Nellessen, J.; Sandlöbes, S.; Raabe, D.: Low cycle fatigue in aluminum single and bi-crystals: On the influence of crystal orientation. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 668, pp. 166 - 179 (2016)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Effects of strain amplitude, cycle number and orientation on low cycle fatigue microstructures in austenitic stainless steel studied by electron channelling contrast imaging. Acta Materialia 87, pp. 86 - 99 (2015)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Effects of strain amplitude, cycle number and orientation on low cycle fatigue microstructures in fcc materials studied by Electron Channeling Contrast Imaging. TMS 2015 - 144th Annual Meeting & Exhibition, Orlando, FL, USA (2015)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Systematic Investigation of the Influence of Strain Amplitude, Orientation and Cycle Number on the Dislocation Structures Formed during Low Cycle Fatigue. MSE 2014, Darmstadt, Germany (2014)
Nellessen, J.; Sandlöbes, S.; Raabe, D.: Systematic and efficient investigation of the influences on the dislocation structures formed during low cycle fatigue in austenitic stainless steel. Euromat 2013, Sevilla, Spain (2013)
Nellessen, J.: Effects of strain amplitude, cycle number and orientation on low cycle fatigue microstructures in austenitic stainless steel and aluminum. Dissertation, RWTH Aachen, Aachen, Germany (2015)
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…