• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.79-86
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• 2007

The shape of excessive penetration mainly depends on welding conditions(welding current and welding voltage), and welding process(groove gap and welding speed). These conditions are the major affecting factors to width and height of back bead. In this paper, back-bead prediction and weldability estimation using artificial neural network were investigated. Results are as follows. 1) If groove gap, welding current, welding voltage and welding speed will be previously determined as a welding condition, width and height of back bead can be predicted by artificial neural network system without experimental measurement. 2) From the result applied to three weld quality levels(ISO 5817), both experimented measurement using vision sensor and predicted mean values by artificial neural network showed good agreement. 3) The width and height of back bead are proportional to groove gap, welding current and welding voltage, but welding speed. is not.

• Journal of Welding and Joining
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• v.27 no.1
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• pp.95-101
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• 2009

In case of manufacturing the high quality welds or pipeline, the full penetration weld has to be made along the weld joint. Thus the root pass welding is very important and has to be selected carefully. In this study, an experimental method for the selection of optimal welding condition was proposed in the root pass welding which was done along the V-grooved butt weld joint. This method uses the response surface analysis in which the width and height of back bead were chosen as the quality variables of the weld. The overall desirability function, which is the combined desirability function for the two quality variables, was used as the objective function for getting the optimal welding condition. In the experiments, the target values of the back bead width and the height are 6mm and zero respectively for the V-grooved butt weld joint of 8mm thickness mild steel. The optimal welding conditions could predict the back bead profile(bead width and height) as 6.003mm and -0.003mm. From a series of welding test, it was revealed that a uniform and full penetration weld bead can be obtained by adopting the optimal welding condition which was determined according to the method proposed.

• Journal of Ocean Engineering and Technology
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• v.16 no.2
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• pp.53-59
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• 2002

This paper shows the experimental results controlling the height of surface and back bead in GMAW by analyzing the unexpected gaps between base metals produced in welding and by controlling welding velocity due to the variation of the gap between base metals in thin-plate welding. The back bead behavior and burn-through in I-type butt joint $CO_2$ welding of thin mild steel are analyzed in the views of short circuit time ratio and short circuit frequency. It is shown through experimental consideration that the short circuit time ratio method is more reasonable than the short circuit frequency method in analyzing the formulation of back bead under changing the gap between base metals. Based on the these results, welding manipulator is designed so as to satisfy the bead height control in real time by measuring the short circuit time ratio. To show the effectiveness of the developed bead formulation control system, the experiment is implemented under two welding conditions such as increasing gap from 0mm to 0.8mm and gradually increasing gap from 0mm to 1.2mm. The experimental results show that the bead formulation can be controlled uniformly in spite of the variation of the gap between base metals.

• Journal of Welding and Joining
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• v.28 no.5
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• pp.86-92
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• 2010

In GMAW processes, bead geometry is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage, welding speed, shielding gas and so on. Thus the welding condition has to be selected carefully. In this paper, an experimental method for the selection of optimal welding condition was proposed in the root pass welding which was done along the GMA V-grooved butt weld joint. This method uses the response surface analysis in which the width and height of back bead were chosen as the quality variables of the weld. The overall desirability function, which is the combined desirability function for the two quality variables, was used as the objective function for getting the optimal welding condition. Through the experiments, the target values of the back bead width and the height were chosen as 4mm and 1mm respectively for the V-grooved butt weld joint. From a series of welding test, it was revealed that a uniform weld bead can be obtained by adopting the optimal welding condition which was determined according to the method proposed.

• Proceedings of the KWS Conference
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• 2002.10a
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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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• v.28 no.6
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• pp.21-27
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• 2010

Due to excellent weld quality, orbital welding with TIG is widely applied to pipe welding. But concave back bead is formed easily in overhead and inclined-up position of butt orbital welding. It is difficult to find a paper to overcome this problem. In this study, in order to make convex back bead in overhead and inclined-up position of pipe 5G welding, control method of molten pool was actively investigated. Melt run welds were conducted on thickness 4.0mm SS400 with overhead and inclined-up position and was observed the variation of bead shape after welding with the bead former developed. The height of back bead showed the trend of increase as the distance from molten pool to the bead former was decreased. Also, there is no trend in the bead width of front and back as welding position was changed or the distance from molten pool to the bead former was decreased.

• International Journal of Korean Welding Society
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• v.3 no.2
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1845-1848
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• 2005

The automatic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.208-213
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• 2001

Objective of this paper is to develop a new approach involving the use of an Artificial Neural Network(ANN) and multiple regression methods in the prediction of process parameters on bead height for GMA welding process. Using a series of robotic are welding, multi-pass butt welds carried out in order to verify the performance of the neural network estimator and multiple regression methods. To verify the developed system, the design parameters of the neural network estimator are selected from an estimation error analysis. The experimental results show that the proposed models can predict the bead height with reasonable accuracy and guarantee the uniform weld quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.596-599
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• 2004

The robotic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. To achieve this above objective, Taguchi method was employed using five different process parameters (tip gap, gas flow rate, welding speed, arc current, welding voltage) as a guide for optimization of process parameters.