Tasan, C. C.; Diehl, M.; Yan, D.; Raabe, D.: Coupled high-resolution experiments and crystal plasticity simulations to analyze stress and strain partitioning in multi-phase alloys. TMS2015, Orlando, FL, USA (2015)
Tasan, C. C.; Yan, D.; Raabe, D.: A novel, high-resolution approach for concurrent mapping of micro-strain and micro-structure evolution up to damage nucleation. TMS 2015, Orlando, FL, USA (2015)
Roters, F.; Diehl, M.; Shanthraj, P.; Zambaldi, C.; Tasan, C. C.; Yan, D.; Raabe, D.: Simulation analysis of stress and strain partitioning in dual phase steel based on real microstructures. MMM2014, 7th International Conference on Multiscale
Materials Modeling
, Berkeley, CA, USA (2014)
Tasan, C. C.; Diehl, M.; Yan, D.; Zambaldi, C.; Shanthraj, P.; Roters, F.; Raabe, D.: Integrated experimental and simulation analysis of stress and strain partitioning in dual phase steel. IUTAM Symposium on Connecting Multiscale Mechanics to Complex Material Design, Evanston, IL, USA (2014)
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
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.