Bambach, M.; Heppner, S.; Steinmetz, D.; Roters, F.: Assessing and ensuring parameter identifiability for a physically-based strain hardening model for twinning-induced plasticity. Mechanics of Materials 84, pp. 127 - 139 (2015)
Roters, F.; Steinmetz, D.; Wong, S. L.; Raabe, D.: Crystal Plasticity Implementation of an Advanced Constitutive Model Including Twinning for High Manganese Steels. MSE 2014
, Darmstadt, Germany (2014)
Roters, F.; Steinmetz, D.; Wong, S. L.; Raabe, D.: Crystal Plasticity Implementation of an Advanced Constitutive Model Including Twinning for High Manganese Steels. 2nd International Conference High Manganese Steel, HMnS 2014
, Aachen, Germany (2014)
Steinmetz, D.; Roters, F.; Eisenlohr, P.; Raabe, D.: A dislocation density-based constitutive model for TWIP steels. 1st International Conference on High Manganese Steels, Seoul, South Korea (2011)
Steinmetz, D.; Zaefferer, S.: Currents state of the art in EBSD: Possibilities and limitations. Seminar Talk at Ludwig-Maximilians-Universität, München, Germany (2011)
Steinmetz, D.; Zaefferer, S.: Improving the physical resolution of electron backscatter diffraction by decreasing accelerating voltage. EBSD 2010 Meeting, Rolls-Royce Leisure Association, Derby, UK (2010)
Steinmetz, D.; Zaefferer, S.: Quantitative determination of twin volume fraction in TWIP steels by high resolution EBSD. Materials Science and Technology (MS&T) 2010, Pittsburgh, PA, USA (2009)
Steinmetz, D.; Zaefferer, S.: Challenges of low-accelerating voltage electron backscatter diffraction. 3rd International Conference on Texture and Anisotropy of Polycrystals (ITAP-3), Göttingen, Germany (2009)
Steinmetz, D.; Zaefferer, S.: Towards ultrahigh resolution EBSD by use of low accelerating voltage. EBSD 2009 Meeting, University of Swansea, Wales, UK (2009)
Steinmetz, D.: A constitutive model of twin nucleation and deformation twinning in High-Manganese Austenitic TWIP steels. Dissertation, RWTH Aachen, Aachen, Germany (2013)
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.