Classification of defects in ultrasonic nondestructive testing (NDT)/nondestructive evaluation (NDE) has a role of primary importance in all those applications in which the knowledge of the typology of defect is crucial for the manufact destination. In such a context, the necessity to have efficient investigation instruments for a correct classification analysis emerges clearly. A defect, even when invisible to the naked eye, can be revealed as the cause that reduces the similarity of a measured signal with respect to a reference. Considering the fuzziness intrinsic in the signals, the reliance on fuzzy techniques to evaluate similarity appears desirable. Two fundamental achievement of research in such field, which both derive from the fuzzy thinking and which share common traits, are computing with words (CW) and the concept of fuzzy similarity (FS). In CW, a word is considered as a label of a fuzzy set of points clustered by similarity (granule) which lead to a particular formulation of a bank of fuzzy rules structured per classes. FS is an evaluation index of similarity among entities (for example signals) particularly useful for the formation of specific classes. Both approaches are based on computational linguistics (for example, descriptive formalism in natural language). This chapter is conceptually divided into two parts: the first one is dedicated to the development of detection and classification techniques of defectiveness for ultrasonic NDE by means of CW, while the second one, with the same purpose, proposes an approach based on FS.
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