• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.7-12
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• 2001

Real time monitoring has become critical as the use of laser welding increases. Plasma and spatter are measured and used as the signal for estimating weld quality. The estimating algorithm was made using the fuzzy pattern recognition with the area of data that is beyond the tolerance boundary. Also, an algorithm that detects the spatter and the localized defect was created in order to kd the partially produced pit and the sudden loss of weld penetration. These algorithms were used in quality monitoring of the $CO_2$ laser tailored blank weld. Statistical program that can display the laser weld quality result and the signal transition was made for the first stage of the remote control system.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.53-60
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• 2014

The automatic arc welding is generally accepted as the preferred joining technique and commonly chosen for assembly of large metal structures such as in areas of automotive, aircraft and shipbuilding due to its joint strength, reliability, and low cost compared to other joint processes. Recently, several mathematical models have been developed and studied for control and monitoring welding quality, productivity, microstructure and weld properties in arc welding processes. This study indicates the prediction of process parameters for the expected welding quality with accordance to the adaptive GTA welding process. Furthermore, the mathematical models is also develop to aid the selection of an optimal welding process as the generation of process controls to predict the bead geometry as a function output parameters in the GTA welding process. The developed models through this study showed comparatively excellent predicted results, and will extend to other welding processes to integrate an optimized system for the robotic welding process.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.182-188
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• 2000

High-frequency electric resistance welding (HERW) technique is one of the most productive manufacturing method currently available for pipe and tube production because of its high welding speed. In this process, a heat input is controlled by skilled operators observing color and shape of bead but such a manual control can not provide reliability and stability required for manufacturing pipes of high grade quality because of a variety of bead shapes and noisy environment. In this paper, in an effort to provide reliable quality inspection, we propose a neural network-based method for classification of bead shape. The proposed method utilizes the structure of Kohonen network and is designed to learn the skill of the expert operators and to provide a good solution to classify bead shapes according to their welding conditions. This proposed method is implemented on the real pipe manufacturing process, and a series of experiments are performed to show its effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.626-630
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• 2005

This paper presents a novel Intelligent-Welding-Carriage (IWC) for automation of curved block in shipbuilding. The curved block is usually used in both front and back side of the ship. In curved block root gap is big, $1{\sim}7$ [mm] and inclination, $0{\sim}30$ [deg]. Since available conventional carriage type is limited to use below root gap of 3 [mm], only manual welding is employed in curved block. To adopt an IWC in curved block, it requires control of the welding conditions, i.e., voltage, current and travel speed, with respect to root gap and inclination to achieve good welding quality. In this paper, an IWC is developed for automization of welding operation to accommodate gap and inclination. Kinematics model and dynamics using Lagrangian formulation of the manipulator is introduced. IWC utilizes a database to perform accurate welding. The database is programmed based on numerous experimental test results with respect to gap, inclination, material, travel speed, weaving condition, voltage, and current. Finally, experimental result using PID control is addressed for verify the trajectory tracking accuracy of end-effector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.937-942
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• 2000

This paper describes for applications of neural networks in the field of arc welding. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters affecting quality include the arc voltage, the welding current and the torch travel speed. The relationship between the welding parameters and weld quality is not a direct one, and in addition, the effect of the weld parameter variables are not independent of the each other - changing the welding current will affect the arc voltage, and so on. Finally, a suitable proposal to improve the construction of the model has also been presented in the paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6282-6288
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• 2014

$CO_2$ welding is used widely in the field. On the other hand, welding defects occur when welders cannot adjust the current and voltage needed for welding and have to stop working to adjust the current and voltage, causing sudden cooling down of the welding structure inside a vehicle or tank where the control panel is invisible or when work site is far. This study used three types of existing $CO_2$ welders. This also applied SS400 rolled steel for welding structural purposes for remote control torch welding, perform a welding test through v-groove butt welding with a remote control torch and existing $CO_2$ welding torch, conducted visual inspection on the appearance of a welded top bead. In addition, the appearance quality of the welding part was monitored mainly through penetrant testing and a bending test to evaluate the welding defect reduction and the effect on the performance and compatibility by replacing the existing welder.

• Proceedings of the KWS Conference
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• 2002.05a
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• pp.65-67
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• 2002

The gas metal arc welding(GMAW) is regarded as one of the best candidate for welding automation in industrial joining application. It is important to monitor the weld quality for the high performance of weld automation. The measured analog signal is frequency analyzed by digital signal process method. In order to observe the welding phenomena and control welding condition, arc light, voltage, and current are measured at the same time. They are analyzed and compared with arc sound. for these experiments, a power source of constant voltage characteristics was used in the pure metal transfer mode.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.117-120
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• 2001

Resistance spot welding is widely employed in a manufacturing process. In recent years, the requirement for more sophisticated quality control procedures has been in the mass production industries. The requirements for high productivity and good welding qualities have lead to the development of more widely available microprocessor or computer based control. In this study, the inverter-type power source and welding servo gun are developed.

• Proceedings of the KWS Conference
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• 2006.10a
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• pp.77-79
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• 2006

While RSW(Resistance Spot Welding) have been the most successful sheet metal joining process in automotive industry, there are certain qualify issues due to the control system and its application process. Recent materials and coatings make the process more complicated resulting in new challenges for quality welds. In this research, an intelligent RSW system with adaptive control algorithm is introduced to overcome typical RSW issues and its applications to automotive body assembly are presented.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1028-1033
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• 1991

This paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use of quality monitoring and control in GMA welding processes. The estimator utilizes surface temperatures measured at various points on the top surface of the weldment as its input. The main task of the neural net is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training, A series of bead-on plate welding experiments were performed to assess the performance of the neural estimator.