Weld Classification In Radiographic Images : Data Mining Approach

The need for non-destructive evaluation (NDE) technologies for maintenance of complex welded structures such as pressure vessels, load bearing structural members and power plants has long been recognized. This paper presents an application of data mining approach for weld data extracted from reported radiographic images. Data mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent times, machinelearning models such, as neural networks are becoming standard tools for data mining of scientific data. This paper addresses various issues related to data mining and demonstrates their application. The study highlights the two major aspects of insight of data and prediction of the model for the problem domain. INTRODUCTION The assessment of the safety and reliability of existing welded structures such as pressure vessels, load bearing structural members and power plants, has been the focus of much investigation in recent years. An assessment of welded structural system requires knowledge of their strength, response characteristics, quantitative and qualitative data concerning the current state of the structure, and a methodology to integrate various types of information into decisionmaking process of evaluating the safety of entire structure. Perhaps the most challenging aspect of weld evaluation is need for developing a rational methodology to synthesize the diverse information related to the structural welds condition and their behavior. In practice, non-destructive evaluation (NDE) technologies have been used very often for weld evaluation (Berger, 1977, Bray and Stanley, 1989). In a broad sense, NDE can be viewed as the methodology used to assess the integrity of the structure without compromising its performance. Recently, many studies have reported results where signal processing and neural networks (NN) * Conference Speaker