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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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…