• Journal of the Korean Society for Precision Engineering
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• v.18 no.8
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• pp.71-78
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• 2001

In GMA(Gas Metal Arc)Welding, the weld size that is a locally melted area of a workpiece is one of the most important considerations in determining the strength of a welded structure. Variations in the weld power and the welding heat flux may affect the weld pool formation and ultimately the size of the weld. Therefore, an accurate prediction of the weld size requires a precise analysis of the weld thermal cycle. In this study, a model which can estimate the weld bead geometry and a method for thermal analysis, including the model, are suggested. In order to analyze the weld bead geometry, a mathematical model was developed with transformed coordinates to apply to the horizontal fillet joints. A heat flow analysis was performed with a two dimensional finite element model that was adopted for computing the base metal melting zone. The reliability of the proposed model and the thermal analysis was evaluated through experiments, and the results showed that the proposed model was very effective for predicting the weld bead shape and good correspondence in melting zone of the base metal.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.151-155
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• 2002

In GMA(Gas Metal Arc)Welding, the weld size that is a locally melted area of a workpiece is one of the most important considerations in determining the strength of a welded structure. Variations in the weld power and the welding heat flux may affect the weld pool formation and ultimately the size of the weld. Therefore, an accurate prediction of the weld size requires a precise analysis of the weld thermal cycle. In this study, a model which can estimate the weld bead geometry and a method for thermal analysis, including the model, are suggested. In order to analyze the weld bead geometry, a mathematical model was developed with transformed coordinates to apply to the horizontal fillet joints. A heat flow analysis was performed with a two dimensional finite element model that was adopted for computing the base metal melting zone. The reliability of the proposed model and the thermal analysis was evaluated through experiments, and the results showed that the proposed model was very effective for predicting the weld bead shape and good correspondence in melting zone of the base metal.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.539-544
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• 1998

Generally, welding gap is a serious factor of a falling-off in weld quality among various kind of weld defect. Welding gap is created between two work piece in GMAW(Gas Metal Arc Welding) of horizontal fillet weld because surface of workpiece is not flat by cutting process. In these days, there were many attempts to detect welding gap. though we prevalently use the vision sensor or arc sensor in welding process, it is difficult to detect welding gap for improvement of welding quality. But we have a trouble to find relationship between welding gap and many welding parameters due to non-linearity of welding process. As mentioned about the various difficult problem, we can detect welding gap precisely using neural networks which are able to model non-linear function. Also, this paper was proposed real-time monitoring of weld bead shape to find effect of welding gap and to estimate weld quality. Monitoring of weld bead shape examined the correlation of welding parameters with bead eometry using learning ability of neural networks. Finally, the developed system, welding gap detecting system and bead shape monitoring system, is expected to the successful capability of automation of welding process by result of simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1379-1386
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• 2005

For efficient welding it is necessary to maintain stability of the welding process and control the shape of the welding bead. The welding quality can be controlled by monitoring important parameters, such as, the Arc Voltage, Welding Current and Welding Speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using fuzzy logic and neural networks for improving welding qualify and maintaining the desired weld bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and remove the welding detects.

• International Journal of Korean Welding Society
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• v.4 no.1
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• pp.30-37
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• 2004

There have been continuous efforts on automating welding processes. This automation process could be said to fall into two categories, weld seam tracking and weld quality evaluation. Recently, the attempts to achieve these two functions simultaneously are on the increase. For the study presented in this paper, a vision sensor is made, a vision system is constructed and using this, the 3 dimensional geometry of the bead is measured on-line. For the application as in welding, which is the characteristic of nonlinear process, a fuzzy controller is designed. And with this, an adaptive control system is proposed which acquires the bead height and the coordinates of the point on the bead along the horizontal fillet joint, performs seam tracking with those data, and also at the same time, controls the bead geometry to a uniform shape. A communication system, which enables the communication with the industrial robot, is designed to control the bead geometry and to track the weld seam. Experiments are made with varied offset angles from the pre-taught weld path, and they showed the adaptive system works favorable results.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.792-795
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• 1996

In TIG welding of pipe, back bead size monitoring is important for weld quality assurance. Many researches have been performed on estimation of the back bead size by heat conduction analysis. However numerical conduction model based on many uncertain thermal parameters causes remarkable errors and thermomechanical phenomena in molten pool can not be considered. In this paper, filler wire feeding force in addition to weld current, wire feedrate, torch travel speed and orbital position angle is monitored to estimate back bead size in orbital TIG welding. Monitored welding process variables are fed into an artificial neural network estimator which has been trained with the monitored process variables (input patterns) and actual back bead size (output patterns). Experimental verification of the proposed estimation method was performed. The predicted results are in a good agreement with the actual back bead shape. The results are quite promising in that estimation of invisible back bead shape can be achieved by analyzing the welding parameters without any conventional NDT of welds.

• Journal of Welding and Joining
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• v.17 no.3
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• pp.26-35
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• 1999

On the laser weld production line, a slight alteration of the welding condition changes the bead size and the strength of the weldment. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in $CO_2$ laser welding. The relationship between the sensor signals of plasma or spatter and the bead shape, and the mechanism of the plasma and spatter were analyzed for the bead size estimation. The penetration depth and the bead width were estimated using the multiple regression analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1326-1331
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• 2006

Weld root gap is a important fact of a falling-off weld quality in various kind of weld defect. The welding quality can be controlled by monitoring important parameters, such as, the Arc voltage, welding current and welding speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using neural networks for detecting and monitoring of weld root gap and bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and detect the welding defects.

• Laser Solutions
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• v.11 no.1
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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 Ocean Engineering and Technology
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• v.24 no.4
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• pp.38-46
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• 2010

The wall thinning phenomenon of pipes was simulated as metal loss due to erosion and corrosion. Such wall thinning defects in the pipes of power plants are a very important safety consideration. In this study, we analyzed wall thinning defects that occurred by weld bead of weld zone of boost pump recirculation pipe. From the results of the analysis of pipe failures, numerical analysis was performed by Fluent v6.3.26 using the standard k-$\varepsilon$ model of the weld bead shape, such as an elliptical or a spherical shape, on the inner wall of the pipe. Using the results obtained, we showed the overlap effect by cavitations corrosion and erosion-corrosion at the bottom of the wall-thinning defect.