Sedighiani, K.; Traka, K.; Diehl, M.; Roters, F.; Sietsma, J.; Raabe, D.: Determination and validation of BCC crystal plasticity parameters for a wide range of temperatures and strain rates. 7th Conference on Recrystallization and Grain Growth, REX 2019, Ghent, Belgium (2019)
Diehl, M.; Roters, F.; Raabe, D.: Coupled Experimental-Computational Investigations of Grain Scale Mechanics in Complex Metallic Microstructures. 15th U.S. National Congress on Computational Mechanics, Ausrin, TX, USA (2019)
Han, F.; Diehl, M.; Roters, F.; Raabe, D.: Multi-scale modeling of plasticity. ICIAM 2019 - The 9th International Congress on Industrial and Applied Mathematics, Valencia, Spain (2019)
Liu, C.; Shanthraj, P.; Roters, F.; Raabe, D.: Phase-field/CALPHAD methods for multi-phase and multi-component microstructures. The 4th International Symposium on Phase Field Modelling in Materials Science (PF 19), Bochum, Germany (2019)
Raabe, D.: Metastable Nanostructured Metallic Alloy. The KAIST Lecture in Materials Science and Engineering 2019, Korea Advanced Institute of Science and Technology KAIST, Daejeon, Korea (2019)
Raabe, D.: Atomic-Scale Analysis of Chemistry at Lattice Defects. The KAIST Lecture in Materials Science and Engineering 2019, Korea Advanced Institute of Science and Technology KAIST, Daejeon, Korea (2019)
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.