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)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
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