• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2006년 추계학술발표대회 개요집
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• pp.271-273
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• 2006

This paper focuses on the developed empirical models for the prediction on top-bead width in GMA(Gas Metal Arc) welding process. Three empirical models have been developed: linear, curvilinear and an intelligent model. Regression analysis was employed fur optimization of the coefficients of linear and curvilinear model, while Genetic Algorithm(GA) was utilized to estimate the coefficients of intelligent model. Not only the fitting of these models were checked, but also the prediction on top-bead width was carried out. ANOVA analysis and contour plots were respectively employed to represent main and interaction effects between process parameters on top-bead width.

• Journal of Welding and Joining
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• 제17권6호
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• pp.46-52
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• 1999

The auxiliary gas-shielded MAG (AMAG) process, which was devised to provide an argon-rich shielding environment using small amount of argon gas, was investigated experimentally to figure out its effects on metal transfer and weld quality. Proper conditions for the AMAG process including the argon gas ratio, position and direction of the auxiliary nozzle were determined experimentally. Performance of the AMAG process was compared with that of the double gas-shielded MAG(DMAG) and MAG processes by monitoring the bead profile, current and voltage waveforms. The AMAG process was found to provide better bead profile, more stable arc and wider operating range of spray transfer mode compared with the DMAG process. In general, performance of the AMAG process using the argon ratio of 30% was comparable to that of the MAG process using 80% argon and 20% CO₂ gas.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2002년도 Proceedings of the International Welding/Joining Conference-Korea
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• pp.301-306
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• 2002

This paper presents the use of the neural network technology to establish a mathematical model for predicting bead geometry (top-bead width, top-bead height, back-bead width and back-bead height) for multi-pass welding, and understand relationships between process parameters and bead geometry for robotic GMA welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the developed neural network model. The results show that not only the proposed model can predict the bead geometry with reasonable accuracy and guarantee the uniform weld quality, but also the neural network model could be better than the linear and curvilin ear equations developed from Lee [8].

• Journal of Welding and Joining
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• 제30권6호
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• pp.42-48
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• 2012

Due to increase in demand of stable and long pipelines in natural gas industry, wide range of researches are being performed on automation welding to improved welding quality with respect to weld process parameters in real time measurement. In particular, the coupling between the pipe manufacturing process and location of the weld seam, the measured size of the gap that exists in the weld position and the weld angle depending on whether the movement of molten weld. This is due to absence of controlling welding penetration position, depending on the required size of the angle of the setting. In addition, the optimum welding conditions must be considered while selecting, the correlation between these variables and the systematic correlation has not yet been identified. Therefore, in most welded pipe root-pass weld solely depends on the experience of workers in relation to secure a stable weld quality. In this study, automation welding system is implemented to select a suitable root-pass STT (Surface Tension Transfer) welding method using the optimal welding conditions. To successfully accomplish this objective, there were various welding conditions used for welding experiment to confirm that the assessment required for construction through the pipe and automatic welding process is proposed to optimize this plan.

• Journal of Welding and Joining
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• 제8권1호
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• pp.23-30
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• 1990

The robotic welding has been adapted positively in many welding shops forthe purpose of improving the welding efficiency and liberating operators from the severe working atmosphere. But for a large-size structure with thick plates like ship-building and every kind of plants manufacturing, the application of the arc welding robots is not established yet. The reason is assumed that the conventional arc welding robots are not adaptive for multi-pass welding of thick plates whose grooves are not so accurate. As one solution to this problem, a guidance system which uses the welding arc itself as a sensor is largely used. In this study the velocity controller which changes the tip to workpiece distance for regulating the weld proposed. The proportional and integral gain of velocity controller were determined by using the computer simulation of the control system, and the simulation results compared with the experimental ones. It was revealed that the developed control system using the arc sensor principle has a good capability of tracking the weld joint, although some more studies will be needed to refine the model of arc current.

• Journal of Welding and Joining
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• 제15권6호
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• pp.116-123
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• 1997

In GMA welding processes, bead contour and penetration patterns are criterion to estimate weld quality. Bead geometry is commonly defined with width, height and depth. When weaving is taken into account, selection of welding conditions is known to be difficult. Thus, empirical or trial-and-error method are usually introduced. This study examined the correlation of welding process variables including weaving parameters with bead geometry using srtificial neural networks(ANN). The main task of the Ann estimator is to realize the mapping characteristics from the sampled welding process variables to the actual bead geometry through training. After the neural network model is constructed, welding process variables for desired bead geometry is selected by inverse model. Experimental varification of the inverse model is conducted through actual welding.

• International Journal of Korean Welding Society
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• 제1권2호
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• pp.7-11
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• 2001

Almost every die for automobiles must be corrected or remodeled for minor geometrical changes or fur better hardness characteristics by an arc welding process. Although many other kinds of arc welding processes have been automated with robots, the molten metal deposition process for die remodeling still depends entirely on experienced welders. In this study, a database for bead shapes with respect to welding parameters is constructed through experiments to automate molten metal deposition by the arc welding process. Changes in the welding parameters for inclined base metal are studied to consider the effect of die geometries on the welding process.

• Journal of Welding and Joining
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• 제25권6호
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• pp.4-10
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• 2007

• Journal of Welding and Joining
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• 제21권3호
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• pp.46-50
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• 2003

The horizontal fillet joint is one of the most important weld joints in the shipbuilding industry. High-speed rotating arc welding, which can increase the leg length, is an effective way to improve the weld productivity and quality for the horizontal fillet welding. Based on the Taguchi method, the effects of welding parameters on bead characteristics - leg length, asymmetry, undercut, overlap - are investigated fur high-speed welding process. As a result, the adequate welding parameters are selected for the required leg length, symmetric bead and no undercut. Besides, considerably consistent leg length is observed for the horizontal fillet welding with gap variation up to 3mm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.608-611
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• 2002

In GMAW(Gas Metal Arc Welding) process, bead geometry (penetration, bead width and height) is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage and travel speed, shielding gas, CTWB (contact- tip to workpiece distance) and so on. In this paper, welding process variables were selected as welding current, arc voltage and travel speed. And bead geometry was reasoned from the chosen welding process variables using negro-fuzzy algorithm. Neural networks was applied to design FL(fuzzy logic). The parameters of input membership functions and those of consequence functions in FL were tuned through the method of learning by backpropagation algorithm. Bead geometry could be reasoned from welding current, arc voltage, travel speed on FL using the results learned by neural networks.