Juntunen, P.; Raabe, D.; Karjalainen, P.; Kopio, T.; Bolle, G.: Optimizing continuous annealing of IF steels for improving their deep drawability. Metallurgical and Materials Transactions A 32, pp. 1989 - 1995 (2001)
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)
Raabe, D.; Becker, R. C.: Coupling of a crystal plasticity finite element model with a probabilistic cellular automaton for simulating primary static recrystallization in aluminum. Modelling and Simulation in Materials Science and Engineering 8, pp. 445 - 462 (2000)
Raabe, D.; Miyake, K.; Takahara, H.: Processing, microstructure, and properties of ternary high-strength Cu–Cr–Ag in situ composites. Material Science and Engineering A 291, pp. 186 - 197 (2000)
Raabe, D.; Mattissen, D.: Experimental investigation and Ginzburg-Landau modeling of the microstructure dependence of superconductivity in Cu–Ag–Nb wires. Acta Materialia 47 (3), pp. 769 - 777 (1999)
Mattissen, D.; Raabe, D.; Heringhaus, F.: Experimental investigation and modeling of the influence of microstructure on the resistive conductivity of a Cu–Ag–Nb in situ composite. Acta Materialia 47, pp. 1627 - 1634 (1999)
Marx, V.; Raabe, D.; Engler, O.; Gottstein, G.: Simulation of the texture evolution during annealing of cold rolled BCC and FCC matals using a cellular automation approach. Textures and Microstructures 28, pp. 211 - 218 (1997)
Raabe, D.: Texture simulation for hot rolling of aluminium by use of a Taylor model considering grain interactions. Acta Metallurgica et Materialia 43 (3), pp. 1023 - 1028 (1995)
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.
Janssens, K. G. F.; Raabe, D.; Kozeschnik, E.; Miodownik, M. A.; Nestler, B.: Computational Materials Engineering – An Introduction to Microstructure Evolution. Academic Press, Elsevier, USA (2007), 360 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)
Magnetic properties of magnetocaloric materials is of utmost importance for their functional applications. In this project, we study the magnetic properties of different materials with the final goal to discover new magnetocaloric materials more suited for practical applications.
Laser Powder Bed Fusion (LPBF) is the most commonly used Additive Manufacturing processes. One of its biggest advantages it offers is to exploit its inherent specific process characteristics, namely the decoupling the solidification rate from the parts´volume, for novel materials with superior physical and mechanical properties. One prominet…
The aim of the Additive micromanufacturing (AMMicro) project is to fabricate advanced multimaterial/multiphase MEMS devices with superior impact-resistance and self-damage sensing mechanisms.
We have studied a nanocrystalline AlCrCuFeNiZn high-entropy alloy synthesized by ball milling followed by hot compaction at 600°C for 15 min at 650 MPa. X-ray diffraction reveals that the mechanically alloyed powder consists of a solid-solution body-centered cubic (bcc) matrix containing 12 vol.% face-centered cubic (fcc) phase. After hot compaction, it consists of 60 vol.% bcc and 40 vol.% fcc. Composition analysis by atom probe tomography shows that the material is not a homogeneous fcc–bcc solid solution
Local lattice distortion is one of the core effects in complex concentrated alloys (CCAs). It has been expected that the strength CCAs can be improved by inducing larger local lattice distortions. In collaboration with experimentalists, we demonstrated that VCoNi has larger local lattice distortions and indeed has much better strength than the…
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization as in micropillar compression. 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.…
In this project we pursue recent developments in the field of austenitic steels with up to 18% reduced mass density. The alloys are based on the Fe-Mn-Al-C system.