Tasan, C. C.; Zaefferer, S.; Raabe, D.: Deformation induced dislocation interactions near martensite-ferrite phase boundaries. MRS Fall Meeting 2011, San Francisco, CA, USA (2011)
Tasan, C. C.: Micro-mechanical characterization and quantification of ductile damage. Seminar talk at Institut für Umformtechnik und Leichtbau, Dortmund, Germany (2010)
Zhang, J.; Raabe, D.; Lai, M.; Yan, D.; Tasan, C. C.: Site-preferential recrystallization and nano-precipitation to achieve improved mechanical properties. MRS Fall Meeting 2016, Boston, MA, USA (2016)
Diehl, M.; Yan, D.; Tasan, C. C.; Shanthraj, P.; Roters, F.; Raabe, D.: Stress and Strain Partitioning in Multiphase Alloys: An Integrated Experimental-Numerical Analysis. Winter School 2014, Research Training Group 1483,
Karlsruher Intitut f. Technologie (KIT), Karlsruhe, Germany (2014)
Lai, M.; Tasan, C. C.; Zhang, J.; Grabowski, B.; Huang, L.; Springer, H.; Raabe, D.: ω phase accommodated nano-twinning mechanism in Gum Metal: An ab initio study. 3rd International Workshop on Physics Based Material Models and Experimental Observations: Plasticity and Creep, Cesme/Izmir, Turkey (2014)
Yan, D.; Tasan, C. C.; Raabe, D.: Graded, ultrafine-grained, ferrite/martensite dual phase steel: a case study for damage-resistant microstructure design. Physics based materials models and experimental observations, Cesme Turkey (2014)
Diehl, M.; Yan, D.; Tasan, C. C.; Shanthraj, P.; Roters, F.; Raabe, D.: Stress and Strain Partitioning in Multiphase Alloys: An Integrated Experimental-Numerical Analysis. Materials to Innovate Industry and Society, Noordwijkerhout, The Netherlands (2013)
Wang, M.; Tasan, C. C.; Ponge, D.; Kostka, A.; Raabe, D.: Size effects on mechanical stability of metastable austenite. GDRi CNRS MECANO General Meeting on the Mechanics of Nano-Objects, MPIE, Düsseldorf, Germany (2013)
Jeannin, O.; Tasan, C. C.; Raabe, D.: Micro-testing of isolated single/bi-crystals of complex alloys with ECCI & δ-EBSD imaging. 4th International Workshop on Remote Electron Microscopy and In Situ Studies, Lisbon, Portugal (2013)
Yan, D.; Tasan, C. C.; Ponge, D.; Diehl, M.; Roters, F.; Hartmaier, A.; Raabe, D.: Experimental-Numerical Analysis of Stress and Strain Partitioning in Dual Phase Steel. 10th Materials Day, Joint workshop of the Materials Research Department (MRD) and the IMPRS-SurMat, Bochum, Germany (2012)
Scharifi, E.; Tasan, C. C.; Hoefnagels, J. P. M.; Raabe, D.: Microstructural analysis of strain rate sensitivity of dual-phase steel. Materials Science Engineering (MSE) 2012, Dramstadt, Germany (2012)
Diehl, M.; Eisenlohr, P.; Roters, F.; Tasan, C. C.; Raabe, D.: Using a "Virtual Laboratory" to Derive Mechanical Properties of Complex Microstructures. Materials to Innovate Industry and Society, Noordwijkerhout, The Netherlands (2011)
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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.