Zambaldi, C.: Primary Recrystallization of a Single-Crystal Ni-Base Superalloy - Experiments and Meso-Scale Modeling. GLADD-Meeting, TU Delft, Delft, The Netherlands (2006)
Zambaldi, C.: Modellierung und experimentelle Untersuchung des Rekristallisationsverhaltens der Nickelbasis-Superlegierung CMSX-4. Kolloquium, University of Bayreuth (2005)
Zambaldi, C.; Zaefferer, S.; Wright, S. I.: Determination of texture and microstructure of ordering domains in gamma-TiAl. Electron Backscatter Diffraction Meeting by the Royal Microscopical Society, University of Sheffield, Sheffield, UK (2008)
Zambaldi, C.; Roters, F.; Bieler, T. R.; Raabe, D.: Micro-mechanical modeling of near-gamma Ti-Al-Nb. 11th World Conference on Titanium (JIMIC5) Ti-2007, Kyoto, Japan (2007)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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…