Roters, F.; Diehl, M.; Shanthraj, P.: On the importance of using 3D microstructures in Crystal Plasticity Simulations. Symposium: 3D materials characterization at all length scales and its applications to iron and steel, Düsseldorf, Germany (2017)
Roters, F.; Kok, P.: An integrated approach on microstructure, damage and texture modelling of modern steels. 5th International Conference on Steels in Cars and Trucks, SCT 2017
, Amsterdam, The Netherlands (2017)
Liu, C.; Diehl, M.; Shanthraj, P.; Roters, F.; Raabe, D.; Sandlöbes, S.; Dong, J.: An integrated crystal plasticity-phase field approach to locally predict twin formation in magnesium. DGM Meeting, "Herausforderungen bei der skalenübergreifenden Modellierung von Werkstoffen ", Regensburg, Germany (2017)
Roters, F.; Wong, S. L.; Shanthraj, P.; Diehl, M.; Raabe, D.: Thermo mechanically coupled simulation of high manganese TRIP/TWIP Steel. 5th International Conference on Material Modeling, ICMM 5, Rome, Italy (2017)
Roters, F.; Bambach, M.; Wong, S. L.: Development of dislocation density based constitutive models ? the parameter dilemma. GAMM 2017, 88th Annual Meeting of the International Association of Applied Mathematics and Mechanics
, Weimar, Germany (2017)
Diehl, M.; Cereceda, D.; Wong, S. L.; Reuber, J. C.; Roters, F.; Raabe, D.: From Phenomenological Descriptions to Physics-based Constitutive Models EPSRC Workshop on Multiscale Mechanics of Deformation and Failure in Materials. EPSRC Workshop on Multiscale Mechanics of Deformation and Failure in Materials
, Aberdeen, Scotland (2016)
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.