ultrafine Grained C–Mn Steels. ISIJ International 45/11, pp. 1721 - 1726 (2005)
Sandim, H. R. Z.; Raabe, D.: EBSD study of grain subdivision of a Goss grain in coarse-grained cold-rolled niobium. Scripta Materialia 53 (2), pp. 207 - 212 (2005)
Song, R.; Ponge, D.; Raabe, D.: Improvement of the work hardening rate of ultrafine grained steels through second phase particles. Scripta Materialia 52/11, pp. 1075 - 1080 (2005)
Song, R.; Ponge, D.; Raabe, D.; Kaspar, R.: Microstructure and crystallographic texture of an ultrafine grained C–Mn steel and their evolution during warm deformation and annealing. Acta Materialia 53 (3), pp. 845 - 858 (2005)
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)
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
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.