• Journal of the Korean Society of Mechanical Technology
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• v.13 no.1
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• pp.17-22
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• 2011

As the quality of a weld joint is strongly influenced by process parameters the welding process, an intelligent algorithms that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. In this study, prepared by ${\Phi}1.6mm$ GMA welding of metal wire nose Advice jowelui 350A 600A grade level inverter welder and DAIHEN SCR's were carried out using welding. Welding conditions were 5.5m/min wire feed rate the welding current is rapidly transmit approximately 260A, welding voltage was about 30V. CTWD a 22mm, shielding gas was Ar 20L/min and the welding speed was a 240mm/min. Using data collected during welding equipment welding current and welding voltage waveform was analyzed by measuring the volume of the transition mode. Addition of $CaCO_3$ as a loss of the spread of the weld bead dilution rate decreased, suggesting that, GMA in the overlay welding bead shape control, dilution control and may be used as a welding flux is considered. Stabilizing effect of the arc by the Ca-containing $CaF_2$, $CaCO_3$, $CaMg(CO_3)_2$, respectively, welding flux 0.1wt.% added GMA welding and weld overlay were evaluated with dilution, $CaF_2$, and $CaMg(CO_3)_2$ added to the dilution of Seemed to increase.

• Proceedings of the KWS Conference
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• 1995.10a
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• pp.34-38
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• 1995

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.159-161
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• 2003

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.284-286
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• 2003

• Proceedings of the KWS Conference
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• 2002.05a
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• pp.218-221
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• 2002

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.22-28
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• 2013

Robotic GMA (Gas Metal Arc) welding process is one of widely acceptable metal joining process. The heat and mass inputs are coupled and transferred by the weld arc to the molten weld pool and by the molten metal that is being transferred to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired quality of the weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. MD (Mahalanobis Distance) technique was employed for investigating and modeling the GMA welding process and significance test techniques were applied for the interpretation of the experimental data. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats. First, a set of weldments without any faults were generated in a number of repeated sessions in order to be used as references. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. Statistical models developed from experimental results which can be used to quantify the welding quality with respect to process parameters in order to achieve the desired bead geometry based on weld quality criteria.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.44-54
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• 1998

In this study a new arc sensor algorithm for automatic weld seam tracking was proposed, which uses the relative welding current variation according to the tip-to-workpiece distance in GMA welding. Since the new developed arc sensor algorithm is not sensitive to unstable factors of arc signal, the system is expected to get rid of the problems of already existing arc sensor system which include the difficulty of modeling the process for various welding conditions and limitation of application to thick plate welding. Thus the system is applicable not only to thick plate welding but also to thin plate welding. To implement the new arc sensor algorithm the system parameters which include sampling time, averaging range, weighting factor of moving averaging, basic compensation time, and basic compensation distance were determined by experimental analysis. Consequently this system has shown the successful tracking capability for the various welding conditions.

• 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 KWS Conference
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• 2004.05a
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• pp.191-193
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• 2004

GMA(Gas Metal Arc)용접에서는 콘택트 팁이 토치 선단에 위치하여 용접 와이어를 송급하여 준다. 이러한 콘택트 팁의 주된 기능은 용접 전류를 와이어에 전송시켜주고, 와이어를 용접하고자 하는 위치로 유도하는 것이다. (중략)

• Proceedings of the KWS Conference
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• 2002.05a
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• pp.68-70
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• 2002

A CCD camera with a laser strip was applied to realize the automation of welding process in GMAW. The Hough transformation was used to extract the laser stripe and to obtain specific weld points. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.