Han, C. S.; Ma, A.; Roters, F.; Raabe, D.: A Finite Element approach with patch projection for strain gradient plasticity formulations. International Journal of Plasticity 23, pp. 690 - 710 (2007)
Kobayashi, S.; Zaefferer, S.; Raabe, D.: Relative Importance of Nucleation vs. Growth for Recrystallisation in Particle-containing Fe3Al Alloys. Materials Science Forum 550, not specified, pp. 345 - 350 (2007)
Ma, A.; Roters, F.; Raabe, D.: A dislocation density based constitutive law for BCC materials in crystal plasticity FEM. Computational Materials Science 39, pp. 91 - 95 (2007)
Raabe, D.: A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size. Modelling and Simulation in Materials Science and Engineering 15, pp. 39 - 63 (2007)
Raabe, D.: Recrystallization Models for the Prediction of Crystallographic Textures with Respect to Process Simulation. The Journal of Strain Analysis for Engineering Design 42 (4), pp. 253 - 268 (2007)
Raabe, D.; Al-Sawalmih, A.; Yi, S. B.; Fabritius, H.: Preferred crystallographic texture of α-chitin as a microscopic and macroscopic design principle of the exoskeleton of the lobster Homarus americanus. Acta Biomaterialia 3, pp. 882 - 895 (2007)
Sandim, H. R. Z.; Bernardi, H. H.; Verlinden, B.; Raabe, D.: Equal channel angular extrusion of niobium single crystals. Materials Science and Engineering: A 467, pp. 44 - 52 (2007)
Takahashi, T.; Ponge, D.; Raabe, D.: Investigation of orientation gradients in pearlite in hypoeutectoid steel by use of orientation imaging microscopy. Steel Research International 78 (1), pp. 38 - 44 (2007)
Tikhovskiy, I.; Raabe, D.; Roters, F.: Simulation of earing during deep drawing of an Al-3%Mg alloy (AA 5754) using a texture component crystal plasticity FEM. Journal of Materials Processing Technology 183, pp. 169 - 175 (2007)
Winning, M.; Raabe, D.; Brahme, A.: A texture component model for predicting recrystallization textures. Materials Science Forum 558 / 559, pp. 1035 - 1042 (2007)
Zambaldi, C.; Roters, F.; Raabe, D.; Glatzel, U.: Modeling and experiments on the indentation deformation and recrystallization of a single‑crystal nickel-base superalloy. Materials Science and Engineering A 454–455, pp. 433 - 440 (2007)
Liu, W. C.; Li, Z.; Man, C.-S.; Raabe, D.; Morris, J. G.: Effect of precipitation on rolling texture evolution in continuous cast AA 3105 aluminum alloy. Materials Science and Engineering: A 434 (1-2), pp. 105 - 113 (2006)
Han, C. S.; Roters, F.; Raabe, D.: On strain gradients and size-dependent hardening descriptions in crystal plasticity frameworks. Metals and Materials International 12, 5, pp. 407 - 411 (2006)
Dorner, D.; Zaefferer, S.; Lahn, L.; Raabe, D.: Overview of Microstructure and Microtexture Development in Grain-oriented Silicon Steel. Journal of Magnetism and Magnetic Materials 304 (2), pp. 183 - 186 (2006)
Li, F.; Ardehali Barani, A.; Ponge, D.; Raabe, D.: Austenite Grain Coarsening Behavior in a Medium Carbon Si–Cr spring steel with and without Vanadium. Steel Research International 77 (8), pp. 590 - 594 (2006)
Raabe, D.; Jia, J.: Evolution of crystallinity and of crystallographic orientation in isotactic polypropylene during rolling and heat treatment. European Polymer Journal 42 (8), pp. 1755 - 1766 (2006)
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
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.
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.