Cereceda, D.; Diehl, M.; Roters, F.; Raabe, D.; Perlado, J. M.; Marian, J.: An atomistically-informed crystal plasticity model to predict the temperature dependence of the yield strength of single-crystal tungsten. XXV International Workshop on Computational Micromechanics of Materials, Bochum, Germany (2015)
Roters, F.; Zhang, S.; Shantraj, P.: Including damage modelling into crystal plasticity simulation. XXV International Workshop on Computational Micromechanics of Materials, Bochum, Germany (2015)
Wong, S. L.; Roters, F.: Multiscale micromechanical modelling for advanced high strength steels including both the TRIP and TWIP effect. XXV International Workshop on Computational Micromechanics of Materials, Bochum, Germany (2015)
Diehl, M.; Eisenlohr, P.; Roters, F.; Shanthraj, P.; Reuber, J. C.; Raabe, D.: DAMASK: The Düsseldorf Advanced Material Simulation Kit for studying crystal plasticity using an FE based or a spectral numerical solver. Seminar of the Centro Nacional de Investigaciones Metalúrgicas (CENIM) del CSIC , Madrid, Spain (2015)
Roters, F.: Multi-scale Micromechanics and Damage: From Model Development to Real Systems. IEK-Kolloquium „Simulation von Energiematerialien“
, Jülich, Germany (2015)
Wong, S. L.; Roters, F.: A crystal plasticity model for advanced high strength steels including both TRIP and TWIP effect. 12th International Conference on the Mechanical Behavior of Materials ICM 12
, Karlsruhe, Germany (2015)
Diehl, M.; Shanthraj, P.; Roters, F.; Tasan, C. C.; Raabe, D.: A Virtual Laboratory to Derive Mechanical Properties. M2i Conference "High Tech Materials: your world - our business"
, Sint Michielgestel, The Netherlands (2014)
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
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.
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.