• Proceedings of the KWS Conference
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• 2002.05a
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• pp.136-138
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• 2002

• Laser Solutions
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• v.13 no.4
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• pp.14-20
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• 2010

Robot path generation and laser welding technology for manufacturing automotive body are studied. Laser welding and industrial robot systems are used with the robot based laser welding system. The laser system used in this study is 1.6kW Fiber laser, while the robot system is 6 axes Industrial robot (payload: 130kg). The robot based laser welding system is equipped with laser scanner system for remote laser welding. The laser source, robot and laser scanner system are used to increase the processing speed and to improve the process efficiency. The welding joints of steel plate are butt and lapped joints. The quality test of the laser welding are through the observation the shape of bead on plate and cross-section of welding part. The 3 dimensional laser welding for non-linear pipe welding line is performed. This paper introduces the robot based laser welding system to resolve the limited welding speed and accuracy of the conventional spot welding system.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.265-270
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• 2007

According to previous research, welding-induced residual stresses in steel structures can significantly affect the fatigue behaviour. Usually, high tensile residual stresses up to the yield strength are conservatively assumed at the weld toes. This conservative assumption can result in misleading fatigue assessments. Thee welding-induced residual stresses need be known in advance for a reliable fatigue assessment, which becomes possible to an increasing extent by numerical welding simulation. In this study, a fatigue Analysis technique for steel structures with welding induced residual stress is presented. First, We calculate the history of temperature according with welding process. Secondly, residual stress with a welding thermal history was evaluated by non-linear thermal stress analysis and lastly, fatigue strength is estimated with modified Goodman equation which can consider the effect of mean stress level.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.102-110
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• 1998

The horizontal fillet welding is prevalently used in heavy and ship building industries to fabricate the large scale structures. A deep understanding of the horizontal fillet welding process is restricted, because the phenomena occurring in welding are very complex and highly non-linear characteristics. To achieve the satisfactory weld bead geometry in robot welding system, the seam tracking algorithm should be reliable. The number of seam tracker was developed for arc welding automation by now. Among these seam tracker, the arc sensor is prevalently used in industrial robot welding system because of its low cost and flexibility. However, the accuracy of arc sensor would be decreased due to the electrical noise and metal transfer. In this study, the signal processing algorithm based on the neural network was implemented to enhance the reliability of measured welding current signals. Moreover, the seam tracking algorithm in conjunction with the signal processing algorithm was implemented to trace the center of weld line. It was revealed that the neural network could be effectively used to predict the welding current signal at the end of weaving.

• International Journal of Korean Welding Society
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• v.5 no.1
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• pp.29-34
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• 2005

Finite element analysis (FEA) and experimental studies on an elastic bending method have been performed in order to control the angular distortion at the fillet weldment for curved panel. Process parameters for the elastic bending method such as clamping span and release time were analyzed with reference to welding condition and geometric effect of the curved panel, which can minimize or prevent the angular distortion by producing a proper skin stress to the fillet weldment. The amounts of the angular distortion decrease almost in a linear manner with an increase in the skin stress. The skin stress required for non-angular distortion at the fillet weldment is strongly dependent on the plate thickness, not the heat intensity applied. The clamping span for obtaining uniform skin stress was defined as functions of the plate thickness and length of the free edge. Clamp should be removed after the fillet weldment is cooled down to room temperature for non-angular distortion. Effectiveness of the elastic bending method established was verified by its application to an actual curved panel.

• Journal of Welding and Joining
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• v.14 no.6
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• pp.58-67
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• 1996

The horizontal filet welding is prevalently used in heavy and ship building industries to join the parts. The phenomena occurring in the horizonal fillet welding process are very complex and highly non-linear, so that its analysis is relatively difficult. Furthermore, various kinds of weld defect such as undercut, overlap, porosity. excess weld metal and incomplete penetration can be induced due to improper welding conditions. Among these defects, undercut, overlap and excess weld metal appear frequently in horizontal filet welding. To achieve a satisfactory weld bead geometry without weld defects, it is necessary to study the effect of welding conditions in the weld bead geometry. For analyzing the weld bead geometry with and without weld defects in horizontal fillet welding, a mathematical model was proposed in conjunction with a two-dimensional heat flow analysis adopted for computing the melting tone in . base metal. The reliability of the proposed model was evaluated through experiments. which showed that the proposed model was very effective for predicting the weld bead shape with or without weld defects in horizontal fillet welding.

• Proceedings of the Korean Society of Laser Processing Conference
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• 2005.11a
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• pp.50-56
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• 2005

The robot systems are widely used in the many industrial field as well as welding manufacturing. The essential tasks to operate the welding robot are the acquisition of the position and/or shape of the parent metal. For the seam tracking or the robot tracking, many kinds of contact and non-contact sensors are used. Recently, the vision is most popular. In this paper, the development of the system which measures the shape of the welding part is described. This system uses the line-type structured laser diode and the vision sensor. It includes the correction of radial distortion which is often found in the image taken by the camera with short focal length. The Direct Linear Transformation (DLT) method is used for the camera calibration. The three dimensional shape of the parent metal is obtained after simple linear transformation. Some demos are shown to describe the performance of the developed system.

• 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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• 2004.10a
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ 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 Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• 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.