• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.28-36
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• 2020

In shipbuilding industry, there is conducting most of works by welding at the shipyard, and the volume of welding is increasing with international trends of green ships. Welding joint is guaranteed quality through non-destructive testing (NDT). The manual welding inspection report is produced by identifying the drawings designed, which results in losses the many workforce and occurs human errors. To solve these problems, this paper covers a system that reports on inspection information is automatically generation by input data based on shipbuilding-specific CAD. The developed system analyzes the shape data from drawings of modeling. Also, the system determines welding joints through expansion of the part boundary and generates tag numbers. In addition, it provides the function to automatically extract the information needed for inspection such as weld length, thickness and etc. We conducted test to verify the usefulness of the developed system and confirmed that the welding inspection information extracted through system matches the information shown in drawings of modeling.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.281-284
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• 2006

After building a ship in a shipyard, there are so many repeated inspection of welding seam defects and painting status before delivering to the ship's owner. An inspection on the bottom part of a ship in commercial service should be done in every two years for the purpose of safety and for the prevention of ship speed deterioration. conventional welding seam inspection systems are rely on the visual inspection by human or the ultrasonic inspection for the selective part of a ship. This paper suggests a remote controlled inspection system for the examination of large ships or steel structures. The proposed system moves in contact with the ship under inspection and have a CCD camera to provide visual-guidance information to a remotely located human worker. Additionally this system utilizes a weld line tracking algorithm for an optimal position control. We verified the effectiveness of the inspection system by experimental data.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.105-111
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• 2016

In order to inspect the quality of spot welding, inefficient destructive test and NDT (non destructive testing) utilizing expensive foreign ultrasonic inspection are being conducted in the automobile production lines, but NDT will be difficult to be used in the domestic automobile production due to complexity of the waveform analysis and lack of mobility. In this paper, NDT system inspecting the quality of spot welding based on mobile network is proposed to complement drawbacks of the conventional inefficient destructive testing and NDT inspecting the quality of spot welding. Regardless of daily condition of NDT tester, the proposed NDT system can determine the quality of spot welding automatically and transmit the information of NDT quality to smart devices of field workers in real-time so that convenience of NDT and productivity of automobile production will be improved. Several specimens with a variety of welding quality was produced to evaluate the performance of the proposed mobile ultrasonic NDT system and the conventional foreign equipment, through this experiments, the proposed mobile ultrasonic NDT system indicate the superior properties compared to the conventional equipment in terms of convenience, productivity, and economic.

• Journal of the Society of Disaster Information
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• v.4 no.1
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• pp.102-136
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• 2008

Coated welding points are eliminated for Open-Inspection of above-ground storage tanks which should be inspected periodically in accordance with the Hazmat-treatment Safety Act. But it was speculated on this study that it causes such huge economical losses in terms of manpower and materials that applicable to magnetic particle testing. As a result, it was concluded that the magnetic particle testing is applicable to the coating depth below $50{\mu}m$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.193-201
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• 2021

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.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of Welding and Joining
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• v.22 no.5
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• pp.32-37
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• 2004

Application of bonding by adhesives can be found in many industries, particularly in advanced technological domains such as aeronautical and space, automobile and electronics industries. Periodic inspection with conventional ultrasonic NDE techniques is capable of indicating the presence and possible location of crack. Continuous ultrasonic attenuation monitoring has potential to supply information. This article discusses the use of pulse-echo ultrasonic testing for the inspection of adhesive bonds between metal sheets. The method is based on the measurement of the reflection coefficient at the metal/adhesive interface. By means of a control experiment it is shown that Quantitative Nondestructive Evaluation in Adhesive Joints are evaluated together with Ultrasonic Testing and Fracture Testing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.520-523
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• 2008

As a result of the recent development of the electro-information industry, the hardware of an automated ultrasonic testing system is getting lighter and diversified image processing techniques are applied to its software so that the possible precise totaling and detecting of the flaws are studied. This study proposes an automated ultrasonic testing system of the pipe in order to organize the optimized system, and also describes the data flow and general composition of the software for the design of the system.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.107-116
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• 2024

Copper Filter Dryer (CFD) are responsible for removing impurities from the circulation of refrigerant in refrigeration and cooling systems to maintain clean refrigerant, and defects in CFD can lead to product defects such as leakage and reduced lifespan in refrigeration and cooling systems, making quality assurance essential. In the quality inspection stage, human inspection and defect judgment methods are traditionally used, but these methods are subjective and inaccurate. In this paper, YOLOv7 object detection algorithm was used to detect defects occurring during the CFD Shaft pipe and welding process to replace the existing quality inspection, and the detection performance of F1-Score 0.954 and 0.895 was confirmed. In addition, the cause of defects occurring during the welding process was analyzed by analyzing the sensor data corresponding to the Timestamp of the defect image. This paper proposes a method for manufacturing quality assurance and improvement by detecting defects that occur during CFD process and analyzing their causes.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.6
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• pp.590-596
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• 2004

Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented.