Inverse problem approach using photoelastic analysis and artificial neural networks in tandem

This source preferred by Siamak Noroozi and John Vinney

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

http://www3.interscience.wiley.com/journal/118763924/abstract

Journal: Strain

Volume: 40

Pages: 73-77

ISSN: 0039-2103

DOI: 10.1111/j.1475-1305.2004.00108.x

This data was imported from Scopus:

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

Journal: Strain

Volume: 40

Issue: 2

Pages: 73-77

ISSN: 0039-2103

DOI: 10.1111/j.1475-1305.2004.00108.x

An ANN was trained to determine the applied loads on a mechanical component using photoelastic fringe orders. Two different beams, both fabricated from birefringent material, were used. One beam was subjected to pure bending and the other to four non-symmetrical and random loads. Completely random loads were applied to the beams and relative retardation (N) values were collected using a digital polariscope. These values were used as inputs to the ANN and loads were calculated. For the case of pure bending the average error between calculated loads and actual loads was <4% and for the un-symmetrical loading test it was <8%.

This data was imported from Web of Science (Lite):

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

Journal: STRAIN

Volume: 40

Issue: 2

Pages: 73-77

ISSN: 0039-2103

DOI: 10.1111/j.1475-1305.2004.00108.x

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