Fujita, N.; Ishikawa, N.; Roters, F.; Tasan, C. C.; Raabe, D.: Experimental–numerical study on strain and stress partitioning in bainitic steels with martensite–austenite constituents. International Journal of Plasticity 104, pp. 39 - 53 (2018)
Jafari, M.; Jamshidian, M.; Ziaei-Rad, S.; Raabe, D.; Roters, F.: Constitutive modeling of strain induced grain boundary migration via coupling crystal plasticity and phase-field methods. International Journal of Plasticity 99, pp. 19 - 42 (2017)
Imran, M.; Kühbach, M.; Roters, F.; Bambach, M.: Development of a Model for Dynamic Recrystallization Consistent with the Second Derivative Criterion. Materials 10 (11), 1259, pp. 1 - 18 (2017)
Diehl, M.; Groeber, M.; Haase, C.; Roters, F.; Raabe, D.: Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach. JOM-Journal of the Minerals Metals & Materials Society 69 (5), pp. 848 - 855 (2017)
Diehl, M.; Wicke, M.; Shanthraj, P.; Roters, F.; Brueckner-Foit, A.; Raabe, D.: Coupled Crystal Plasticity–Phase Field Fracture Simulation Study on Damage Evolution Around a Void: Pore Shape Versus Crystallographic Orientation. JOM-Journal of the Minerals Metals & Materials Society 69 (5), pp. 872 - 878 (2017)
Zhang, H.; Diehl, M.; Roters, F.: A virtual laboratory using high resolution crystal plasticity simulations to determine the initial yield surface for sheet metal forming operations. International Journal of Plasticity 80, pp. 111 - 138 (2016)
Cereceda, D.; Diehl, M.; Roters, F.; Raabe, D.; Perlado, J. M.; Marian, J.: Unraveling the temperature dependence of the yield strength in single-crystal tungsten using atomistically-informed crystal plasticity calcula- tions. International Journal of Plasticity 78, pp. 242 - 265 (2016)
Diehl, M.; Shanthraj, P.; Eisenlohr, P.; Roters, F.: Neighborhood influences on stress and strain partitioning in dual-phase microstructures. An investigation on synthetic polycrystals with a robust spectral-based numerical method. Meccanica 51 (2), pp. 429 - 441 (2016)
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