Juntunen, P.; Raabe, D.; Karjalainen, P.; Kopio, T.; Bolle, G.: Optimizing continuous annealing of IF steels for improving their deep drawability. Metallurgical and Materials Transactions A 32, pp. 1989 - 1995 (2001)
Roters, F.; Raabe, D.; Gottstein, G.: Work hardening in heterogeneous alloys - A microstructural approach based on three internal state variables. Acta Materialia 48 (17), pp. 4181 - 4189 (2000)
Raabe, D.; Becker, R. C.: Coupling of a crystal plasticity finite element model with a probabilistic cellular automaton for simulating primary static recrystallization in aluminum. Modelling and Simulation in Materials Science and Engineering 8, pp. 445 - 462 (2000)
Raabe, D.; Miyake, K.; Takahara, H.: Processing, microstructure, and properties of ternary high-strength Cu–Cr–Ag in situ composites. Material Science and Engineering A 291, pp. 186 - 197 (2000)
Raabe, D.; Mattissen, D.: Experimental investigation and Ginzburg-Landau modeling of the microstructure dependence of superconductivity in Cu–Ag–Nb wires. Acta Materialia 47 (3), pp. 769 - 777 (1999)
Mattissen, D.; Raabe, D.; Heringhaus, F.: Experimental investigation and modeling of the influence of microstructure on the resistive conductivity of a Cu–Ag–Nb in situ composite. Acta Materialia 47, pp. 1627 - 1634 (1999)
Marx, V.; Raabe, D.; Engler, O.; Gottstein, G.: Simulation of the texture evolution during annealing of cold rolled BCC and FCC matals using a cellular automation approach. Textures and Microstructures 28, pp. 211 - 218 (1997)
Raabe, D.: Texture simulation for hot rolling of aluminium by use of a Taylor model considering grain interactions. Acta Metallurgica et Materialia 43 (3), pp. 1023 - 1028 (1995)
Roters, F.; Eisenlohr, P.; Bieler, T. R.; Raabe, D.: Crystal Plasticity Finite Element Methods in Materials Science and Engineering. Wiley-VCH, Weinheim (2010), 197 pp.
Janssens, K. G. F.; Raabe, D.; Kozeschnik, E.; Miodownik, M. A.; Nestler, B.: Computational Materials Engineering – An Introduction to Microstructure Evolution. Academic Press, Elsevier, USA (2007), 360 pp.
Shanthraj, P.; Diehl, M.; Eisenlohr, P.; Roters, F.; Raabe, D.: Spectral Solvers for Crystal Plasticity and Multi-physics Simulations. In: Handbook of Mechanics of Materials, pp. 1347 - 1372 (Eds. Hsueh, C.-H.; Schmauder, S.; Chen, C.-S.; Chawla, K. K.; Chawla, N. et al.). Springer, Singapore (2019)
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
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
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…
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…
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.