• Proceedings of the KWS Conference
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• 2002.10a
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• pp.595-599
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• 2002

Edge welding of thin sheets is very difficult because of the fit-up problem and small weld area In laser welding, joint fit-up and penetration are critical for sound weld quality, which can be monitored by appropriate methods. Among the various monitoring systems, visual monitoring method is attractive because various kinds of weld pool information can be extracted directly. In this study, a vision sensor was adopted for the weld pool monitoring in pulsed Nd:YAG laser edge welding to monitor whether the penetration is enough and the joint fit-up is within the requirement. Pulsed Nd:YAG laser provides a series of periodic laser pulses, while the shape and brightness of the weld pool change temporally even in one pulse duration. The shutter-triggered and non-interlaced CCD camera was used to acquire a temporally changed weld pool image at the moment representing the weld status well. The information for quality monitoring can be extracted from the monitored weld pool image by an image processing algorithm. Weld pool image contains not only the information about the joint fit-up, but the penetration. The information about the joint fit-up can be extracted from the weld pool shape, and that about a penetration from the brightness. Weld pool parameters that represent the characteristics of the weld pool were selected based on the geometrical appearance and brightness profile. In order to achieve accurate prediction of the weld penetration, which is nonlinear model, neural network with the selected weld pool parameters was applied.

• Journal of Mechanical Science and Technology
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• v.19 no.10
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• pp.1856-1863
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• 2005

In this study, a visual sensor system for weld seam tracking the I-butt weld joints in GMA welding was constructed. The sensor system consists of a CCD camera, a diode laser with a cylindrical lens and a band-pass-filter to overcome the degrading of image due to spatters and arc light. In order to obtain the enhanced image, quantitative relationship between laser intensity and iris opening was investigated. Throughout the repeated experiments, the shutter speed was set at 1/1000 second for minimizing the effect of spatters on the image, and therefore the image without the spatter traces could be obtained. Region of interest was defined from the entire image and gray level of the searched laser stripe was compared to that of weld line. The differences between these gray levels lead to spot the position of weld joint using central difference method. The results showed that, as long as weld line is within $\pm15^{o}$ from the longitudinal straight line, the system constructed in this study could track the weld line successfully. Since the processing time is no longer than 0.05 sec, it is expected that the developed method could be adopted to high speed welding such as laser welding.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.307-313
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• 2002

This paper describes a weld pool monitoring technique, which is based on the weld pool image analysis. The proposed image analysis algorithm uses machine vision techniques to extract geometrical information from the weld pool image such as maximum weld pool width, gap width and misalignment between the joint longitudinal axis and the welding wire. These can be related to the welding parameters (welding voltage and current, wire feed speed and standoff) to produce control actions necessary to ensure that the required weld quality will be achieved. The experiments have shown that the algorithm is able to produce good estimates of the weld pool geometry; however, the adjustment of the camera parameters affects the image quality and, consequently, has a great influence over the estimation.

• Journal of Welding and Joining
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• v.18 no.3
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• pp.60-67
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• 2000

In this study, we constructed a preview-sensing visual sensor system for weld seam tracking in GMA welding. The visual sensor consists of a CCD camera, a diode laser system with a cylindrical lens and a band-pass-filter to overcome the degrading of image due to spatters and/or arc light. To obtain weld joint position and edge points accurately from the captured image, we compared Hough transform method with central difference method. As a result, we present Hough transform method can more accurately extract the points and it can be applied to real time weld seam tracking. Image processing is carried out to extract straight lines that express laser stripe. After extracting the lines, weld joint position and edge points is determined by intersecting points of the lines. Although a spatter trace is in the image, it is possible to recognize the position of weld joint. Weld seam tracking was precisely implemented with adopting Hough transform method, and it is possible to track the weld seam in the case of offset angle is in the region of $\pm15^{\circ}$.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.499-503
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• 1994

The feedback control systems of welding process using visual information can improve weld qualities. However, it is very difficult to get the visual information of weld pool since welding are is much stronger than light from weld pool. To explore the possibility of extending the capability of automatic welding machines a study of a closed loop controlled welding system consisted of a GTA welding machine, a vision system, a stepping motor system and a digital computer was undertaken. Particularly, in this system, a CCD camera with 850nm long pass filter was focused on the weld pool to give a weld pool image. Subsequently, image analysis technique has been developed to measure a weld pool width. Using this weld pool width measurement, a colsed loop control system adjusted welding speed to maintain constant weld pool width.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.23-29
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• 2001

In this study, a preview-sensing visual sensor system is constructed far weld seam tracking in GMA welding. The visual sensor system consists of a CCD camera, a diode laser system with a cylindrical lens, and a band-pass-filter to overcome the degrading of image due to spatters and/or arc light. Among the image processing methods, Hough transform method is compared with the central difference method from a viewpoint of the capability for extracting the accurate feature position. As a result, it was revealed that Hough transform method can more accurately extract the feature positions and it can be applied to real time weld seam tracking. Image processing which includes Hough transform method is carried out to extract straight lines that express laser stripe. After extracting the lines, weld joint position and edge points are determined by intersecting the lines. Even though the image includes a spatter trace on it, it is possible to recognize the position of weld joint. Weld seam tracking was precisely implemented with adopting Hough transform method, and it is possible to track the weld seam in the case of offset angle is in the region of $\pm$ $15^{\circ}$.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.9
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• pp.179-185
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• 2001

In this study, a visual sensor system for weld seam tracking the I-butt weld joints in GMA welding was constructed. The sensor system consists of a CCD camera, a diode laser with a cylindrical lens and a band-pass-filter to overcome the degrading of image due to spatters and arc light. In order to obtain the enhanced image, quantitative relationship between laser intensity and iris number was investigated. Throughout the repeated experiments, the shutter speed was set at 1-milisecond for minimizing the effect of spatters on the image, and therefore most of the spatter trace in the image have been found to be reduced. Region of interest was defined from the entire image and gray level of searched laser line was compared to that of weld line. The differences between these gray levels lead to spot the position of weld joint using central difference method. The results showed that, as long as weld line was within $^\pm$15$^\circ$from the longitudinal straight fine, the system constructed in this study could track the weld line successful1y. Since the processing time reduced to 0.05 sec, it is expected that the developed method could be adopted to high speed welding such as laser welding.

• Journal of Korean Society of Steel Construction
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• v.11 no.1 s.38
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• pp.1-11
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• 1999

The aim of this study is to construct a machine vision monitoring system for an automatic visual inspection of weld joint in steel structure. An image processing algorithm for a visual weld defects detection on weld bead is developed using the intensity image. An optic system for getting four intensity images was set as a fixed camera position and four different illumination directions. The input images were thresholded and segmented after a suitable preprocessing and the features of each region were defined and calculated. The features were used in the detection and the classification of the visual weld defects. It is confirmed that the developed algorithm can detect weld defects that could not be detected by previously developed techniques. The recognized results were evaluated and compared to expert inspectors' results.

• Journal of Welding and Joining
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• v.16 no.6
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• pp.68-76
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• 1998

A CCD camera with a laser stripe was applied to realized the automatic weld seam tracking. The 3-dimensional information obtained from the vision system made it possible to generate the weld torch path. The adaptive Hough transformation was used to extract laser stripes an to obtain specific weld points. It takes relatively long time to process image on-line control using the basic control using the basic Hough transformation, but it has a tendency of robustness over the noises such as spatter. For this reason, it was complemented with adaptive Hough transformation to have an on-line processing ability for scanning specific weld points. The dead zone, where the sensing of weld line is impossible, was eliminated by rotating the camera with its rotating axis centered at the weld torch. When weld lines were detected, the camera angle was controlled in order to get the minimum image data for sensing of weld lines. Consequently, the image processing time was reduced.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1271-1278
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• 2018

The NDT using ultrasonic is largely divided into A-Scan and C-Scan methods. Since A-Scan method is subject to subjective judgement by trained personnel, C-Scan method has been introduced, which presents the weld area in two dimensions by placing the transducers two dimensionally used in the A-Scan method. Therefore, it is necessary to develop equipment that can provide weld quality without the help of a welding expert and the presentation of effective C-Scan images. Thus, in this paper, the algorithms that express a low resolution 2-dimensional gray image formed by C-Scan method as a high-resolution color C-Scan image and automatically determine the weld quality from the generated C-Scan color image. The high resolution color C-Scan images proposed in this paper allow the exact shape of the weld point to be expressed, and an objective algorithm to use this image to automatically determine weld quality.