| 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 |
Failure Prediction of Fiber-Reinforced Polymer Composite Materials Using Data-Driven Modeling |
| Author(s) |
Nicole Sharp, Allyson Fontes, Farjad Shadmehri |
| On-Site Speaker (Planned) |
Allyson Fontes |
| Abstract Scope |
In many engineering applications, the use of Fiber-Reinforced Polymer (FRP) composite materials has been on the rise due to their desirable properties. However, conventional theories are not sufficiently accurate in predicting the complex failure of these materials, as displayed by the World-Wide Failure Exercises (WWFE) I and II. The development of data-driven models using Deep Neural Networks (DNN) has shown promising results in predicting the failure strength of composite materials and has proven to be more accurate and time-efficient than conventional theories. Previous studies have used experimental failure data of laminates under biaxial and triaxial loadings from WWFE I and II to develop a data-driven failure model for FRP composite materials using a DNN framework. The features describe the lamina properties, layup sequence, and loading conditions of the test specimen. While this model effectively predicted failure envelopes consistent with the experimental data with an acceptable degree of accuracy, it had difficulty predicting the transition region from uniaxial to biaxial loading, and some sections of the envelope were non-convex. The present study aims to improve the data-driven model by proposing new features that fix the transition region issue and enhance the overall failure predictions. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |