About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
A Deep Neural Network Formulation for Anisotropic Yield Prediction |
Author(s) |
Anderson Nascimento, Sharan Roongta, Martin Diehl, Irene Beyerlein |
On-Site Speaker (Planned) |
Anderson Nascimento |
Abstract Scope |
At the continuum level, the plastic anisotropy of a wide range of metals and alloys is well described by advanced phenomenological yield surfaces. Relevant difficulties in their usage, however, are associated with the non-trivial parameter identification process and the non-uniqueness of the anisotropy coefficients, commonly noticed in practice and reported in the literature. Alternative avenues for plastic flow prediction have been studied, and machine learning based approaches have gained notoriety due to their high fitting capabilities. A deep neural network based surrogate model, trained with virtual stress data points and with performance comparable to advanced phenomenological yield functions has been developed. Important features such as well defined flow vector, convexity and yield prediction are studied and compared against benchmark yield criteria. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Mechanical Properties, |