Bambach, M.; Heppner, S.; Steinmetz, D.; Roters, F.: Assessing and ensuring parameter identifiability for a physically-based strain hardening model for twinning-induced plasticity. Mechanics of Materials 84, pp. 127 - 139 (2015)
Roters, F.; Steinmetz, D.; Wong, S. L.; Raabe, D.: Crystal Plasticity Implementation of an Advanced Constitutive Model Including Twinning for High Manganese Steels. MSE 2014
, Darmstadt, Germany (2014)
Roters, F.; Steinmetz, D.; Wong, S. L.; Raabe, D.: Crystal Plasticity Implementation of an Advanced Constitutive Model Including Twinning for High Manganese Steels. 2nd International Conference High Manganese Steel, HMnS 2014
, Aachen, Germany (2014)
Steinmetz, D.; Roters, F.; Eisenlohr, P.; Raabe, D.: A dislocation density-based constitutive model for TWIP steels. 1st International Conference on High Manganese Steels, Seoul, South Korea (2011)
Steinmetz, D.; Zaefferer, S.: Currents state of the art in EBSD: Possibilities and limitations. Seminar Talk at Ludwig-Maximilians-Universität, München, Germany (2011)
Steinmetz, D.; Zaefferer, S.: Improving the physical resolution of electron backscatter diffraction by decreasing accelerating voltage. EBSD 2010 Meeting, Rolls-Royce Leisure Association, Derby, UK (2010)
Steinmetz, D.; Zaefferer, S.: Quantitative determination of twin volume fraction in TWIP steels by high resolution EBSD. Materials Science and Technology (MS&T) 2010, Pittsburgh, PA, USA (2009)
Steinmetz, D.; Zaefferer, S.: Challenges of low-accelerating voltage electron backscatter diffraction. 3rd International Conference on Texture and Anisotropy of Polycrystals (ITAP-3), Göttingen, Germany (2009)
Steinmetz, D.; Zaefferer, S.: Towards ultrahigh resolution EBSD by use of low accelerating voltage. EBSD 2009 Meeting, University of Swansea, Wales, UK (2009)
Steinmetz, D.: A constitutive model of twin nucleation and deformation twinning in High-Manganese Austenitic TWIP steels. Dissertation, RWTH Aachen, Aachen, Germany (2013)
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…