A novel approach for detecting and classifying defects in metallic plates

Abstract

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.