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Proceedings Paper

Evaluation of residual stress gradients by diffraction methods with wavelets: a neural network approach
Author(s): Harald Wern; Marc Ringeisen
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Paper Abstract

The presence of residual stress gradients is often revealed by x-ray diffraction analysis. Because x-rays always detect an averaged information due to absorption, in the past some approaches have been employed to retrieve the true-z- profiles from the measured (tau) -profiles where (tau) is in general the 1/e information depth of the diffracted intensity. However, all problems which can be described as so called inverse problems like x-ray diffraction analysis are often extremely ill-conditioned. What makes the wavelet basis interesting is that individual wavelet functions are quite localized in space and simultaneously they also are quite localized in frequency. This particular kind of dual localization achieved by wavelets renders operators to be sparse to some high accuracy, when transformed into the wavelet domain. However, the number of required wavelet coefficients for the representation of a residual stress gradients in general still exceeds the number of measurements. therefore, a multilayer feed forward neural network approach has been investigated. With the implementation of a fast backtracking algorithm, suitable learning rates can be achieved. The advantages of this neural network approach, which to the authors knowledge is first introduce in the field of residual stress analysis, will be discussed.

Paper Details

Date Published: 8 February 1999
PDF: 11 pages
Proc. SPIE 3585, Nondestructive Evaluation of Aging Materials and Composites III, (8 February 1999); doi: 10.1117/12.339863
Show Author Affiliations
Harald Wern, Hochschule fuer Technik und Wirtschaft des Saarlandes (Germany)
Marc Ringeisen, Hochschule fuer Technik und Wirtschaft des Saarlandes (Germany)


Published in SPIE Proceedings Vol. 3585:
Nondestructive Evaluation of Aging Materials and Composites III
George Y. Baaklini; Carol A. Nove; Eric S. Boltz, Editor(s)

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