| About this Abstract |
| Meeting |
2024 ASC Technical Conference, US-Japan Joint Symposium, D30 Meeting
|
| Symposium
|
2024 ASC Technical Conference, US-Japan Joint Symposium, D30 Meeting
|
| Presentation Title |
Integrated Process and Failure Analysis in Composites Using Multi-Fidelity Simulations and Machine Learning |
| Author(s) |
Amirali Eskandariyun, Huilong Fu, Navid Zobeiry |
| On-Site Speaker (Planned) |
Amirali Eskandariyun |
| Abstract Scope |
For accurately predicting the failure response of composite structures, it is crucial to consider the impacts of process-induced residual stresses and defects. However, given the complex multi-scale nature of both process-induced defects and composite failure, as well as the trade-off between simulation fidelity and computational speed, this aspect is often neglected. To address this challenge, we have developed a novel approach that utilizes both low- and high-fidelity finite element (FE) simulations, as well as surrogate machine learning (ML) models, for integrated process and failure analyses. Our approach begins with macro-scale process simulation of thermo-chemical-mechanical responses. This is followed by micro-scale analysis of residual stresses and failure mechanisms, utilizing two distinct modeling fidelities. By generating large datasets from low-fidelity simulations and enriching them with smaller, high-fidelity datasets, we then train surrogate ML models. These models achieve the precision of high-fidelity simulations but operate at speeds surpassing even those of low-fidelity simulations. Benchmark studies, using the HEXCEL AS4/8552 material system, demonstrate our approach's ability to significantly accelerate the simulation speed of high-fidelity models while enhancing accuracy by integrating process and failure analyses. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |