Jägle, E. A.: Impact of the process gas atmosphere in Laser Additive Manufacturing – desired and undesired effects. Alloys for Additive Manufacturing Symposium 2018, Sheffield, UK (2018)
Kürnsteiner, P.; Wilms, M. B.; Weisheit, A.; Jägle, E. A.; Raabe, D.: Preventing the Coarsening of Al3Sc Precipitates by the Formation of a Zr-rich Shell During Laser Metal Deposition. TMS2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Jägle, E. A.: Ex-situ and in-situ heat treatment of alloys during Laser Additive Manufacturing. AWT Kolloquium, Institut für Werkstofftechnik, Bremen, Germany (2017)
Jägle, E. A.: Additive Manufacturing and 3D Printing - What’s beyond the hype? Institute Lecture at Indian Institute of Technology Roorkee, Roorkee, India (2017)
Jägle, E. A.: Alloys for Additive Manufacturing, Alloys by Additive Manufacturing. Plenary presentation, Advances in Materials & Processing: Challenges and Opportunities, Indian Institute of Technology Roorkee, Roorkee, India (2017)
Jägle, E. A.: Exploiting the Intrinsic Heat Treatment during Laser Additive Manufacturing to trigger Precipitation Reactions. International Mechanical Engineering Congress & Exposition (IMECE), Tampa, FL, USA (2017)
Kürnsteiner, P.; Wilms, M. B.; Weisheit, A.; Jägle, E. A.; Raabe, D.: In-process precipitation strengthening in Al–Sc during Laser Metal Deposition by exploiting the Intrinsic Heat Treatment. Alloys for Additive Manufacturing Symposium, Zürich, Switzerland (2017)
Jägle, E. A.: Alloys for Additive Manufacturing, Alloys by Additive Manufacturing. Seminar talk at Culham Center for Fusion Energy, Oxford, Oxford, UK (2017)
Jägle, E. A.: Alloys for Additive Manufacturing, Alloys by Additive Manufacturing. Laser-Kolloquium at Fraunhofer Institut für Lasertechnik, Aachen, Aachen, Germany (2017)
Jägle, E. A.: Alloys for Additive Manufacturing, Alloys by Additive Manufacturing. Seminar talk at Institut für Umformtechnik und Leichtbau, TU Dortmund, Dortmund, Germany (2017)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
The prediction of materials properties with ab initio based methods is a highly successful strategy in materials science. While the working horse density functional theory (DFT) was originally designed to describe the performance of materials in the ground state, the extension of these methods to finite temperatures has seen remarkable…
This work led so far to several high impact publications: for the first time nanobeam diffraction (NBD) orientation mapping was used on atom probe tips, thereby enabling the high throughput characterization of grain boundary segregation as well as the crystallographic identification of phases.
Smaller is stronger” is well known in micromechanics, but the properties far from the quasi-static regime and the nominal temperatures remain unexplored. This research will bridge this gap on how materials behave under the extreme conditions of strain rate and temperature, to enhance fundamental understanding of their deformation mechanisms. The…
In 2020, an interdepartmental software task force (STF) was formed to serve as a forum for discussion on topics related to software development and digital workflows at the MPIE. A central goal was to facilitate interdepartmental collaboration by co-developing and integrating workflows, aligning internally developed software, and rolling out…
The aim of the work is to develop instrumentation, methodology and protocols to extract the dynamic strength and hardness of micro-/nano- scale materials at high strain rates using an in situ nanomechanical tester capable of indentation up to constant strain rates of up to 100000 s−1.
The balance between different contributions to the high-temperature heat capacity of materials can hardly be assessed experimentally. In this study, we develop computationally highly efficient ab initio methods which allow us to gain insight into the relevant physical mechanisms. Some of the results have lead to breakdown of the common…