• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.3
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• pp.81-87
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• 2007

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. However, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis and artificial neural network were used as the research methods. And, the results of two prediction methods were compared and analyzed.

• Journal of Welding and Joining
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• v.27 no.1
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• pp.71-78
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• 2009

In the circumferential welding of pipe, welding phenomenon changes with the position of pipe. Especially in the overhead position, back bead of vertical-up position would be sunk. To investigate the size of back bead and keyhole with the change of the flow rate of pilot and shield gas at each position, bead-on plate welds were conducted on 6mm thickness SS400 with inclined-up position. When the rest of welding conditions remained constant, the width of back bead was increased as the flow rate of pilot gas was increased. And back bead tended to convex as the flow rate of shield gas was increased.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.4
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• pp.321-327
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• 2017

In this study, to investigate the shape of the back bead as a weld quality parameter and to select the optimal condition of the root-pass TIG welding of a horizontal butt-joint, an experimental design and the response surface method (RSM) have been employed. Three parameters are used as input variables, which include the base current, peak current, and welding speed. The back bead width is selected as an output variable representing the weld quality, the target value of the width is 5.4 mm. Conducting the experiments according to the Box-Behnken experimental design, a $2^{nd}$ regression model for the back bead width was made, and the validation of the model was confirmed by using the F-test. The desirability function was designed through the nominal-the-best formula for the appropriate back bead width. Finally, the following optimal condition for welding was selected using the RSM: base current of 0.9204, peak current of 0.8676, and welding speed of 0.3776 in coded values. For verification, a test welding process under the optimal condition was executed and the result showed the back bead width of 5.38 mm that matched the target value well.

• 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].

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.329-334
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• 1987

The purpose of this study is to monitor and control the back bead width in arc welding with consumable electrode for reduction of the occurrence of weld defect. The temperature of a point on the weldment surface is selected, as a monitoring parameter, and measured by an optical infra-red sensor. The correlation between the back bead width and the surface temperature is experimentally obtained for various thicknesses of the weldment. The welding travel speed and the surface temperature are taken, respectively, as an input and an output of the welding process under the stable condition of arc. A PI control scheme to maintain the surface temperature at the desired level is proposed by the experimental study.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.206.1-206.1
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• 2005