• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.323-327
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• 1996

Gap and thickness difference information between blanks are often necessary for tailored blank welding quality evaluation , optimum welding parameters selection and evaluation of shearing machine, blink allocation device accuracy and clamping device. We develope 3D vision system and camera unit using structured lighting for this purpose. A simple ar d efficient scheme for gap and thickness feature recognition Is developed as well as measurements. Experimental results shows this system measuring accuracy is 10 ${\mu}{\textrm}{m}$ and 16${\mu}{\textrm}{m}$ for gap and thickness difference respectively The data are expexed to be useful for preview gap control.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.46-52
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• 2000

3D Welding and Milling is a solid freeform fabrication process which is based on the combination of welding as additive and conventional milling as subtractive technique. This hybrid approach enables direct building of metallic parts with high accuracy and surface finish. Although it needs further improvements it shows an application potential in rapid tooling of injection mold inserts as the investigation results show. To optimize the process for higher surface quality and accuracy effectively Taguchi method is applied to the experimental investigation. in this way relationships between process parameters and final product qualities such as tensile strength and surface hardness are found with minimal efforts.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.51-60
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• 2001

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• Journal of Welding and Joining
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• v.20 no.5
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• pp.113-119
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• 2002

To prevent problems caused by welding deformation, preparation in the design stage is necessary. Countermeasures in the design stage is also the most cost-effective method. In this study, to give designers information on the welding deformation, a system to visualize the welding deformation is developed. The model to visualize the deformation is the stiffened plate common in steel structures. To increase computational efficiency, theoretical solutions to calculate the deformation of plate and stiffener are used instead of numerical analysis. Also, to secure accuracy, experiments to estimate bending moment causing welding deformations are performed. A computer program written with Visual C++ is developed for interactive data input, calculation of welding deformation and display of deformed shape. Designers can change the design in the early stage after checking the deformed shape by this system.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1065-1070
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• 2003

In arc spot welding process, the arc is not moving and heat input is concentrated in one spot so that the heat input efficiency of arc is higher than that of GMAW. In other words, the heat input efficiency of arc change during weld time because arc start is done in spot and weld metal is filled. Therefore, the heat input model of arc spot welding should be different from that of general GMAW. In present study, the calculating model of heat input efficiency in arc spot welding was suggested by temperature monitoring near spot in arc spot welding of copper plate. The result showed that the heat input efficiency of arc was changed three times during weld time. The accuracy of calculating method of heat input efficiency was verified by heat transfer analysis of arc spot welding process using finite element method.

• International Journal of Korean Welding Society
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• v.4 no.1
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• pp.10-14
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• 2004

In this paper, a study on the residual stress of plate with longitudinal stiffeners is explained in terms of the welding sequences. In order to verify the results of numerical analysis, the hole drilling method (HDM) is performed, to measuring the residual stresses of the test plates in $CO_2$ Flux Cored Arc Welding (FCAW) under various welding conditions. The non-linear transient analysis technique for the numerical analysis in a large and complicate structure is considered. The residual stress of plate in consideration of the welding sequences and directions is evaluated by some numerical simulations and also by experiments. Comparison of numerical analysis results with experimental data shows the accuracy and validity of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.129-134
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• 2001

It is essential to predict the welding deformation at assembly stage, to increase productivity through mechanization and automation effectively. A practical analysis method appled for production engineering was proposed to simulate the deformation of arc welding, with an analytical model using finite element method solving thermal-elastic-plastic behavior. In this research, for accurate assembling, 3-D thermal-elastic-plastic finite element model is used to simulate the out-of-plane deformation caused by arc welding. Efforts have been made to find out the efficient method to improve the reliability and accuracy of the numerical calculation. Each of theories of small and large deformation is applied in solving 3-D thermal-elastic-plastic problem to compare with their efficiency about calculation imes and solution accuracy. When solid elements are used in a bending problem of a plate, phenomenon that the predictive deformation is more than that of actual survey is observed. To prevent this phenomenon, reduced integration method for element is employed instead of full integration that is generally used in 3-D thermal-elastic-plastic analysis.

• Journal of Welding and Joining
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• v.13 no.3
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• pp.77-88
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• 1995

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO$_{2}$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a backpropagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the ailure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• Tribology and Lubricants
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• v.39 no.3
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• pp.118-122
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• 2023

In our study, we develop a finite element model based on Archard's wear law to predict the cumulative wear and the evolution of the tool profile in friction stir welding (FSW) applications. Our model considers the rotational and translational behaviors of the tool, providing a comprehensive description of the wear process. We validate the accuracy of our model by comparing it against experimental results, examining both the predicted cumulative wear and the resulting changes to the tool profile caused by wear. We perform a detailed comparison between the predictions of the model and experimental data by manipulating non-dimensional coefficients comprising model parameters, such as element sizes and time increments. This comparison facilitates the identification of a specific non-dimensional coefficient condition that best replicates the experimentally observed cumulative wear. We also directly compare the worn tool profiles predicted by the model using this specific non-dimensional coefficient condition with the profiles obtained from wear experiments. Through this process, we identify the model settings that yield a tool wear profile closely aligning with the experimental results. Our research demonstrates that carefully selecting non-dimensional coefficients can significantly enhance the predictive accuracy of finite element models for tool wear in FSW processes. The results from our study hold potential implications for enhancing tool longevity and welding quality in industrial applications.