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)
Liu, T.; Raabe, D.; Zaefferer, S.: A 3D tomographic EBSD analysis of a CVD diamond thin film. Science and Technology of Advanced Materials 9, 035013 (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
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…