Kwiatkowski da Silva, A.; Inden, G.; Kumar, A.; Ponge, D.; Gault, B.; Raabe, D.: Competition between formation of carbides and reversed austenite during tempering of a medium-manganese steel studied by thermodynamic-kinetic simulations and atom probe tomography. Acta Materialia 147, pp. 165 - 175 (2018)
Wang, M.; Li, Z.; Raabe, D.: In-situ SEM observation of phase transformation and twinning mechanisms in an interstitial high-entropy alloy. Acta Materialia 147, pp. 236 - 246 (2018)
Breitbarth, E.; Zaefferer, S.; Archie, F. M. F.; Besel, M.; Raabe, D.; Requena, G.: Evolution of dislocation patterns inside the plastic zone introduced by fatigue in an aged aluminium alloy AA2024-T3. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 718, pp. 345 - 349 (2018)
Svendsen, B.; Shanthraj, P.; Raabe, D.: Finite-deformation phase-field chemomechanics for multiphase, multicomponent solids. Journal of the Mechanics and Physics of Solids 112, pp. 619 - 636 (2018)
Dutta, A.; Ponge, D.; Sandlöbes, S.; Raabe, D.: Understanding hot vs. Cold rolled medium manganese steel deformation behavior using in situ microscopic digital image correlation. Materials Science Forum 941, pp. 198 - 205 (2018)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.