A Novel Tool for Automated Evaluation of Radiographic Weld Images

Radiography is one of the oldest and the most widely used NDT method for the detection of volumetric defects in welds and castings. Once a radiograph of a weld or a casting or an assembly is taken, the radiographer examines the same. The task of the radiographer consists of identifying the defects and quantitatively evaluating the same based on codes and specifications. Radiographic interpretation primarily depends on the expertise of the individual radiographer. To overcome the subjectivity involved in human interpretation, it is thus desirable to develop a computer based automated system to aid in the interpretation of radiographs. Towards this goal, the authors have developed a flowchart chalking out the various stages involved. Typical weld images of tube to tubesheet weld joints were digitised using high resolution digitiser. The images were segmented and 52 invariant moments were computed to be used as features. The results of these are presented in this paper. Once the features (invariant moments) are extracted and ranked, a neural network classifier based on error back-propagation has to classify the (top ranking) features and evaluate the image for acceptance or rejection. Introduction : Radiography is one of the oldest and the most widely used NDT method for the detection of volumetric defects in welds and castings. Though imaging plates and real time systems based on image intensifiers and flat panel detectors are available, more than 75% of the radiography is still being carried out using films. The basic advantage of films is their higher resolution and their flexible usability for any kind of job – be it straight or curved. Once a radiograph of a weld, a casting or an assembly is taken, the radiographer examines the same for identifying the defects and quantitatively evaluating the same based on codes and specifications. Radiographic interpretation primarily depends on the expertise of the individual radiographer. The task can become complicated when the radiographic contrast and sensitivity is poor, making interpretation quite difficult. Human interpretation of radiographs is also subjective and labor intensive. When a number of radiographs are to be interpreted by a single person within a stipulated time, operator fatigue can result in inconsistent and biased evaluations. Apart from all these, interpretation of radiograph is a science and art and depends much on the experience and capabilities of the operator. It is thus desirable to develop a computer based automated system to aid in the interpretation of radiographs. Such a system would ensure uniformity and thereby reliability in the interpretation of radiographs. An extensive database pooling the resources of the experts would be needed for effective interpretation. Such a database would serve as a repository of knowledge for the future. Review of literature indicates that limited work has been done in this particular area. T.W.Liao et. al have used MLP neural network and case based reasoning for the detection of welding flaws [1-3], Nacereddine nafaa et. al [4] have used geometric invariant moments to construct a set of weld defect descriptors in X-ray images and developed a classifier based on multiplayer feed forward neural network. At the authors lab, ANN, has been successfully applied for the classification of defects in eddy current testing and for the prediction of temperature / strain rate during tensile deformation of AISI 316 SS[5,6]. As part of the technology development program, efforts have been initiated at the authors lab for the development of a computer based automated radiographic evaluation system for the interpretation of the microfocal radiographs of the tube to tubesheet weld joints of the steam generator of prototype fast breeder reactor. In this paper, we outline the approach being adopted for the development of such a system. The preliminary result of feature classification based on geometric invariant moments using the DESKPACK [7] System Software (developed at the author’s laboratory) is presented.