Park, S. J.; Han, H. N.; Oh, K. H.; Raabe, D.; Kim, J. K.: Finite element simulation of grain interaction and orientation fragmentation during plastic deformation of BCC metals. Proc. ICOTOM 13, pp. 371 - 376 (2002)
Raabe, D.: Cellular automata in materials science with particular reference to recrystallization simulation. Annual Review of Materials Research 32, pp. 53 - 76 (2002)
Raabe, D.; Roters, F.; Zhao, Z.: Texture component crystal plasticity finite element method for physically-based metal forming simulations including texture update. Proc. 8th Int. Conf. on Aluminium Alloys, pp. 31 - 36 (2002)
Raabe, D.; Zhao, Z.; Mao, W.: On the dependence of in-grain subdivision and deformation texture of aluminium on grain interaction. Acta Materialia 50, pp. 4379 - 4394 (2002)
Sachtleber, M.; Zhao, Z.; Raabe, D.: Experimental investigation of plastic grain interaction. Materials Science and Engineering A 336, pp. 81 - 87 (2002)
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)
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
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…
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…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.