Raabe, D.: Don’t trust your simulation - Computational materials science on its way to maturity? Advanced Engineering Materials 4 (5), pp. 255 - 267 (2002)
Raabe, D.; Zhao, Z.; Park, S. J.; Roters, F.: Theory of orientation gradients in plastically strained crystals. Acta Materialia 50 (2), pp. 421 - 440 (2002)
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)
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
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
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.