| 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 |
Neural Network Surrogates Model for Metals Undergoing Yield Point Phenomena within Finite Element Analysis |
| Author(s) |
Jason P. Allen, Jiahao Cheng, Xiaohua Hu, Xin Sun |
| On-Site Speaker (Planned) |
Jason P. Allen |
| Abstract Scope |
Finite element analysis (FEA) has yielded results in excellent agreement with experiments for a wide range of mechanical simulations and material constitutive models. However, it is often the case that simple material models are unable to capture the wide range of behavior found in real materials without increasing the complexity of the model and simulation time. For example, a mathematical constitutive model for BCC metals that show upper and lower yield points (i.e., the yield point phenomenon) is not available. In this work, a neural network is trained using the constitutive response for the tungsten-tantalum alloy system for various temperatures and strain rates. The neural network is then used as a surrogate model within FEA simulations with the calculated stress-strain response compared to the experimentally measured data. It will be shown that the trained neural network surrogate model captures the material behavior remarkably well. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |