Abstract
Aircraft wing structures are subjected to different types of loads such
as static and dynamic loads throughout their life span. A methodology was developed to predict the static load applied on a wing rib without load cells using
Artificial Neural Network (ANN). In conjunction with the finite element modelling
of the rib, a classic two layer feed-forward networks were created and
trained on MATLAB using the back-propagation algorithm. The strain values
obtained from the static loading experiment was used as the input data for the
network training and the applied load was set as the output. The results obtained
from the ANN showed that this method can be used to predict the static load
applied on the wing rib to an accuracy of 92%.
as static and dynamic loads throughout their life span. A methodology was developed to predict the static load applied on a wing rib without load cells using
Artificial Neural Network (ANN). In conjunction with the finite element modelling
of the rib, a classic two layer feed-forward networks were created and
trained on MATLAB using the back-propagation algorithm. The strain values
obtained from the static loading experiment was used as the input data for the
network training and the applied load was set as the output. The results obtained
from the ANN showed that this method can be used to predict the static load
applied on the wing rib to an accuracy of 92%.
| Original language | English |
|---|---|
| Title of host publication | IFIP International Federation for Information Processing 2014 |
| Publisher | Springer Berlin Heidelberg |
| Pages | 576 |
| Number of pages | 584 |
| Volume | 436 |
| ISBN (Electronic) | 978-3-662-44654-6 |
| ISBN (Print) | 978-3-662-44653-9 |
| Publication status | Published - 22 Sept 2014 |
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