| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling
|
| Presentation Title |
A Physics-based Machine Learning Approach to Microstructure-based Modeling of High Cycle Fatigue Life Prediction |
| Author(s) |
Dong Qian |
| On-Site Speaker (Planned) |
Dong Qian |
| Abstract Scope |
High cycle fatigue (HCF) is the dominant failure mechanism of many engineering applications. For fatigue life predictions safe-life and damage-tolerance approaches have been used extensively, however, they are limited due to the empirical nature. These limitations can be addressed by performing a direct numerical simulation of the fatigue loading history. These limitations motivate the development of a multiscale HCF simulation approach called the extended space-time finite element method (XTFEM) to be presented. To address the challenge in capturing nonlinear material behavior associated with material microstructures under the HCF loading condition, XTFEN is integrated with a microstructure-based HCF damage model based on machine learning using the HideNN-AI self-consistent clustering analysis. This implementation enables the direct modeling of complex material microstructures with much reduced computational cost. Finally, examples of HCF life prediction are presented to demonstrate the robustness of the proposed multiscale approach. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Modeling and Simulation, Other |