Aota, L. S.; Bajaj, P.; Sandim, H. R. Z.; Jägle, E. A.: Laser Powder-Bed Fusion as an Alloy Development Tool: Parameter Selection for In-Situ Alloying Using Elemental Powders. Materials 13 (18), 3922 (2020)
Haines, M. P.; Peter, N. J.; Babu, S.; Jägle, E. A.: In-situ synthesis of oxides by reactive process atmospheres during L-PBF of stainless steel. Additive Manufacturing 33, 101178 (2020)
Bajaj, P.; Hariharan, A.; Kini, A.; Kürnsteiner, P.; Raabe, D.; Jägle, E. A.: Steels in additive manufacturing: A review of their microstructure and properties. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 772, 138633 (2020)
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.