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http://dx.doi.org/10.6109/jkiice.2021.25.2.193

A Development of Welding Information Management and Defect Inspection Platform based on Artificial Intelligent for Shipbuilding and Maritime Industry  

Hwang, Hun-Gyu (Division of Ocean ICT & Advanced Materials Technology Research, Research Institute of Medium & Small Shipbuilding)
Kim, Bae-Sung (Division of Ocean ICT & Advanced Materials Technology Research, Research Institute of Medium & Small Shipbuilding)
Woo, Yun-Tae (Division of Ocean ICT & Advanced Materials Technology Research, Research Institute of Medium & Small Shipbuilding)
Yoon, Young-Wook (ViewOn)
Shin, Sung-chul (Department of Naval Architecture and Ocean Engineering, Busan University)
Oh, Sang-jin (Department of Naval Architecture and Ocean Engineering, Busan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.2, 2021 , pp. 193-201 More about this Journal
Abstract
The welding has a high proportion of the production and drying of ships or offshore plants. Non-destructive testing is carried out to verify the quality of welds in Korea, radiography test (RT) is mainly used. Currently, most shipyards adopt analog-type techniques to print the films through the shoot of welding parts. Therefore, the time required from radiography test to pass or fail judgment is long and complex, and is being manually carried out by qualified inspectors. To improve this problem, this paper covers a platform for scanning and digitalizing RT films occurring in shipyards with high resolution, accumulating them in management servers, and applying artificial intelligence (AI) technology to detect welding defects. To do this, we describe the process of designing and developing RT film scanning equipment, welding inspection information integrated management platform, fault reading algorithms, visualization software, and testing and verification of each developed element in conjunction.
Keywords
Welding quality information management platform; Welding defect inspection; Automatic detection; Non-destructive test (NDT); Radiography test (RT);
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1 B. S. Kim, H. G. Hwang, C. S. Song, and K. T. Lee, "A development of automatic extraction system for welding inspection information based on shipbuilding and maritime CAD," Journal of the Korea Institute of Information and Communication Engineering, vol. 24, no. 1, pp. 28-36, Jan. 2020.   DOI
2 C. S. Song, J. H. Cho, and B. S. Kim, "A Design of artificial intelligence reading platform for radiographic testing of welds," in Proceeding of the 42th Korean Society of Maine Engineering Fall Conference, Busan, pp. 223, 2018.
3 B. S. Kim, J. H. Cho, and H. G. Hwang, "An implementation of integrated platform for management of welding quality in shipbuilding and marine engineering," in Proceeding of the 44th Korean Society of Maine Engineering Fall Conference, Online, pp. 170, 2020.
4 B. S. Kim, J. H. Cho, C. S. Song, and H. G. Hwang, "An implementation of integrated welding inspection information management system for ships and offshore plant," in Proceeding of the 43th Korean Society of Maine Engineering Fall Conference, Busan, pp. 156, 2019.
5 N. Nacereddine, M. Zelmat, S. S. Belaïfa, and M. Tridi, "Weld defect detection in industrial radiography based digital image processing," International Journal of Computer and Information Engineering, vol. 1, no. 2, Jan. 2007.
6 S. E. Florence, R. V. Samsingh, and V. Babureddy, "Artificial intelligence based defect classification for weld joints," in Proceeding of the 2nd International Conference on Advances in Mechanical Engineering, Chennai, pp. 1-13, 2018.
7 R. K. Kountchev, S. H. Rubin, V. T. Todorov, and R. A. Kountcheva, "A detection of welding defects," in International Journal of Reasoning-based Intelligent Systems, vol. 3, no. 1, pp. 34-43, Dec. 2011.   DOI
8 W. Hou, Y. Wei, Y. Jin, and C. Zhu, "Automatic detection of welding defects using deep neural network," in Proceeding of the 10th International Conference on Computer and Electrical Engineering, Edmonton, pp. 1-10, 2017.
9 S. M. Shin, C. N. Jin, J. Y. Yu, and S. H. Rhee, "Real-time detection of weld defects for automated welding process base on deep neural network," Journal of Multidisciplinary Digital Publishing Institute, vol. 10, no. 3, pp. 1-16, Mar. 2020.
10 Y. J. Cruz, M. Rivas, R. Quiza, G. Beruvides, and R. E. Haber, "Computer vision system for welding inspection of liquefied petroleum gas pressure vessels based on combined digital image processing and deep learning techniques," Journal of Multidisciplinary Digital Publishing Institute, vol. 20, no. 16, pp. 1-13, Aug. 2020.
11 M. Sundaram, J. P. Jose, and G. Jaffino, "Welding defects extraction for radiographic images using c-means segmentation method," in Proceeding of the International Conference on Communication and Network Technologies, Sivakasi, pp. 79-83, 2014.
12 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN : Towards real-time object detection with region proposal networks," Advances in neural information processing systems (NIPS), 2015.
13 H. G. Hwang, B. S. Kim, J. H. Cho, and C. S. Song, "A requirements analysis of integrated data platform for automatic testing of welding quality based on artificial intelligence in shipbuilding and maritime domain," in Proceeding of the 43th Korean Society of Maine Engineering Spring Conference, Mokpo, pp. 272, 2019.
14 S. J. Oh, B. C. Park, D. S. Shin, C. O. Lim, and S. C. Shin, "Automatic detection of welding area using deep learning," in Proceeding of the Joint Academic Conference of the Korean Association of Ocean Science and Technology Societies, Jeju, pp. 454-460, 2018.
15 S. J. Oh, B. C. Park, C. O. Lim, and S. C. Shin, "Automatic detection of welding defects using faster R-CNN," in Proceeding of the Society of Naval Architects of Korea Annual Autumn Conference, Changwon, pp. 236-240, 2018.