Identification of weld sub-surface defects by radiographic images using texture features

Abstract

Non-Destructive Testing (NDT) is important to detect sub-surface defects in the weldments to ensure the quality of weld joints. The weld radiographs are digitized using a high-resolution digital camera. Data augmentation techniques are applied to expand the radiographic image dataset. Multi-class defect classification is done using the Gray-level co-occurrence matrix as a feature extractor and these features are given as input to various classifiers for classifying slag inclusion, incomplete penetration, and acceptable weld bead classes. The proposed methodology achieved the highest accuracies of 84%,83%,80%,70%, and 64% respectively for GLCM plus Random Forest, GLCM plus XGBoost, GLCM plus lightGBM, GLCM plus KNN, and GLCM plus SVM. The technology of applying ML techniques on radiographic images in detection of defects in welding as well as other manufacturing processes can be a sustainable practice.