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
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.
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…
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…