Author:
Ramana E.V.,Penekalapati Sai Varun,Kumar Namala Kiran
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.
Reference11 articles.
1. Shafeek H.I., Gadelmawla E.S., Abdel-Shafy A.A., and Elewa I.M.. "Assessment of welding defects for gas pipeline radiographs using computer vision." NDT & e International 37, 4 (4), 2004–2291.
2. Rathod Vijay R., Radhey Shyam Anand, and Ashok Alaknanda. "Comparative analysis of NDE techniques with image processing." Nondestructive Testing and Evaluation 27, 4 (4), 2012–2305.
3. Sebastian V., Bino A. Unnikrishnan , and Balakrishnan Kannan. "Gray level cooccurrence matrices: generalisation and some new features." arXiv preprint arXiv:1205.4831 (2012).
4. Kumar Jayendra, Anand Radhey Shyam, and Srivastava S.P.. "Multi-class welding flaws classification using texture feature for radiographic images." In 2014 International Conference on Advances in Electrical Engineering (ICAEE), pp. 1-4. IEEE, 2014.
5. Mery Domingo, and Angel Berti Miguel. "Automatic detection of welding defects using texture features." Insight-Non-Destructive Testing and Condition Monitoring 45, 10 (10), 2003–2676.