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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.