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)
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
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.