Jägle, E. A.: Atom Probe Tomography: Basics, data analysis and application to the analysis of phase transformations. Department of Materials Engineering house seminar, KU Leuven, Leuven, Belgium (2014)
Jägle, E.: Parameter finding for and accuracy of the Maximum Separation algorithm assessed by Atom Probe simulations. 2nd European APT Workshop at ETH Zürich, Zürich, Switzerland (2013)
Jägle, E.: Atom Probe Tomography: Basics, data analysis and application to the analysis of advanced steels. Symposium "Frontiers in Steelmaking and Steel Design", INM, Saarbrücken, Germany (2013)
Jägle, E.: Atom Probe Tomography: Basics, data analysis and application to the analysis of phase transformations. Kolloquium at Max-Planck-Institute for Intelligent Systems, Stuttgart, Germany (2013)
Hariharan, A.; Lu, L.; Risse, J.; Jägle, E. A.; Raabe, D.: Mechanisms Contributing to Solidification Cracking during laser powder bed fusion of Inconel-738LC. Alloys for Additive Manufacturing Symposium 2019 (AAMS2019), Chalmers University of Technology, Gothenburg, Sweden (2019)
Bajaj, P.; Gupta, A.; Jägle, E. A.; Raabe, D.: Precipitation kinetics during non-linear heat treatment in Laser Additive Manufacturing. International Conference on Advanced Materials and Processes, ‘ADMAT 2017’ SkyMat, Thiruvananthapuram, India (2017)
Jägle, E. A.: Microstructural Aspects of Additive Manufacturing. Lecture: Workshop “Microstructural Aspects of Additive Manufacturing”, Indian Institute of Technology Roorkee, 3,5h of lectures, Roorkee, India, December 02, 2017
Ackers, M.: Recommissioning of a metal powder atomisation system and investigation of its suitability to produce powders for additive Manufacturing processes. Master, Ruhr-Universität Bochum, Bochum, Germany (2017)
Qin, Y.: Effect of post-heat treatment on the microstructure and mechanical properties of SLM-produced IN738LC. Master, RWTH Aachen, Aachen, 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
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
In this project we developed a phase-field model capable of describing multi-component and multi-sublattice ordered phases, by directly incorporating the compound energy CALPHAD formalism based on chemical potentials. We investigated the complex compositional pathway for the formation of the η-phase in Al-Zn-Mg-Cu alloys during commercial…
The project HyWay aims to promote the design of advanced materials that maintain outstanding mechanical properties while mitigating the impact of hydrogen by developing flexible, efficient tools for multiscale material modelling and characterization. These efficient material assessment suites integrate data-driven approaches, advanced…
A novel design with independent tip and sample heating is developed to characterize materials at high temperatures. This design is realized by modifying a displacement controlled room temperature micro straining rig with addition of two miniature hot stages.
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.
While Density Functional Theory (DFT) is in principle exact, the exchange functional remains unknown, which limits the accuracy of DFT simulation. Still, in addition to the accuracy of the exchange functional, the quality of material properties calculated with DFT is also restricted by the choice of finite bases sets.