Jentner, R.; Scholl, S.; Srivastava, K.; Best, J. P.; Kirchlechner, C.; Dehm, G.: Local strength of bainitic and ferritic HSLA steel constituents understood using correlative electron microscopy and microcompression testing. Materials and Design 236, 112507 (2023)
Jentner, R.; Tsai, S.-P.; Welle, A.; Scholl, S.; Srivastava, K.; Best, J. P.; Kirchlechner, C.; Dehm, G.: Automated classification of granular bainite and polygonal ferrite by electron backscatter diffraction verified through local structural and mechanical analyses. Journal of Materials Research 38 (18), pp. 4177 - 4191 (2023)
Li, J.; Pharr, G. M.; Kirchlechner, C.: Quantitative insights into the dislocation source behavior of twin boundaries suggest a new dislocation source mechanism. Journal of Materials Research 36 (10), pp. 2037 - 2046 (2021)
Tian, C.; Dehm, G.; Kirchlechner, C.: Influence of strain rate on the activation of {110}, {112}, {123} slip in ferrite of DP800. Materialia 15, 100983 (2021)
Tian, C.; Kirchlechner, C.: The fracture toughness of martensite islands in dual-phase DP800 steel. Journal of Materials Research 36, pp. 2495 - 2504 (2021)
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
About 90% of all mechanical service failures are caused by fatigue. Avoiding fatigue failure requires addressing the wide knowledge gap regarding the micromechanical processes governing damage under cyclic loading, which may be fundamentally different from that under static loading. This is particularly true for deformation-induced martensitic…