• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.36-41
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• 1997

With the trend towards welding automation and robozation, mathematical models for studying the influence of various parameters on the weld bead geometry in Gas Metal Arc(GMA) welding process are required. The results of bead on plate welds deposited using the GMA welding process has enabled mathematical relationships to be developed that model the weld bead geometry. Experimental results were compared to outputs obtained using existing formulae that correlate process input variables to output parameters and subsequent modelling was performed in order to better predict the output of the GMA welding process. The aim of this work was to explain the relationships between GMA welding variables and weld bead geometry and thus, be able to predict input weld bead size. The relationships can be usefully employed for open loop process control and also for adaptive control provided that dynamic sensing of process output is performed.

• Journal of Welding and Joining
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• v.17 no.4
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• pp.39-45
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• 1999

The procedure of robotic Gas metal Arc (GMA) welding in order to achieve the optimized bead geometry needs the selection of suitable process parameters such as arc current, welding voltage, welding speed. It is required the relationships between process parameters and bead geometry. The objective of this paper is to develop the algorithm that enables the determination of process parameters from the optimized bead geometry for robotic GMA welding. It depends on the inversion of empirical equations derived from multiple regression analysis of the relationships between the process parameters and the bead dimensions using the least square method. The method not only directly determines those parameters which will give the desired set of bead geometry, but also avoids the need to iterate with a succession of guesses employed Finite Element Method(FEM). These results suggest that process parameter from experimental equation for robotic GMA welding may be employed to monitor and control the bead geometry in real time.

• Journal of Welding and Joining
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• v.29 no.4
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• pp.41-47
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• 2011

GMA welding process is a production process to improve productivity for the provision of higher welding quality of material. These includes numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experiments conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a good bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.300-302
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• 2001

This paper describes several advances in vision sensor and process control techniques for applications in Submerged Arc Welding(SAW) which combine to give a fully automatic system capable of controlling and adapting the overall welding process.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.46.6-46
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• 2001

A servo gun welding system having a AC servo motor and a PC control system is presented for the improvement of quality control in the spot welding. The spot welding process is composed of the press stage, the weld stage, and the hold stage. The changes of gun press forces according to three stages in the spot welding process are controlled and measured through the load cell in order to know the influence on the welding quality. The relation between the measured force changes according to three stages and welding qualities is also implemented on the multilayer perceptrons, one of supervised learning method of neural network, which are powerful for realization of complex mapping characteristics. The estimated results and ...

• Journal of Welding and Joining
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• v.15 no.6
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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.

• Proceedings of the KWS Conference
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• 1997.10a
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• pp.229-233
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• 1997

The demand to increase productivity and quality, the shortage of skilled labour and the strict health and safety requirements have led to the development of the automated welding process to deal with many of the present problems of welded fabrication. To make effective use of the automated arc welding process, it is imperative that a mathematical model, which can be programmed easily and fed to the robot, should be developed. The objectives of the paper are to develop the mathematical equations (linear and curvilinear) for study of the relationship between process variables and bead geometry by employing a standard statistical package program, SAS and to choose the best model for automation of the $CO_2$ gas arc welding process. Mathematical models developed from experimental results can be employed to control the process variables in order to achieve the desired bead geometry based on weld quality criteria. Also these equations may prove useful and applicable for automatic control system and expert systems.

• Proceedings of the KWS Conference
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• 2000.10a
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• pp.31-33
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• 2000

Welding processing is used in the various industrial fields such as shipbuilding, car, airplane and steel structure, etc.. But the welding process has a bad working condition and lack of skillful worker. The welding depended on man power causes low productivity and difficulty in keeping continuous and stable quality control. This paper shows the development results of welding mobile robot with the several functions such as continuous/intermittent welding, initial welding speed control, acceleration control, crater and deceleration speed control in welding end. The robot is developed based on microprocess which is intel 80c196kc.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.97-103
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• 1997

Designing a feedback controller requires a dynamic model of the process to be controlled. But the GMA welding process models have not been fully developed for many reasons such as complexity of the welding process and lack of reliable sensors. Because of the complexity of the welding system, we obtained a dynamic model for control using system identification routines, rather than derived a model from fundamental physical laws. The controller was designed based on the experimentally derived linear dynamic model of the welding process. In order to demonstrate application of the designed controller, the simulation was carried out.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2004.05a
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• pp.154-158
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• 2004

Power inductor is usually used in the field of the power circuit of a cellular phone, TFT LCD module etc.. This paper presents the development process of Power Inductor Full Automation Winding/Welding System for TFT LCD. This process, the process algorithm, high precision welding current control, design of welding head, high speed, high precision feeding mechanism, and user interface process control program technologies are included.