• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.338-344
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• 2010

Both ends of welding line are often closed by wall in the welding of ship blocks. In this research, seam tracking sensing system for butt welding in the condition with wall was developed. Seam tracking sensing system measures position of carriage from wall and detects root-pass of welding line. The system consists of the laser displacement sensors and ultrasonic sensors. The laser displacement sensor reciprocal1y rotates by the motor and measures a distance from laser sensor to the welding material. The ultrasonic sensor measures a distance between welding system and walls. The distance measured by the ultrasonic sensor is used to get X(driving) position and to determine initial and end point of the weld line. Y(weaving) and Z(height) of the weld line are obtained by the distance measured by the laser displacement sensor and the orientation of the sensor. The sensing system includes the controller that is independent from the welding carriage. The seam tracking sensing system is attached to both side of welding carriage so that interference between welding torch and sensing system can be avoided during the welding. And both side sensing system minimize dead zone. Finally, developed sensing system was adhered to welding carriage and verified usefulness by experiments.

• Journal of the Korean Society of Marine Environment & Safety
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• v.12 no.2 s.25
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• pp.151-155
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• 2006

In these days, welding is the most commonly used metallic connection technology and also is the fundamental production technology of the modem industrial, which is used in various areas of the industrial fields, such as shipbuilding, automobiles, airplanes and plant facilities. However welding process produces strong light, electric currents, and fume gases etc., and the welding automation is not so easy compared to the other works of manufacturing industries which produce the standardized products in large quantities. So it is difficult to weld and detect the all kinds of seams automatically by a specific sensor. In this paper the sensor applying strain gauges is used to detect the seams of triangular wave form. With the auto carriage having the sensor we proposed the experiment to weld and track the seam automatically.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.141-148
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• 2021

In this paper, we propose an algorithm of welding bead detection and evaluation using geodesic active contour algorithm and high pass filter with image processing technique. The algorithm uses histogram equalization and high pass filter as gaussian filter to improve contrast. The image processing techniques smoothens the welding beads reduce the noise on an image. Then, the algorithm detects the welding bead area by applying the geodesic active contour algorithm and morphological ooperation. It also applies the balloon force that either inflates in, or deflates out the evolving contour for a better segmentation. After that, we propose a method for determining the quality of welding bead using effective length and width of the detected bead. In the experiments, our algorithm achieved the highest recall, precision, F-measure and IOU as 0.9894, 0.9668, 0.9780, and 0.8957 respectively. We compared the proposed algorithm with the conventional algorithms to evaluate the performance of the proposed algorithm. The proposed algorithm achieved better performance compared to the conventional ones with a maximum computational time of 0.6 seconds for segmenting and evaluating one welding bead.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.944-951
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• 2009

In this paper, we use image processing algorithms to detect the defects existed on a welding area automatically. It is difficult to detect the welding defects because it is sensitive to lights and has irregular patterns. For this reason, images are captured with 2 kinds of illumination condition, and are processed by 2 different algorithms for each image. The first algorithm separates some ROI's from the captured image and compares the similarity of intensity between each divided region. The second algorithm extracts boundary information from the processed image by the first algorithm, and calculates the length of boundary, curvature and base line area based on boundary information. The proposed method showed high performance in detection and classification of defects.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.10
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• pp.7-13
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• 2006

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.209-215
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• 2020

In this paper, we propose an algorithm for segmenting weld bead images using edge enhancement and active contours. In the proposed method, high-frequency filtering and contrast improvement are performed for edge enhancement, and then, by applying the active contour method, only the weld bead region can be obtained. The proposed algorithm detects an edge through high-frequency filtering and reinforces the detected edge by using contrast enhancement. After the edge information is improved in this way, the weld bead area can be extracted by applying the active contour method. The proposed algorithm shows better performance than the existing methods for segmenting the weld bead in the image. For the objective reliability of the proposed algorithm, it was compared with the existing high pass filtering methods, and it was confirmed that the welding bead segmentation of the proposed method is excellent. The proposed method can be usefully used in evaluating the quality of the weld bead through an additional procedure for the segmented weld bead.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1374-1380
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• 2002

Recently, the use of Robot increase little by little for the purpose of developing a welding quality and productivity in the welding part. It is more important to contact the seam for arc welding before moving a welding robot. There are two types of method to contact the seam namely contact and non-contact type largely. In this paper, image processing sensor(a kind of non-contact sensor) is concerned to track the seam by using laser diode and CCD camera. A structured laser diode's light illuminated on the weld groove and the reflected shape is introduced by CCD camera. The image board captures this image and software analyzes this image. The robot is moved and welded exactly as acquired image X-Y data is changed with robot's X-Y value. Also, most of seam tracking are considered by changing the program simply in case of the different weld groove of plane surface.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.460-464
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• 2001

Recently, the use of Robot increase little by little for the purpose of developing a welding quality and productivity in the arc welding part. It is more important to contact the seam for arc welding before moving a welding robot. There are two types of method to contact the seam namely contact and noncontact type largely In this paper, image processing sensor(a kind of non-contact sensor) is concerned to track the seam by using laser diode and CCD camera. A structured laser diode's light illuminated on the weld groove and the reflected shape is introduced by CCD camera. The image board captures this image and software analyzes this image. The robot is moved and welded exactly as acquired image X-Y data is changed with robot's X-Y value. Also, most of seam tracking are considered by changing the program simply in case of the different weld prove of plane surface.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.103-110
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• 2008

A reliable extraction of welded area is the precedent task before the detection of weld defects in industrial radiography. This paper describes an attempt to detect and extract the welded features of steel tubes from the computed radiography(CR) images. The statistical properties are first analyzed on over 160 sample radiographic images which represent either weld or non-weld area to identify the differences between them. The analysis is then proceeded by pattern classification to determine the clustering parameters. These parameters are the width, the functional match, and continuity. The observed weld image is processed line by line to calculate these parameters for each flexible moving window in line image pixel set. The local statistic line-clustering method is used as the classifier to recognize each window data as weld or non-weld cluster. The sequential procedure is to track the edge lines between two distinct regions by iterative calculation of threshold, and it results in extracting the weld feature. Our methodology is concluded to be effective after experiment with CR weld images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.556-560
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• 1993

최근 모든 산업에 있어서 자동화에 대한 요구가 높아지고 있다. 모든 자동화는 힘들고 어려운 작업을 기계가 인간을 대신하여 수행하며 품질 및 생산성을 향상시킨다는 관점에서 끊임없이 추구되고 있다. 용접 자동화도 그 예외는 아니어서 용접 구조물 제작의 경우, 임의의 용접 경로를 추적하는 방법과 공정 변수의 제어법 등이 현재 연구되고 있다. 특히 용접 공정중 용접선 경로 변화를 측정할 수 있는 센서가 용접 공정의 자동화에 필수적이다. 이러한 센서중에는 아크센서, 시각센서, 초음파센서, 레이저센서가 있으며, 이중 널리 이용되고 있는 것은 팁-모재간의 거리변화에 따른 전류 또는 전압 변화를 모델링하여 위치 정보로 이용하는 아크센서와, 카메라를 이용한 영상 정보를 처리함으로써 토치가 이동해야 항 위치를 찾는 시각센서가 있다.