| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Micromechanics Surrogate Model for Fatigue Life Prediction of Composites |
| Author(s) |
Brandon Hearley, Steven M. Arnold |
| On-Site Speaker (Planned) |
Brandon Hearley |
| Abstract Scope |
Fatigue modeling of composites, particularly using a multiscale approach, can be very costly, due the number of iterations that must occur when evaluating the damage state of each constituent within each ply, thus making it difficult for engineering in early design stages to evaluate a large number of potential candidate material configurations. In this work, a machine learning surrogate model for ply level micromechanics fatigue damage of composites is presented, enabling S-N curve prediction of laminates for any arbitrary number of plies. Training data is created using they physics-based Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) tool for a single ply under multiaxial loading, predicting the damage increment and corresponding cycles to damage for each ply. The developed machine learning model will allow engineers to quickly get a reasonably accurate estimate of the fatigue life a composite with any arbitrary number of plies subject to any multiaxial load. |
| Proceedings Inclusion? |
Undecided |