Ma, A.; Roters, F.; Raabe, D.: Numerical study of textures and Lankford values for FCC polycrystals by use of a modified Taylor model. Computational Materials Science 29, 3, pp. 259 - 395 (2004)
Raabe, D.; Roters, F.: Using texture components in crystal plasticity finite element simulations. International Journal of Plasticity 20, pp. 339 - 361 (2004)
Roters, F.: Simulation der Umfornmung von metallischen Werkstoffen nach der Texturkomponenten-Kristallplastitizitäts-FEM. Simulation, pp. 50 - 53 (2003)
Roters, F.: A new concept for the calculation of the mobile dislocation density in constitutive models of strain hardening. Physica Status Solidi (b), pp. 68 - 74 (2003)
Raabe, D.; Zhao, Z.; Park, S. J.; Roters, F.: Theory of orientation gradients in plastically strained crystals. Acta Materialia 50 (2), pp. 421 - 440 (2002)
Karhausen, K. F.; Roters, F.: Development and application of constitutive equations for the multiple-stand hot rolling of Al-alloys. Journal of Materials Processing Technology 123, pp. 155 - 166 (2002)
Raabe, D.; Roters, F.; Zhao, Z.: Texture component crystal plasticity finite element method for physically-based metal forming simulations including texture update. Proc. 8th Int. Conf. on Aluminium Alloys, pp. 31 - 36 (2002)
Roters, F.; Zhao, Z.: Application of the texture component crystal plasticity finite element method for deep drawing simulations - A comparison with Hill’s yield criterion. Advanced Engineering Materials 4, pp. 221 - 223 (2002)
Roters, F.; Raabe, D.; Gottstein, G.: Work hardening in heterogeneous alloys - A microstructural approach based on three internal state variables. Acta Materialia 48 (17), pp. 4181 - 4189 (2000)
Roters, F.; Eisenlohr, P.; Bieler, T. R.; Raabe, D.: Crystal Plasticity Finite Element Methods in Materials Science and Engineering. Wiley-VCH, Weinheim (2010), 197 pp.
Shanthraj, P.; Diehl, M.; Eisenlohr, P.; Roters, F.; Raabe, D.: Spectral Solvers for Crystal Plasticity and Multi-physics Simulations. In: Handbook of Mechanics of Materials, pp. 1347 - 1372 (Eds. Hsueh, C.-H.; Schmauder, S.; Chen, C.-S.; Chawla, K. K.; Chawla, N. et al.). Springer, Singapore (2019)
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
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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…