Davut, K.; Zaefferer, S.: Improving the Reliability of EBSD-based Texture Analysis by a New Large Area Mapping Technique. Materials Science Forum 702-703, pp. 566 - 569 (2012)
Davut, K.; Zaefferer, S.: The effect of size and shape of austenite grains on the mechanical properties of a low-alloyed TRIP steel. Steel Research International 83 (6), pp. 584 - 589 (2012)
Davut, K.; Gür, C. H.: Monitoring the Microstructural Evolution in Spheroidised Steel by Magnetic Barkhausen Noise Measurement. Journal of Nondestructive Evaluation 29, pp. 241 - 247 (2010)
Davut, K.; Zaefferer, S.: Statistical Reliability of Phase Fraction Determination Based on Electron Backscatter Diffraction (EBSD) Investigations on the Example of an Al-TRIP Steel. Metallurgical and Materials Transactions A 41 (9), pp. 2187 - 2196 (2010)
Davut, K.; Zaefferer, S.: The effect of texture on the stability of retained austenite in Al-alloyed TRIP steels of Al-alloyed TRIP Steels. MRS 2010 Fall Meeting, Boston, MA, USA, 2011. (2011)
Davut, K.; Zaefferer, S.: Improving the Reliability of EBSD-based Texture Analysis by a New Large Area Mapping Technique. International Conference on the Textures of Materials, ICOTOM 16, Mumbai, India (2011)
Davut, K.; Zaefferer, S.: Factors influencing the strain-induced transformation of residual austenite in a low-alloyed TRIP steel. Euromat 2011 Conference, Montpellier, France (2011)
Davut, K.; Zaefferer, S.: A new large-area mapping technique to improve the statistical reliability of EBSD datasets. Royal Microscopy Society (RMS) EBSD 2011 Meeting, Düsseldorf, Germany (2011)
Davut, K.; Zaefferer, S.: The effect of texture on the stability of retained austenite in Al-alloyed TRIP steels of Al-alloyed TRIP Steels. MRS 2010 Fall Meeting, Boston, MA, USA (2010)
Davut, K.; Zaefferer, S.: Statistical Reliability of EBSD Data Sets for the Characterization of Al-alloyed TRIP Steels. 15th International Metallurgy and Materials Congress, Istanbul, Turkey (2010)
Davut, K.; Zaefferer, S.: Statistical Reliability of Phase Fraction and Texture Determination Based on EBSD Investigations on the Example of an Al-TRIP steel. Royal Microscopy Society (RMS) EBSD 2010 Meeting, Derby, UK (2010)
Davut, K.; Zaefferer, S.: Phase fraction and texture quantification of Al-TRIP steel from EBSD data. 3rd Int. Conf. On Texture and Anisotropy of Polycrystals (ITAP-3), Göttingen, Germany (2009)
Davut, K.; Gür, C. H.: Monitoring the Microstructural Evolution in Spheroidised Steel by Magnetic Barkhausen Noise Measurement. 7th Int. Conf. on Barkhausen Noise & Micromagnetic Testing, Aachen, Germany (2009)
Davut, K.; Zaefferer, S.: Effect of step size and scanned area on phase fraction and texture quantification from EBSD data. DGM-DVM, EBSD-Workshop 2009, Mikrostrukturuntersuchungen im REM, Chemnitz, Germany (2009)
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…