Computer-aided shape analysis and classification of weld defects in industrial radiography based invariant attributes and neural networks

Abstract

The interpretation of possible weld discontinuities in industrial radiography is ensured by human interpreters. Consequently, it is submitted to subjective considerations such as the aptitude and the experiment of the interpreter, in addition of the poor quality of radiographic images, due essentially to the exposure conditions. These considerations make the weld quality interpretation inconsistent, labor intensive and sometimes biased. It is thus desirable to develop computer-aided techniques to assist the interpreter in evaluating the quality of the welded joints. For the characterization of the weld defect region, looking for features which are invariant regarding the usual geometric transformations proves to be necessary because the same defect can be seen from several angles according to the orientation and the distance from the welded framework to the radiation source. Thus, a set of invariant geometrical attributes which characterize the defect shape is proposed. The principal component analysis technique is used in order to reduce the number of attribute variables in the aim to give better performance for defect classification. Thereafter, an artificial neural network for weld defect classification was used. The proposed classification consists in assigning the principal types of weld defects to four categories according to the morphological characteristics of the defects usually met in practice.