Automated Extraction of Welds from Digitized Radiographic Images Based on MLP Neural Networks

Abstract

It is desired to automate inspection of welding flaws. Automated extraction of welds forms the first step in developing an automated weld inspection system. This article presents a multilayered perceptron (MLP) based procedure for extracting welds from digitized radiographic images. The procedure consists of three major components: feature extraction, MLP-based object classification, and postprocessing. For each object in the line image extracted from the whole image, four features are defined: the peak position (x1), the width (x2), the mean square error between the object and its Gaussian intensity plot (x3), and the peak intensity (x4). Fiftyone training samples were used to train MLP neural networks. The training of MLP classifiers is discussed. Trained MLP neural networks are subsequently used to test unlearned feature patterns and to identify whether the patterns are welds or not. Postprocessing is performed to remove noises (misclassified nonweld objects) and restore the continuity of weld line (discontinuity due to missed weld objects). Test results show that the procedure can successfully extract all welds (100%) from 25 radiographic images.