Authors :
Sonal Chaudhari; Siddhant Nitin Rege; Sakshi Ganesh Manjrekar; Aarti Aklu Gupta; Sahil Pravin Satardekar
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/4hbj2pvj
Scribd :
https://tinyurl.com/48yfnepv
DOI :
https://doi.org/10.38124/ijisrt/25mar1806
Google Scholar
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Abstract :
In several industries, such as manufacturing, construction, and the automotive sector, welding is an essential
procedure. Safety and structural integrity are directly impacted by weld quality. Conventional welding inspection techniques
result in inconsistencies and inefficiencies because they are labor-intensive, manual, and prone to human error. This study uses
convolutional neural networks (CNN) and machine learning (ML) to offer an automated welding fault diagnosis method. Before
assigning the weld to one of six categories—Good Weld, Burn Through, Contamination, Misalignment, Lack of Penetration,
and Lack of Fusion—the system first confirms whether welding has been done. The model achieves great accuracy in defect
identification after being trained on a variety of datasets. This method is appropriate for industrial applications since it increases
efficiency, decreases reliance on humans, and improves the accuracy of defect identification by utilizing deep learning
techniques.
Keywords :
Machine Learning, CNN, Welding Fault Detection, Image Processing, Automated Inspection.
References :
- American Welding Society (AWS). (2020). Welding Handbook. Miami, FL: American Welding Society.
- B. A. Davis, "Non-Destructive Testing Methods for Welds," Journal of Materials Engineering, vol. 45, no. 3, pp. 123-130, 2021.
- J. Smith and R. Johnson, "Welding Defects: Causes and Prevention," International Journal of Welding Science, vol. 12, no. 2, pp. 45-60, 2022.
- K. R. K. Rao, "Machine Learning Applications in Welding," Journal of Manufacturing Processes, vol. 35, pp. 123-135, 2020.
- M. T. Brown, "Advancements in Ultrasonic Testing for Weld Inspection," NDT & E International, vol. 112, pp. 123-130, 2020.
- S. Lee, "Deep Learning for Weld Defect Detection," IEEE Transactions on Industrial Informatics, vol. 16, no. 4, pp. 2345-2353, 2020.
- Palma-Ramírez, R., Ross-Veitiá, J., & Rodríguez, D. (2024). Deep convolutional neural network for weld defect classification in radiographic images. Journal of Manufacturing Processes, 78, 345-356.
In several industries, such as manufacturing, construction, and the automotive sector, welding is an essential
procedure. Safety and structural integrity are directly impacted by weld quality. Conventional welding inspection techniques
result in inconsistencies and inefficiencies because they are labor-intensive, manual, and prone to human error. This study uses
convolutional neural networks (CNN) and machine learning (ML) to offer an automated welding fault diagnosis method. Before
assigning the weld to one of six categories—Good Weld, Burn Through, Contamination, Misalignment, Lack of Penetration,
and Lack of Fusion—the system first confirms whether welding has been done. The model achieves great accuracy in defect
identification after being trained on a variety of datasets. This method is appropriate for industrial applications since it increases
efficiency, decreases reliance on humans, and improves the accuracy of defect identification by utilizing deep learning
techniques.
Keywords :
Machine Learning, CNN, Welding Fault Detection, Image Processing, Automated Inspection.
