Patil, P.; Lee, S.; Dehm, G.; Brinckmann, S.: Influence of crystal orientation on twinning in austenitic stainless-steel during single micro-asperity tribology and nanoindentation. WEAR 504-505, 204403 (2022)
Tsybenko, H.; Farzam, F.; Dehm, G.; Brinckmann, S.: Scratch hardness at a small scale: Experimental methods and correlation to nanoindentation hardness. Tribology International 163, 107168 (2021)
Duarte, M. J.; Fang, X.; Rao, J.; Krieger, W.; Brinckmann, S.; Dehm, G.: In situ nanoindentation during electrochemical hydrogen charging: a comparison between front-side and a novel back-side charging approach. Journal of Materials Science 56 (14), pp. 8732 - 8744 (2021)
Ebner, A. S.; Brinckmann, S.; Plesiutschnig, E.; Clemens, H.; Pippan, R.; Maier-Kiener, V.: A Modified Electrochemical Nanoindentation Setup for Probing Hydrogen-Material Interaction Demonstrated on a Nickel-Based Alloy. JOM-Journal of the Minerals Metals & Materials Society 72 (5), pp. 2020 - 2029 (2020)
Brinckmann, S.: A framework for material calibration and deformation predictions applied to additive manufacturing of metals. International Journal of Fracture 218, pp. 85 - 95 (2019)
Brinckmann, S.: The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal. International Journal of Fracture 218 (1-2), pp. 5 - 61 (2019)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…