Zhao, H.; Gault, B.; Ponge, D.; Raabe, D.; De Geuser, F.: Parameter free quantitative analysis of atom probe data by correlation functions: Application to the precipitation in Al–Zn–Mg–Cu. Scripta Materialia 154, pp. 106 - 110 (2018)
Lai, M.; Li, Y.; Lillpopp, L.; Ponge, D.; Will, S.; Raabe, D.: On the origin of the improvement of shape memory effect by precipitating VC in Fe–Mn–Si-based shape memory alloys. Acta Materialia 155, pp. 222 - 235 (2018)
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)
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)
Luo, H.; Li, Z.; Chen, Y.-H.; Ponge, D.; Rohwerder, M.; Raabe, D.: Hydrogen effects on microstructural evolution and passive film characteristics of a duplex stainless steel. Electrochemistry Communucations 79, pp. 28 - 32 (2017)
Tarzimoghadam, Z.; Ponge, D.; Klöwer, J.; Raabe, D.: Hydrogen-assisted failure in Ni-based superalloy 718 studied under in situ hydrogen charging: The role of localized deformation in crack propagation. Acta Materialia 128, pp. 365 - 374 (2017)
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.