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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.