ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Robot Vision and Artificial Perception
This article is part of the Research TopicHarnessing Visual Computing to Revolutionize Manufacturing Efficiency and InnovationView all 3 articles
Application of Convolutional Neural Networks for Surface Discontinuities Detection in Shielded Metal Arc Welding Process
Provisionally accepted
- Mechanical Engineering Faculty, Technological University of Panama, Panama, Panama
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Detecting surface discontinuities in welds is essential to ensure the structural integrity of welded elements. This study addresses the limitations of manual visual inspection in shielded metal arc welding by applying convolutional neural networks for automated discontinuities detection. A specific image dataset of discontinuities on Shielded Metal Arc Welding weld seams was developed through controlled experiments with various electrode types and welder experience levels, resulting in 3,000 images. The YOLOv7 architecture was trained and evaluated on this dataset, achieving a precision of 97% and mAP@0.5 of 94%. Results showed that increasing the dataset size and training periods significantly improved detection performance, with optimal accuracy observed around 250–300 epochs. The model demonstrated robustness to moderate variations in image aspect ratio and generalization capabilities to an external dataset. This paper presents an approach for detecting SMAW weld surface discontinuities, offering a reliable and efficient alternative to manual inspection and contributing to the advancement of intelligent welding quality control systems.
Keywords: Shielded metal arc welding (SMAW), weld quality assurance, Weld SurfaceDiscontinuities, Convolutional Neural Networks, Computer Vision
Received: 21 May 2025; Accepted: 11 Nov 2025.
Copyright: © 2025 Mendieta, Quintero and Pinzón-Acosta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: César Pinzón-Acosta, cesar.pinzon1@utp.ac.pa
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.