Automatic Detection of Root Pass Weld Defects of Gas Pipelines Using Expert Nonlinear Classifier

The welded joints radiogram often contain defects which the interpreter must identify and quantify, before he decides on their acceptability, by referring to non‐destructive testing standards and codes . Once the radiographic segmentation is accomplished providing a description in term of regions ( defect and background), the problem is then to interpret their contents. It is thus question of determining effective attributes which permit to characterize these defect regions and to even recognize them like class elements easily identifiable. In industrial radiography, we can obtain radiograms on which weld defects, if they exist, can have various sizes and orientations. In recent years there has been a marked advance in the research for the development of an automated system to analyze weld defects detected by radiographs. In a normal welded gas pipeline there are four passes of welding containing rootpass, hot‐pass, fillerpass and cover‐pass. The quality of the root‐pass has the most importance in determining the quality of weld with respect to other passes.This work describes a study of nonlinear pattern classifiers, implemented by artificial neural networks, to classify weld defects existent in rootpasses which have seen in the radiographic images of welds. Mainly the most important defects related to root passes are lack Of Penetration (LOP), Internal Concavity or Suck Back, Internal Root Undercut and Burn Through. Using a novel approach for this area of research, a criterion of neural relevance was applied to evaluate the discrimination capacity of the classes studied by the features used, aiming to prove that the quality of the features is more important than the quantity of features used.The results prove the efficiency of the techniques for the data used.

کلیدواژه ها:

Weld Defect ، Root Pass ، Gas Pipeline ، Expert Non‐linear Classifier