• Proceedings of the KWS Conference
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• 2009.11a
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• pp.23-23
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• 2009

우수한 용접부 품질 때문에 TIG를 이용한 오비탈 용접은 파이프 용접에 널리 사용되고 있다. 하지만 루트갭이 없고 루트페이스가 큰 맞대기 오비탈 용접의 위보기 및 경사상진자세에서는 오목한 이면비드가 형성되기 쉽지만, 이러한 문제를 극복하기 위한 연구는 희박한 실정이다. 본 연구에서는 위보기 및 경사상진자세에서 볼록한 이면비드의 형성을 연구하기위해서 용융지의 제어 방법을 적극적으로 검토하였다. 4mm 두께의 SS400 시편을 위보기 및 경사상진자세에서 각각 Bead-on-plate 용접하고, 이 때 비드성형기의 사용에 따른 비드 형상 변화를 관찰하였다. 텅스텐 전극과 비드 성형기간의 거리(Tip To Former Distance, 이하 TTFD)를 4.5mm에서 7.5mm로 1mm단위로 변경시켜 실험하였으며, TTFD가 증가할수록 위보기 및 경사상진자세에서 이면비드 높이가 감소하였으며 표면비드의 처짐이 증가하였다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.87-92
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• 2007

The full automation 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 paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.66-70
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• 2005

The full automation of 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 paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to select an optimal neural network model.

• Journal of Welding and Joining
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• v.32 no.6
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• pp.8-13
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• 2014

One of the main challenges of the automatic arc welding process which has been widely used in various constructions such as steel structures, bridges, autos, motorcycles, construction machinery, ships, offshore structures, pressure vessels, and pipelines is to create specific welding knowledge and techniques with high quality and productivity of the production-based industry. Commercially available automated arc welding systems use simple control techniques that focus on linear system models with a small subset of the larger set of welding parameters, thereby limiting the number of applications that can be automated. However, the correlations of welding parameters and bead geometry as welding quality have mostly been linked by a trial and error method to adjust the welding parameters. In addition, the systematic correlation between these parameters have not been identified yet. To solve such problems, a new or modified models to determine the welding parameters for tandem GMA (Gas Metal Arc) welding process is required. In this study, A new predictive model called STACO model, has been proposed. Based on the experimental results, STACO model was developed with the help of a standard statistical package program, MINITAB software and MATLAB software. Cross-comparative analysis has been applied to verify the reliability of the developed model.