Brinckmann, S.; Kirchlechner, C.; Dehm, G.; Matoy, K.: Using simulations to investigate the apparent fracture toughness of microcantilevers. Nanomechanical Testing in Materials Research and Development VI, Dubrovnik, Croatia (2017)
Arigela, V. G.; Kirchlechner, C.; Dehm, G.: Setup of a microscale high temperature loading rig for micro-fracture mechanics. Euromat 2017, Thessaloniki, Greece (2017)
Kirchlechner, C.: Insights into dislocation grainboundary interactions by in situ micromechanics. Seminar Lecture at the FAU Erlangen/Nürnberg, Erlangen, Germany (2017)
Kirchlechner, C.: Using nano- and micromechanics to understand interface plasticity. Hysitron Nanobrücken 2017, University of Manchester, Manchester, UK (2017)
Luo, W.; Kirchlechner, C.; Dehm, G.; Stein, F.: Fracture Toughness of Hexagonal and Cubic NbCo2 Laves Phases. Nanobrücken 2017, European Nanomechanical Testing Conference, University of Manchester, Manchester, UK (2017)
Dehm, G.; Malyar, N.; Kirchlechner, C.: Towards probing the barrier strength of grain boundaries for dislocation transmission. Electronic Materials and Applications 2017, Orlando, FL, USA (2017)
Dehm, G.; Malyar, N.; Kirchlechner, C.: Do we understand dislocation transmission through grain boundaries? PICS meeting, Luminy, Marseille, France (2017)
Jaya, B. N.; Kirchlechner, C.; Dehm, G.: Fracture Behavior of Nanostructured Heavily Cold Drawn Pearlite: Influence of the Interface. TMS 2017, San Diego, CA, USA (2017)
Kirchlechner, C.: What can you learn from a µLaue experiment? 8th International Conference on Multiscale Materials Modeling - MMM 2016, Dijon, France (2016)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.