Version 1
: Received: 17 October 2024 / Approved: 18 October 2024 / Online: 21 October 2024 (08:36:04 CEST)
How to cite:
Say, D.; Mian Qaisar, S.; Krichen, M.; Zidi, S. Artificial Intelligence Assistive Non-Destructive Testing of Welding Joints: A Review. Preprints2024, 2024101518. https://doi.org/10.20944/preprints202410.1518.v1
Say, D.; Mian Qaisar, S.; Krichen, M.; Zidi, S. Artificial Intelligence Assistive Non-Destructive Testing of Welding Joints: A Review. Preprints 2024, 2024101518. https://doi.org/10.20944/preprints202410.1518.v1
Say, D.; Mian Qaisar, S.; Krichen, M.; Zidi, S. Artificial Intelligence Assistive Non-Destructive Testing of Welding Joints: A Review. Preprints2024, 2024101518. https://doi.org/10.20944/preprints202410.1518.v1
APA Style
Say, D., Mian Qaisar, S., Krichen, M., & Zidi, S. (2024). Artificial Intelligence Assistive Non-Destructive Testing of Welding Joints: A Review. Preprints. https://doi.org/10.20944/preprints202410.1518.v1
Chicago/Turabian Style
Say, D., Moez Krichen and Salah Zidi. 2024 "Artificial Intelligence Assistive Non-Destructive Testing of Welding Joints: A Review" Preprints. https://doi.org/10.20944/preprints202410.1518.v1
Abstract
Non-Destructive Testing (NDT) is an appealing technique for confirming the welding quality and ensuring structural integrity without causing damage. It assists in regulatory compliance, risk mitigation, and process optimization for improved industrial reliability. This study reviews potential NDT approaches. Through a rigorous analysis of existing surveys, we aim to decipher the current landscape and highlight the significant advancements in the field. Because of the potential of artificial intelligence (AI) assistive X-ray imaging-based NDT, we particularly examine the integration of AI algorithms and X-ray imaging in the NDT of welds. This convergence represents a paradigm shift, redefining traditional methods and ushering in a new era of precision, automation, and efficiency. As we navigate through potential X-ray imaging datasets, delve into crucial image processing techniques, examine feature extraction methods, and explore AI algorithms, our survey reveals the intricate interplay of technologies that drive automated weld defect categorization. The review broadens its focus by including various practical applications, highlighting the adaptable utility of AI-assistive X-ray imaging for weld defect detection in potential industrial applications. Moreover, the promising opportunities and nuanced challenges associated with integrating X-ray imaging and AI in weld integrity assessment are highlighted. It provides a comprehensive perspective on this rapidly evolving field.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
