• 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.

• Proceedings of the KWS Conference
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• 2006.05a
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• pp.62-64
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• 2006

• Proceedings of the KWS Conference
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• 2006.05a
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• pp.245-247
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• 2006

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.5
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• pp.623-628
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• 2006

Laser material processing is a very fast advancing technology for various industrial applications. because of many advantages. Its major advantage of less and controlled heat input has been exploited successfully for the very critical application of aluminium alloy welding. This study suggested the occurrence source of weld-defects and its solution methods in a welding of lithium ion battery by pulsed Nd:YAG laser. In experiment. battery case has changed over joint geometry from welding of side position to flat one. In the case of a electrolyte injection hole in order to seal it. welding is carried out after pressing Al ball. At this time. an eccentric degree. contact length and gap are worked as a major parameters. As improving the method of Al ball pressing. it was able to reduce an eccentricity. increase the contact length and decrease gap. As a results of a experiment. a sound weld bead shape and crack-free weld bead can be obtained.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.339-343
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• 2005

Laser material processing is a very fast growing technology for various industrial applications, because of many advantages. Its major advantage of less and controlled heat input has been exploited successfully for the very critical application of aluminium alloy welding. This study suggested the occurrence source of weld-defects and its solution methods in a welding of lithium ion battery by pulsed Nd:YAG laser. In experiment, battery case has changed over joint geometry from welding of side position to flat one. In case of a electrolyte injection hole in order to seal it, welding is carried out after pressing Al ball. At this time, an eccentric degree, contact length and gap are worked as a major parameters. As improving the method of Al ball pressing, it was able to reduce an eccentricity, increase the contact length and decrease gap. As a results of a experiment, a sound weld bead shape and crack-free weld bead can be obtained.

• 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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.70-75
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• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.752-756
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• 2002

Variable weld bead penetrations related to the base metal chemistry of stainless steels in GTA welding have been under constant investigations due to their industrial implications. It has been proposed that among other elements, the sulfur content of steels determines the weld pool geometry, particularly its penetration. It is suggested that the surface tension temperature gradient of steels becomes positive with appropriate dosing in sulfur and results in inward melt flow, propitious for deeper welds. However, the chemistry of industrial steels is complex due to the presence of multiple minor elements either deliberately added or remnant impurity traces. With this in view, investigations on 41 austenitic and nine martensitic stainless steels were carried to see if there existed any possible relation between the weld profile and some of the designated elements. The results suggest no direct correlation between sulfur or any other major or trace element and weld penetration. At first glance the results are contradictory to what is often asserted.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.128.5-128
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• 2001

This paper presents investigation of an arc force sensor for a robotic welding control system. Arc force sensor is employed in this research to monitor the bead geometry of the arc welding process. Arc force sensor mounted at the end of the robot wrist was employed to measure the arc force applied to the weld. Experimental configuration for measurement of arc force was used to quantify the changes in the arc force distributions of the plate being welded. A relationship between the bead dimension and the arc force distributions was established. The sensor information was used to establish a relationship between welding current and arc force. Arc force sensor have shown to be one of the most sophisticated technique to monitor perturbations that occurred during robotic arc welding process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1326-1331
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• 2006

Weld root gap is a important fact of a falling-off weld quality in various kind of weld defect. The welding quality can be controlled by monitoring important parameters, such as, the Arc voltage, welding current and welding speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using neural networks for detecting and monitoring of weld root gap and bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and detect the welding defects.