| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
| Presentation Title |
Identification of Crystal Plasticity Model Parameters by Multi-objective Optimization Integrating Texture Evolution |
| Author(s) |
Daniel Savage, Marko Knezevic, Zhangxi Feng |
| On-Site Speaker (Planned) |
Daniel Savage |
| Abstract Scope |
Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. A set of model parameters are typically calibrated through the fitting of mechanical data such as stress–strain curves and lattice strains. Whereas, microstructural data such as texture evolution or twin fractions are used for verifying slip and twin activities are reasonable. In this work, we use a multi-objective genetic algorithm to identify crystal plasticity hardening parameters and incorporate texture into the optimization. The utility of the developed methodology is demonstrated through two case studies: 1) A series of textures from plane-strain compression of Nb is used to recover a dislocation hardening law; representing one of the first applications in which texture alone has been used to recover model parameters. 2) A large Ti texture and stress-strain dataset is demonstrated to constrain per mode Hall-Petch and hierarchical twin contributions in a complex dislocation hardening law. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Modeling and Simulation, Titanium |