• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2000년도 특별강연 및 춘계학술발표대회 개요집
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• pp.303-306
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• 2000

Quality estimation of the weld has been one of the important issues in RSW which is a main process of the sheep metal fabrication in auto-body industry, It was well known that among the various welding process variables, dynamic resistance has a close relation with nugget formation. With this variable, it is possible to estimate the weld quality in real time. In this study, a new quality estimation algorithm is developed with the primary dynamic resistance measured at welding machine timer. For this, feature recognition method of Hopfield neural network is used. Primary resistance patterns are vectorized and classified with five patterns. The network trained by these patterns recognizes the dynamic resistance pattern and estimates the weld quality Because the process variable monitored at the primary circuit is used, it is possible to apply this system to real time application without any consideration of electrode wear or shunt effect.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.791-796
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• 2001

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time evaluation of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensor. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, focus off, and nozzle change. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding. Weld quality prediction program was developed using previous weld results and statistical program which could show the trend of weld quality and signal was developed.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.345-348
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• 2008

최근 용접공정은 무인화 및 자동화 시스템의 구축이 급속하게 발전하고 있으며 정확한 용접현상의 해석을 위하여 여러 가지 신호처리 알고리즘을 적용하고 있다. 본 논문에서는 아크용접의 모니터링시스템의 구성에 있어서 용접 품질을 실시간으로 판단할 수 있는 효율적인 신경회로망을 제시하고, 학습 데이터의 선정을 위한 전처리 과정을 제시하며, 학습된 신경회로망을 이용하여 실제 용접이 이루어지는 파형에 대한 평가를 보다 정밀하고 정확하게 평가 할 수 있는 방법을 제시한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.643-646
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• 2000

In this study, we constructed a preview-sensing visual sensor system for weld seam tracking in real time in GMA welding. A sensor part consists of a CCD camera, a band-pass filter, a diode laser system with a cylindrical lens, and a vision board for inter frame process. We used a commercialized robot system which includes a GMA welding machine. To extract the weld seam we used a inter frame process in vision board from that we could remove the noise due to the spatters and fume in the image. Since the image was very reasonable by using the inter frame process, we could use the simplest way to extract the weld seam from the image, such as first differential and central difference method. Also we used a moving average method to the successive position data of weld seam for reducing the data fluctuation. In experiment the developed robot system with visual sensor could be able to track a most popular weld seam, such as a fillet-joint, a V-groove, and a lap-joint of which weld seam include planar and height directional variation.

• 한국정보통신학회논문지
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• 제13권2호
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• pp.407-412
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• 2009

최근 용접공정은 무인화 및 자동화 시스템의 구축이 급속하게 발전하고 있으며 정확한 용접현상의 해석을 위하여 여러 가지 신호처리 알고리즘을 적용하고 있다. 본 논문에서는 아크용접의 모니터링시스템 구성에 있어서 용접품질을 실시간으로 판단할 수 있는 효율적인 신경회로망을 제시하고, 학습 데이터의 선정을 위한 전처리 과정을 제시하며, 학습된 신경회로망을 이용하여 실제 용접이 이루어지는 파형에 대한 평가를 보다 정밀하고 정확하게 평가할 수 있는 방법을 제시한다.

• 한국정밀공학회지
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• 제18권9호
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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.

• 한국레이저가공학회지
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• 제11권1호
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• pp.32-35
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• 2008

In the remote welding system using $CO_2$ laser, laser beam can be rapidly transferred to a workpiece by moving mirrors of scanner system. So, it makes reducing the cycle time of welding process. We can also use and apply various patterns of weld beads by linear controlled mirrors. But most of the domestic car makers have usually applied use stitch-shaped weld bead. In that case, we don't have the merit of remote welding system efficiently. Therefore, in this paper, we investigated the characteristic of weld strength according to patterns of weld bead on $CO_2$ laser welding. And we also studied the relationship between shape of weld bead and value of tensile load.

• Journal of Welding and Joining
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• 제21권1호
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• pp.60-65
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• 2003

At present. welding of most pipes with large diameter is carried out by the manual process. Automation of the welding process is necessary f3r the sake of consistent weld quality and improvement in productivity. In this study, two vision sensors, based on the optical triangulation, were used to obtain the information for seam tracking and detecting the weld defects. Through utilization of the vision sensors, noises were removed, images and 3D information obtained and positions of the feature points detected. The aforementioned process provided the seam and leg position data, calculated the magnitude of the gap, fillet area and leg length and judged the weld defects by ISO 5817. Noises in the images were removed by using the gradient values of the laser stripe's coordinates and various feature points were detected by using an algorithm based on the iterative polygon approximation method. Since the process time is very important, all the aforementioned processes should be conducted during welding.

• 한국정밀공학회지
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• 제21권12호
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• pp.84-91
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• 2004

A CCD camera with a laser stripe was applied to realize the automatic weld seam tracking in GMAW. It takes relatively long time to process image on-line control using the basic Hough transformation, but it has a tendency of robustness over the noises such as spatter and arc tight. For this reason, it was complemented with adaptive Hough transformation to have an on-line processing ability for scanning specific weld points. The adaptive Hough transformation was used to extract laser stripes and to obtain specific weld points. The 3-dimensional information obtained from the vision system made it possible to generate the weld torch path and to obtain the information such as width and depth of weld line. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.

• 한국레이저가공학회지
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• 제3권2호
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• pp.53-61
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• 2000

On the laser weld production line, a slight alteration of the welding condition produces many defects. The defects are monitored in real time, in order to prevent continuous occurrence of defects, reduce the loss of material, and guarantee good quality. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in CO$_2$ laser welding. For high speed CO$_2$ laser welding, laser tailored welded blanks for example, on-line weld quality monitoring system was developed by using fuzzy multi-feature pattern recognition. Weld qualities were classified optimal heat input, a little low heat input, low heat input, and focus misalignment, and final weld quality were classified good and bad.