| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Data-driven Plastic Anisotropy Predictions Using Crystal Plasticity and Deep Learning Models |
| Author(s) |
Hojun Lim, Taejoon Park, David Montes de Oca Zapiain, Farhang Pourboghrat |
| On-Site Speaker (Planned) |
Hojun Lim |
| Abstract Scope |
Traditional methods of characterizing plastic anisotropy in metal alloys require iterative experiments or high-fidelity computational simulations. To avoid expensive anisotropy characterization procedures, a novel data-driven anisotropy prediction model is developed from a large dataset of crystal plasticity (CP) calculations. A deep learning (DL) model was trained by analytical CP calculations of normalized yield stresses and plastic strain increments. The validity and accuracy of the DL model was assessed by additional validation dataset from CP calculations. Quantitative comparisons of CP and DL predictions show that the DL model accurately and efficiently links material’s initial crystallographic texture to plastic anisotropy. DL-based predictions were further assessed by performing finite element simulations of cup drawing using the non-quadratic yield function parameterized from CP, CP-FEM and DL anisotropy predictions. The DL-based simulation showed excellent agreement CP and CP-FEM, demonstrating that accurate anisotropy information was obtained without the need of performing expensive high-fidelity simulations.
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| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, ICME, Machine Learning |