Raabe, D.: Compositional Lattice Defect Manipulation for Microstructure Design. The Bauerman Lecture 2019, Department of Materials, Imperial College London, Royal School of Mines, London, UK (2019)
Sedighiani, K.; Diehl, M.; Roters, F.; Sietsma, J.; Raabe, D.: Obtaining constitutive parameters for a physics-based crystal plasticity model from macro-scale behavior. International Conference on Plasticity, Damage, and Fracture , Panama City, Panama (2019)
Li, Z.; Su, J.; Lu, W.; Wang, Z.; Raabe, D.: Metastable high-entropy alloys: design, structure and properties. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
Seol, J. B.; Ko, W.-S.; Bae, J. W.; Jo, Y. H.; Li, Z.; Choi, P.-P.; Raabe, D.; Kim, H. S.: Transition in boron boundary cohesion from effectiveness to harmfulness with respect to application temperatures: high-entropy alloys and Ni-based superalloys. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
Lu, W.; Li, Z.; Liebscher, C.; Dehm, G.; Raabe, D.: TEM/STEM Investigations of the TRIP Effect in a Dual-Phase High-Entropy Alloy. MRS Fall Meeting, Boston, MA, USA (2018)
Su, J.; Li, Z.; Raabe, D.: Microstructural Design to Improve the Mechanical Properties of an Interstitial TRIP-TWIP High-Entropy Alloy. MRS Fall Meeting , Boston, MA, USA (2018)
Sun, B.; Ponge, D.; Fazeli, F.; Scott, C.; Yue, S.; Raabe, D.: Revealing fracture mechanisms of medium manganese steels with and without delta-ferrite. 6th International Conference on Advanced Steels (ICAS 2018), Jeju, South Korea (2018)
Diehl, M.; Kühbach, M.; Raabe, D.: Experimental–computational analysis of primary static recrystallizazion in DC04 steel. 9th International Conference on Multiscale Materials Modeling , Osaka, Japan (2018)
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…