• Journal of Welding and Joining
• /
• v.18 no.6
• /
• pp.55-61
• /
• 2000

Dynamic monitoring of weld pool formation and seam deviations using infrared vision is described in this paper. Isothermal contours representing heat dissipation characteristics during the process of arc welding were analysed and processed using imaging techniques. Maximum bead width and penetration were recorded and the geometric position in relation to the welding seam was measured at each sampling point. Deviations from the desired bead geometry and welding path were sensed and their thermographic representations were digitised and welding path were sensed and their thermographic representations were digitised and subsequently identified. Evidence suggested that infrared thermography can be utilized to compensate for inaccuracies encountered in real-time during robotic arc welding.

• Journal of the Korean Society for Precision Engineering
• /
• v.28 no.9
• /
• pp.1054-1061
• /
• 2011

GMA welding process is a production process to improve productivity for the provision of higher quality of material, These includs 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 experimens 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 healthy bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.

• Journal of Welding and Joining
• /
• v.29 no.4
• /
• pp.41-47
• /
• 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 Korean Society of Precision Engineering Conference
• /
• 2012.10a
• /
• pp.257-258
• /
• 2012

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.03a
• /
• pp.274-280
• /
• 1998

The welding technology and qualities are developed significantly, in recent years, in the use of automated processing technology and welding robot systems. But these automated welding technologies have many difficulties for finding the optimal welding parameter conditions. Because of the lack of mathematical model for determination of optimal welding process parameters. In this study, the sensitivity analysis of the empirical equations for finding weld bead width, height and penetration depth by using the published formulae. The selected major welding process parameters effected to weld bead geometries are the welding speed, current, voltage and weld wire diameter.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.8
• /
• pp.71-78
• /
• 2001

In GMA(Gas Metal Arc)Welding, the weld size that is a locally melted area of a workpiece is one of the most important considerations in determining the strength of a welded structure. Variations in the weld power and the welding heat flux may affect the weld pool formation and ultimately the size of the weld. Therefore, an accurate prediction of the weld size requires a precise analysis of the weld thermal cycle. In this study, a model which can estimate the weld bead geometry and a method for thermal analysis, including the model, are suggested. In order to analyze the weld bead geometry, a mathematical model was developed with transformed coordinates to apply to the horizontal fillet joints. A heat flow analysis was performed with a two dimensional finite element model that was adopted for computing the base metal melting zone. The reliability of the proposed model and the thermal analysis was evaluated through experiments, and the results showed that the proposed model was very effective for predicting the weld bead shape and good correspondence in melting zone of the base metal.

• Proceedings of the KWS Conference
• /
• 2002.10a
• /
• pp.151-155
• /
• 2002

In GMA(Gas Metal Arc)Welding, the weld size that is a locally melted area of a workpiece is one of the most important considerations in determining the strength of a welded structure. Variations in the weld power and the welding heat flux may affect the weld pool formation and ultimately the size of the weld. Therefore, an accurate prediction of the weld size requires a precise analysis of the weld thermal cycle. In this study, a model which can estimate the weld bead geometry and a method for thermal analysis, including the model, are suggested. In order to analyze the weld bead geometry, a mathematical model was developed with transformed coordinates to apply to the horizontal fillet joints. A heat flow analysis was performed with a two dimensional finite element model that was adopted for computing the base metal melting zone. The reliability of the proposed model and the thermal analysis was evaluated through experiments, and the results showed that the proposed model was very effective for predicting the weld bead shape and good correspondence in melting zone of the base metal.

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

• Journal of Welding and Joining
• /
• v.19 no.2
• /
• pp.200-207
• /
• 2001

Three-diemnsional bead profile was measured using the biprism stereo vision sensor in GMAW, which consists of an optical filter, biprism and CCD camera. Since single CCD camera is used, this system has various advantages over the conventional stereo vision system using two cameras such as finding the corresponding points along the horizontal scanline. In this wort, the biprism stereo vision sensor was designed for the GMAW, and the linear calibration method was proposed to determine the prism and camera parameters. Image processing techniques were employed to find the corresponding point along the pool boundary. The ism-intensity contour corresponding to the pool boundary was found in the pixel order and the filter-based matching algorithm was used to refine the corresponding points in the subpixel order. Predicted bead dimensions were in broad agreements with the measured results under the conditions of spray mode and humping bead.

• Journal of the Korean Institute of Navigation
• /
• v.23 no.4
• /
• pp.105-114
• /
• 1999

Generally, though we use the vision sensor or arc sensor in welding process, it is difficult to define the welding parameters which can be applied to the weld quality control. Especially, the important Parameters is Arc Voltage, Welding Current, Welding Speed in arc welding process and they affect the decision of weld bead shape, the stability of welding process and the decision of weld quality. Therefore, it is difficult to determine the unique relationship between the weld bead geometry and the combination of various welding condition. Due to the various difficulties as mentioned, we intend to use Fuzzy Logic and Neural Network to solve these problems. Therefore, the combination of Fuzzy Logic and Neural network has an effect on removing the weld defects, improving the weld quality and turning the desired weld bead shape. Finally, this system can be used under what kind of welding recess adequately and help us make an estimate of the weld bead shape and remove the weld defects.