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
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
Decarbonisation of the steel production to a hydrogen-based metallurgy is one of the key steps towards a sustainable economy. While still at the beginning of this transformation process, with multiple possible processing routes on different technological readiness, we conduct research into the related fundamental scientific questions at the MPIE.
In this project, we aim at significantly enhancing the strength-ductility combination of quinary high-entropy alloys (HEAs) with five principal elements by simultaneously introducing interstitial C/N and the transformation induced plasticity (TRIP) effect. Thus, a new class of alloys, namely, interstitially alloyed TRIP-assisted quinary (five-component) HEAs is being developed.
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.