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PROBABILITY OF DEFECT DETECTION IN DIFFERENT WELDING PROCESSES BY USING RADIOGRAPHY TESTING

AUTHORS:
J. Kalyana Venu Gopal
Mentor
N. Phani Raja Rao
Affiliation
Department of Mechanical Engineering, Sri Venkateswara Institute of Technology, NH–44, Hampapuram, Anantapur, Andhra Pradesh, India
DOI: 10.55041/ijsmt.v2i5.341
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

This study aims to evaluate the probability of detecting defects in different welding processes using radiographic testing as a non-destructive evaluation method. The research examines the effectiveness of radiography in identifying welding defects through an extensive literature review covering welding techniques, radiographic inspection methods, and factors influencing defect detection probability. The experimental work involved preparing welded specimens using three welding processes gas tungsten arc welding (GTAW), shielded metal arc welding (SMAW), and brazing on 5 mm thick stainless steel SS316 and brass plates with suitable filler materials. The fabricated specimens were subjected to radiographic inspection and assessed by trained radiographers for defect identification. The results showed that defect detection probability varies with the welding process, with GTAW providing the highest detection rate, followed by SMAW and brazing. The findings confirm that radiographic testing is an effective technique for detecting welding defects and that optimizing radiographic parameters according to the welding method can further improve detection efficiency. The study also discusses the significance of these results for advancements in welding technology and radiographic testing practices.


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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 20 2026
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Gopal, J. K. V. (2026). Probability of Defect Detection in Different Welding Processes by using Radiography Testing. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.341

Gopal, J.. "Probability of Defect Detection in Different Welding Processes by using Radiography Testing." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.341.

Gopal, J.. "Probability of Defect Detection in Different Welding Processes by using Radiography Testing." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.341.

References
[1] F. Zuo, J. Liu, M. Fu, J. Lu and H. Liu, "An Effective Detection Method for Complex Weld Defects Based on Adaptive Feature Pyramid," 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, Yibin, China, 2023, pp. 1-5, doi: 10.1109/SAFEPROCESS58597.2023.10295953.

[2] Tusongjiang.Kari, A. Yimamu, Y. Zhou and X. Ma, "Study on Weld Defects Detection and Recognition Based on YOLOv5," 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Chongqing, China, 2025, pp. 1246-1250, doi: 10.1109/ICICML67980.2025.11333441.

[3] F. Zuo, J. Liu, W. Yu, Y. Ren, L. Wang and Z. Zhao, "A X-Ray Based Dual-Expert Detection Method for Automatic Welding Defect Inspection," in IEEE Transactions on Consumer Electronics, vol. 71, no. 4, pp. 9427-9438, Nov. 2025, doi: 10.1109/TCE.2025.3616604.

[4] Z. Shen and J. Sun, "Welding seam defect detection for canisters based on computer vision," 2013 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China, 2013, pp. 788-793, doi: 10.1109/CISP.2013.6745272.

[5] Y. Wang, N. Huang, G. Li and L. Yang, "An Efficient CNN-Based Detector for Weld Defect Detection Based on Enhanced YOLOv8," 2025 40th Youth Academic Annual Conference of Chinese Association of Automation (YAC), Zhengzhou, China, 2025, pp. 1570-1575, doi: 10.1109/YAC66630.2025.11150199.

[6] L. Yang, J. Fan, B. Huo, E. Li, and Y. Liu, “A nondestructive automatic defect detection method with pixelwise segmentation,” Knowledge-Based Systems, vol. 242, p. 108338, 2022.

[7] L. Yang, E. Li, T. Long, J. Fan, Y. Mao, Z. Fang, and Z. Liang, “A welding quality detection method for arc welding robot based on 3d reconstruction with sfs algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 94, pp. 1209–1220, 2018.

[8] W. Zeng, M. Cai, P. Wang, T. Lu, and F. Yao, “Application of laser ultrasonic technique for detecting weld defect based on fdst method,” Optik, vol. 221, p. 165366, 2020.

[9] C. Zhu, H. Yuan, and G. Ma, “An active visual monitoring method for gmaw weld surface defects based on random forest model,” Materials Research Express, vol. 9, no. 3, p. 036503, 2022.

[10] L. Yang, J. Fan, B. Huo, and Y. Liu, “Inspection of welding defect based on multi-feature fusion and a convolutional network,” Journal of Nondestructive Evaluation, vol. 40, pp. 1–11, 2021.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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