By Topic

A novel approach for detecting and classifying defects in metallic plates

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
S. Calcagno ; Dipt. di Informatica Matematica Elettronica e Trasporti, Univ. "Mediterranea" Reggio Calabria, Italy ; F. C. Morabito ; M. Versaci

In the field of nondestructive testing on defect identification in metallic plates, the shape reconstruction is still an open question. State-of-the-art technologies indeed enable the operator to locate the position of a defect but not its shape. The aim of this paper is to make a contribution to the solution of this side of the problem suggesting a novel methodology based on a neurofuzzy approach. Sugeno's neurofuzzy inferences have been carried out for this purpose, as a first step in this direction. Fuzzy entropy was then exploited to measure how far is a given defect from a well-known depth. A sort of classification based on the depth of a defect has been performed this way.

Published in:

IEEE Transactions on Magnetics  (Volume:39 ,  Issue: 3 )