Development of a Welding Defect Detection System in Metalworking Parts Using Digital Image Processing with Deep Learning

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Yngrid Xiomara Cruz Machaca, Alexander Javier Benavides Rojas

10.14445/23488549/IJECE-V12I7P111

How to Cite?

Yngrid Xiomara Cruz Machaca, Alexander Javier Benavides Rojas, "Development of a Welding Defect Detection System in Metalworking Parts Using Digital Image Processing with Deep Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 137-147, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P111

Abstract:

In metalworking industries dedicated to the manufacture of parts, a large number of welds are required, yet not all industries employ advanced technologies for detecting welding defects. As a result, quality control is often performed manually by workers, leading to longer processing times and a higher likelihood of misidentification of defects due to human error. This introduces additional costs to the manufacturing process. This article presents the implementation of a welding defect detection system for metalworking parts using Digital Image Processing (DIP) techniques combined with deep learning. The proposed system utilizes Convolutional Neural Networks (CNNs) trained to identify defects in analyzed metal parts, such as porosities, holes, cracks, bubbles, among others. Additionally, the system integrates a user interface designed to display detected defects in real time and alert supervisors, enabling timely decision-making in production. Finally, this research includes a cost-benefit analysis comparing the proposed system to the traditional method, with the aim of facilitating future real-world testing. The results demonstrate that this technology reduces production times and costs in metalworking welding plants.

Keywords:

Deep Learning, Digital image processing, Welding defect, Metalworking parts.

References:

[1] Ministry of Production of Peru, “Manufacturing Production Report,” Report, Office of Economic Studies, pp. 1-22, 2023.
[Publisher Link]
[2] M.A. Wahab, “6.03 - Manual Metal Arc Welding and Gas Metal Arc Welding,” Comprehensive Materials Processing, vol. 6, pp. 49-76, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jazmin Monserrat Rodriguez Torres, Carolina Reta, and Francisco Javier Ibarra Villegas, “Review of Non-Destructive Methods for the Evaluation of Resistance Spot Welding in the Automotive Industry,” Journal of Technological Sciences, vol. 7, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Hongjie Zhang et al., “A Novel Quality Evaluation Method for Resistance Spot Welding Based on the Electrode Displacement Signal and the Chernoff Faces Technique,” Mechanical Systems and Signal Processing, vol. 62-63, pp. 431-443, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yishuang Zhang, Cheuk Lun Chow, and Denvid Lau, “Artificial Intelligence-Enhanced Non-Destructive Defect Detection for Civil Infrastructure,” Automation in Construction, vol. 171, pp. 1-16, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yu Cheng et al., “Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology,” Research Square, vol. 20, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Dingming Yang et al., “Deep Learning Based Steel Pipe Weld Defect Detection,” Applied Artificial Intelligence, vol. 35, no. 15, pp. 1237-1249, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Wei Huang, and Radovan Kovacevic, “A Laser-Based Vision System for Weld Quality Inspection,” Sensors, vol. 11, no. 1, pp. 506-521, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ali Ebrahimi, Kamal Mirzaie, and Ali Mohamad Latif, “A Hybrid Approach of Dynamic Image Processing and Complex Network to Identify Repetitive Images of Welding Defects in Radiographs of Oil and Gas Pipelines,” International Journal of Nonlinear Analysis and Applications, vol. 14, no. 1, pp. 1671-1682, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] A. Niederwanger, D.H. Warner, and G. Lener, “The Utility of Laser Scanning Welds for Improving Fatigue Assessment,” International Journal of Fatigue, vol. 140, pp. 1-24, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] R. Praveen Kumar, R. Deivanathan, and R. Jegadeeshwaran, “Welding Defect Identification with Machine Vision System using Machine Learning,” Journal of Physics: Conference Series: National Science, Engineering and Technology Conference (NCSET)2020, Chennai, India, vol. 1716, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shaohua Dong et al., “Automatic Defect Identification Technology of Digital Image of Pipeline Weld,” Natural Gas Industry B, vol. 6, no. 4, pp. 399-403, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Liwei Deng, Yangang Guo, and Borong Chai, “Defect Detection on a Wind Turbine Blade Based on Digital Image Processing,” Processes, vol. 9, no. 8, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] József Szőlősi et al., “Welding Defect Detection with Image Processing on a Custom Small Dataset: A Comparative Study,” IET Collaborative Intelligent Manufacturing, vol. 6, no. 4, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Amir-M. Naddaf-Sh, Vinay S. Baburao, and Hassan Zargarzadeh, “Automated Weld Defect Detection in Industrial Ultrasonic B-Scan Images Using Deep Learning,” NDT, vol. 2, no. 2, pp. 108-127, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Lei Yang et al., “An Automatic Welding Defect Location Algorithm Based on Deep Learning,” NDT & E International, vol. 120, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Chen Ji, Hongbing Wang, and Haihua Li, “Defects Detection in Weld Joints Based on Visual Attention and Deep Learning,” NDT & E International, vol. 133, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Gwang-ho Yun, Sang-jin Oh, and Sung-chul Shin, “Image Preprocessing Method in Radiographic Inspection for Automatic Detection of Ship Welding Defects,” Applied Sciences, vol. 12, no. 1, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Honggui Deng et al., “Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed,” Symmetry, vol. 13, no. 9, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] William Percy Cuno Zuniga et al., “Design of a Terrain Mapping System for Low-cost Exploration Robots based on Stereo Vision,” Electrotechnical Review, no. 5, pp. 270-275, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hoanh Nguyen, Tuan Anh Nguyen, and Nguyen Duc Toan, “Optimizing Feature Extraction and Fusion for High-Resolution Defect Detection in Solar Cells,” Intelligent Systems with Applications, vol. 24, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Zhenyue Wang et al., “Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m,” Applied Sciences, vol. 13, no. 23, pp. 1-24, 2023.
[CrossRef] [Google Scholar] [Publisher Link]