{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:56:21Z","timestamp":1780602981742,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects. The effectiveness of the proposed method is confirmed by testing its performance in processing an industrial dataset. The intended dataset contains 4479 X-ray images and belongs to six groups: cavity, cracks, inclusion slag, lack of fusion, shape defects, and normal defects. The devised technique achieved an average accuracy of 92%. This indicates that the approach is promising and can be used in contemporary solutions for the automated detection and categorization of welding defects.<\/jats:p>","DOI":"10.3390\/s23146422","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T01:06:36Z","timestamp":1689555996000},"page":"6422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5166-4928","authenticated-orcid":false,"given":"Dalila","family":"Say","sequence":"first","affiliation":[{"name":"Hatem Bettaher Laboratory, IResCoMath, University of Gabes, Gabes 6029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4330-6072","authenticated-orcid":false,"given":"Salah","family":"Zidi","sequence":"additional","affiliation":[{"name":"Hatem Bettaher Laboratory, IResCoMath, University of Gabes, Gabes 6029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4268-3482","authenticated-orcid":false,"given":"Saeed Mian","family":"Qaisar","sequence":"additional","affiliation":[{"name":"CESI LINEACT, 69100 Lyon, France"},{"name":"Electrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8873-9755","authenticated-orcid":false,"given":"Moez","family":"Krichen","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Al-Baha University, Al-Baha 65528, Saudi Arabia"},{"name":"ReDCAD Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3038, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8088202","DOI":"10.1155\/2022\/8088202","article-title":"Feature Fusion for Weld Defect Classification with Small Dataset","volume":"2022","author":"Hou","year":"2022","journal-title":"J. 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