Cao, Y. P.; Ma, D.; Raabe, D.: The use of flat punch indentation to determine the viscoelastic properties in the time and frequency domains of a soft layer bonded to a rigid substrate. Acta Biomaterialia 5 (1), pp. 240 - 248 (2009)
Cao, Y. P.; Xue, Z. Y.; Chen, X.; Raabe, D.: Correlation between the flow stress and the nominal indentation hardness of soft metals. Scripta Materialia 59, pp. 518 - 521 (2008)
Cao, Y. P.: Determination of the creep exponent of a power-law creep solid using indentation tests. Mechanics of Time-Dependent Materials 11, pp. 159 - 173 (2007)
Balasundaram, K.; Cao, Y. P.; Raabe, D.: Investigating the Applicability of the Oliver & Pharr Method to the Nano-Mechanical Characterization of Soft Matter. Gerberich Symposium, 1st International Conference from Nanoparticles and Nanomaterials to Nanodevices and Nanosystems, Halkidiki, Greece (2008)
Balasundaram, K.; Cao, Y. P.; Raabe, D.: Nanomechanics characterization of softmatter using nanoindentation. 11th GLADD Meeting, TU Gent, Belgium (2008)
Balasundaram, K.; Cao, Y. P.; Raabe, D.: Nano-mechanical Characterization of Soft Matter. Materials science Day, Mechanical Engineering Department at Ruhr-University of Bochum, Bochum, Germany (2008)
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.
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.