• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.75-80
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• 2008

This paper presents a vision based autonomous inspection system for welding quality control of a RV sinking seat. In order to overcome the precision error that arises from a visible inspection by an operator in the manufacturing process of a RV sinking seat, the machine vision based welding quality control system is proposed. It consists of the CMOS camera and the NI vision system. The geometry of the welding bead, which is the welding quality criteria, is measured by using the captured image with a median filter applied on it. The image processing software for the system was developed using the NI LabVIEW software. The proposed welding quality inspection system for RV sinking seat was verified using experimentation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.3
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• pp.71-77
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• 2008

This paper presents a stereo vision based autonomous inspection system for welding quality control of a RV(Recreational Vehicle) sinking seat. The three dimensional geometry of the welding bead, which is the welding quality criteria, is measured by using the captured stereo images with a median filter applied on it. The image processing software for the system was developed using the NI LabVTEW software with NI vision system. In the manufacturing process of a RV sinking seat, the developed system can be used for overcoming the precision error that arises from a visible inspection by an operator. The welding quality inspection system for RV sinking seat was verified using experimentation.

• Journal of Welding and Joining
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• v.19 no.1
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• pp.88-94
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• 2001

DUPIC (Direct use of spent PWR fuel in CANDU reactors) nuclear fuel is a CANDU fuel fabricated remotely from spent PWR fuel materials in a hot cell. The soundness of the end closure welds of nuclear fuel elements is an important factor for the safety and performance of nuclear fuel. To evaluate the soundness of the end closure welds of DUPIC fuel element, a precise X-ray inspection system is developed using a micro-focus X-ray generator with an image intensifier and a real time camera system. The fuel elements made of Zircaloy-4 and stainless steel by an Nd:YAG laser welding and a TIG welding aye inspected by the developed inspection system. The soundness of the welds of the fuel elements was confirmed by the X-ray inspection process, and the irradiation test of DUPIC fuel elements has been successfully completed at the HANARO research reactor.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1553-1558
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• 2007

This paper presents a vision based autonomous inspection system for welding quality control of car sinking seat. In order to overcome the precision error that arises from a visible inspection by operator in the manufacturing process of a car sinking seat, this paper proposes the MVWQC (machine vision based welding quality control) system. This system consists of the CMOS camera and NI machine vision system. The image processing software for the system has been developed using the NI vision builder system. The geometry of welding bead, which is the welding quality criteria, is measured by using the captured image with median filter applied on it. Experiments have been performed to verify the proposed MVWQC of car sinking seat.

• 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.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.371-376
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• 2019

We have developed an acoustic resonance inspection system to inspect the welding defects in the mechanical parts fabricated using friction stir welding method. The inspection system was consisted of a DAQ board, a microphone sensor, an impact hammer, and controlled by a PC software. The system was developed to collect and analyze the sound signal generated by hitting the sample with an impact hammer to determine whether it is defective. In this study, 100% welded good samples were compared with 95%, 90%, and 85% welded samples, respectively. The variation of the completeness in welding did not affect the visual appearance in the samples. As a result of analyzing the natural frequencies of the good samples, the five natural frequency peaks were identified. In the case of the defective samples, the frequency change was observed. The welding failure detection time was fast enough to be only 0.7 seconds. Employing our welding defect inspection system to the actual industrial field will maximize the efficiency of quality inspection and thus improve the productivity.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.5
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• pp.135-143
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• 2000

The vision system is easy to use the exclusive use as a independent computer system but it is not much popularization by reason of expensive and difficult to adapt to the various fields, because it is easy to the existed computer system, the price is cheap and also it can use to the various measurement purpose which user wanted and programed. The measurement method of the vehicle axle casing nut welding part is that measure the value of the welding part to adapt the actual operation program from using the ratio between the actual length of the standard specimen and the length of image, to measure the ratio between the actual product and the camera image. A defect is found by the assembled visual inspection system. The inspection algorithm which estimates the quality of welded product is developed and also, the software program which processes the automatic test function of the inspection system. We make the foundation of the inspection automatic system and we will help to apply other welding machine.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.85-94
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• 1998

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding system, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.37-42
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• 2007

The purpose of this research is stability estimation of plant structure through classification and recognition about welding flaw in SWP(Spiral Welding Pipe). And, In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up 2-axes inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to image processing and probabilistic neural network(PNN) classifying code by MATLAB programming. Through this process, we proved efficiency on the system of SWP stability Estimation.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.406-408
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• 1997

This paper presents the new method of a nondestructive spot welding state inspection system using neural networks. The learning process of neural networks makes the inspection system to adapt the variable welding parameters. The inspecting process is working with on-line real-time after off-line learning process. This neural network based inspection system shows reliable results through the field test for variations of applied voltages, currents, and contact area of the welding electrode.