Real-time load determination for in-service components using photostress analysis and artificial neural networks in tandem

This source preferred by Siamak Noroozi and John Vinney

Authors: Noroozi, S., Amali, R. and Vinney, J.

Pages: 841-845

Publisher: CSREA Press

Place of Publication: Las Vegas

This data was imported from DBLP:

Authors: Noroozi, S., Amali, R. and Vinney, J.

Editors: Arabnia, H.R., Joshua, R. and Mun, Y.

http://www.informatik.uni-trier.de/~ley/db/conf/icai/icai2003-2.html

Pages: 841-845

Publisher: CSREA Press

This data was imported from Scopus:

Authors: Noroozi, S., Amali, R. and Vinney, J.

Volume: 2

Pages: 841-845

In this study, an Artificial Neural Network (ANN) has been trained to determine the applied loads on a mechanical component using Photoelastic fringe orders. Two different beam configurations were used to demonstrate this idea, one beam was subjected to pure bending and the other subjected to four non-symmetrical and random loads. These beams were fabricated from birefringent material. In all cases the inverse method approach accurately predicted the loading based on the fringe order.

The data on this page was last updated at 05:17 on May 25, 2020.