Sun, D. K.; Zhu, M. F.; Pan, S. Y.; Raabe, D.: Numerical Modeling of dendritic growth in alloy solidification with forced convection. International Journal of Modern Physics B 23, pp. 1609 - 1614 (2009)
Verbeken, K.; Barbé, L.; Raabe, D.: Evaluation of the Crystallographic Orientation Relationships between FCC and BCC phases in TRIP Steels. ISIJ International 49 (10), pp. 1601 - 1609 (2009)
Verbeken, K.; van Caenegem, N.; Raabe, D.: Identification of ε-martensite in Fe-based shape memory alloy by means of EBSD. Micron 40, 1, pp. 151 - 156 (2009)
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)
Ma, D.; Friák, M.; Neugebauer, J.; Raabe, D.; Roters, F.: Multiscale simulation of polycrystal mechanics of textured β-Ti alloys using ab initio and crystal-based finite element methods. Physica Status Solidi B 245 (12), pp. 2642 - 2648 (2008)
Friák, M.; Counts, W. A.; Raabe, D.; Neugebauer, J.: Error-propagation in multiscale approaches to the elasticity of polycrystals. Physica Status Solidi (B) 245, pp. 2636 - 2641 (2008)
Al-Sawalmih, A.; Li, C.; Siegel, S.; Fabritius, H.; Yi, S. B.; Raabe, D.; Fratzl, P.; Paris, O.: Microtexture and Chitin/Calcite Orientation Relationship in the Mineralized Exoskeleton of the American Lobster. Advanced Functional Materials 18 (20), pp. 3307 - 3314 (2008)
Nikolov, S.; Raabe, D.: Hierarchical Modeling of the Elastistic Properties of Bone at Submicron Scales: The Role of Extrafibrillar Mineralization. Biophysical Journal 94, pp. 4220 - 4232 (2008)
Bastos, A.; Zaefferer, S.; Raabe, D.: Three-dimensional EBSD study on the relationship between triple junctions and columnar grains in electrodeposited Co–Ni films. Journal of Microscopy 230, pp. 487 - 498 (2008)
Cao, Y. P.; Xue, Z. Y.; Chen, X.; Raabe, D.: Correlation between the flow stress and the nominal indentation hardness of soft metals. Scripta Materialia 59, pp. 518 - 521 (2008)
Counts, W. A.; Friak, M.; Battaile, C. C.; Raabe, D.; Neugebauer, J.: A comparison of polycrystalline elastic constants computed by analytic homogenization schemes and FEM. Physica Status Solidi B 245, pp. 2630 - 2635 (2008)
Frommert, M.; Zobrist, C.; Lahn, L.; Böttcher, A.; Raabe, D.; Zaefferer, S.: Texture measurement of grain-oriented electrical steels after secondary recrystallization. Journal of Magnetism and Magnetic Materials 320, pp. e657 - e660 (2008)
Godara, A.; Raabe, D.: Microstrain localisation measurement in epoxy FRCs during plastic deformation using a digital image correlation technique coupled with scanning electron microscopy. Nondestructive Testing and Evaluation 3, pp. 229 - 240 (2008)
Herrera, C.; Ponge, D.; Raabe, D.: Characterization of the microstrcture, crystallographic texture and segregation of an as-cast duplex stainless steel slab. Steel Research International 79 (6), pp. 482 - 488 (2008)
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)
Kumar, D.; Bieler, T. R.; Eisenlohr, P.; Mason, D. E.; Crimp, M. A.; Roters, F.; Raabe, D.: On Predicting Nucleation of Microcracks Due to Slip-Twin Interactions at Grain Boundaries in Duplex gamma-TiAl. Journal of Engineering and Materials Technology 130 (02), pp. 021012-1 - 021012-12 (2008)
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
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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.
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…