| About this Abstract |
| Meeting |
13th International Conference on the Technology of Plasticity (ICTP 2021)
|
| Symposium
|
13th International Conference on the Technology of Plasticity (ICTP 2021)
|
| Presentation Title |
On the Potential of Machine Learning Algorithms to Predict the Plasticity of Sheet Metal |
| Author(s) |
Maysam Gorji, Dirk Mohr |
| On-Site Speaker (Planned) |
Maysam Gorji |
| Abstract Scope |
Neural networks provide a potentially viable alternative to differential equation based constitutive models. Here, a neural network model is developed to describe the large deformation response of the non-quadratic Yld2000-2d yield criterion along HAH (homogeneous anisotropic hardening) model in sheet material. Using conventional return-mapping scheme, virtual experiments are performed to generate stress-strain data for random reversal and monotonic biaxial loading paths. Subsequently, a basic feed-forward and recurrent neural network model is trained and validated using the results from the virtual experiments. The results for a “shallow network” show remarkably good agreement with all experimental data. The identified neural network model is implemented into a user material subroutine und used in basic structural simulations such as notched tension and in-plane shear experiments. In addition to demonstrating the potential of neural networks for modeling the rate-independent plasticity of metals, their application to more complex problems involving strain-rate and temperature effects is discussed. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |