• Journal of Welding and Joining
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• v.21 no.1
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• pp.60-65
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• 2003

At present. welding of most pipes with large diameter is carried out by the manual process. Automation of the welding process is necessary f3r the sake of consistent weld quality and improvement in productivity. In this study, two vision sensors, based on the optical triangulation, were used to obtain the information for seam tracking and detecting the weld defects. Through utilization of the vision sensors, noises were removed, images and 3D information obtained and positions of the feature points detected. The aforementioned process provided the seam and leg position data, calculated the magnitude of the gap, fillet area and leg length and judged the weld defects by ISO 5817. Noises in the images were removed by using the gradient values of the laser stripe's coordinates and various feature points were detected by using an algorithm based on the iterative polygon approximation method. Since the process time is very important, all the aforementioned processes should be conducted during welding.

• Journal of Welding and Joining
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• v.14 no.2
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• pp.96-105
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• 1996

Robot systems are widely utilized in the shipbuilding industry to enhance the productivity by automating the welding process. In order to increase productivity, it is necessary to reduce the time used for robot teaching. In this work, the automatic teaching system is developed for the subassembly process in the shipbuilding industry. A alser/vision sensor is designed to detect the weld seam and the image of the fillet joint is processed using the arm method. Positions of weld seams defined in the CAD database are transformed into the robot coordinate, and the dynamic programming technique is applied to find the sub-optimum weld path. Experiments are carried out to verify the system performance. The results show that the proposed automatic teaching system performs successfully and can be applied to the robot system in the subassembly process.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.110-117
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• 2004

In automatic welding process using a robot, seam tracking is one of the important parts. Sensor for seam tracking is divided broadly into two categories as non contact sensor and contact sensor. The arc sensor is one of the non contact sensors, and it can be applied in weaving arc and rotating arc welding process. In such the arc sensors, rotating arc sensor can be applied to high speed welding over tens of Hz. The decrease of self regulation by high rotating speed causes to improve accuracy and response of sensor. In this study, fuzzy controller was used to track the seam for the $CO_2$ arc welding which had unstable arc. It could be shown that the rotating arc sensor was better than the weaving arc sensor.

• Journal of Korean Society of Steel Construction
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• v.8 no.3 s.28
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• pp.35-45
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• 1996

The purpose of this paper is to investigate experimentally the strength and deformation of T-joints to improve the structural behavior of truss connection consisted in square hollow steel section. There are two-types to be changed in T-joint shape. One type is TP-series that a brace member is rotated to 45 degree, another type is TR-series that a chord member is rotated to 45 degree. The most important results obtained from the 2nd experiment on the T-joints is as follow; The yielding strength and initial stiffness of TP-series increase more than a current type(TS-series) in $0.6{\leq}{\beta}(d/D){\leq}0.7(0.85{\leq}{\beta}$'$(={\sqrt2}{\cdot}{\beta}){\leq}1.0)$.

• Journal of Welding and Joining
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• v.31 no.3
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• pp.44-48
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• 2013

Artificial neural network (ANN) model is applied to predict arc welding process window for automotive steel plate. Target weldment was various automotive steel plate combination with lap fillet joint. The accuracy of prediction was evaluated through comparison experimental result to ANN simulation. The effect of ANN variables on the accuracy is investigated such as number of hidden layers, perceptrons and transfer function type. A static back propagation model is established and tested. The result shows comparatively accurate predictability of the suggested ANN model. However, it restricts to use nonlinear transfer function instead of linear type and suggests only one single hidden layer rather than multiple ones to get better accuracy. In addition to this, obvious fact is affirmed again that the more perceptrons guarantee the better accuracy under the precondition that there are enough experimental database to train the neural network.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.26-32
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• 2012

In this paper, the effects of irregular bead shapes on fatigue life were investigated. A modified S-N curve method was used to estimate the fatigue life, which considered the inherent multiaxiality caused by the geometrical feature produced by the welding process. The point method of the critical distance method was used to determine the fatigue effective stress. Three types of fillet joint models were tested in the fatigue experiments. For each model, real bead shapes were collected using a 3D laser scanner, and finite element analyses were performed. The results of the analyses with actual bead shapes were compared with those using an idealized bead shape model. The results of the present analytical methods showed good agreement with the experimental results.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.4
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• pp.417-423
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• 2009

This paper is concerned with the fatigue behaviour of welded joints by the notch stress approach. The actual welded shape is complex and 3-dimensional that may influence greatly the fatigue strength. The purpose of the paper is to present a way of modelling the actual weld bead shape by using a 3-D Laser scanner for experimental models of steel plates with longitudinal fillet welds, and applying its results to a proper notch stress method for the fatigue strength. The present approach to assess the fatigue strength is quite promising with application to a variety of welded joints and effects of weld profiling to fatigue strength.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.1-6
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• 2001

A weld quality assessment method is proposed in this work, which can be applied to the short-circuit mode in GMAW. Information about the welding signal trajectory, distribution of the signal duration at each sub-regions and short-circuit frequency is used to evaluate the weld quality. The weighted penalty, which is determined experimentally, is imposed for each abnormal signal. Performance of the proposed method is compared with the Simpson's method under the conditions of shielding gas reduction, workpiece surface contamination and joint gap in the butt and fillet welds. Although the proposed method predicts the weld quality with reasonable accuracy, further modification and extension to other metal transfer modes are needed as a further study.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.320-323
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• 2002

This paper presents a new generation of system for pressure vessel and shipbuilding. Typical pressure vessel and ship building weld joint preparations are either traditional V, butt, fillet grooves or have narrow or semi narrow gap profiles. The fillet and U groove are prevalently used in heavy industries and shipbuilding to melt and join the parts. Since the wall thickness can be up to 6" or greater, welds must be made in many layers, each layer containing several passes. However, the welding time for the conventional processes such as SAW(Submerged Arc Welding) and FCAW(Flux Cored Arc Welding) can be many hours. Although SAW and FCAW are normally a mechanized process, pressure vessel and ship structures welding up to now have usually been controlled by a full time operator. The operator has typically been responsible for positioning each individual weld run, for setting weld process parameters, for maintaining flux and wire levels, for removing slag and so on. The aim of the system is to develop a high speed welding system with multitorch for increasing the production speed on the line and to remove the need for the operator so that the system can run automatically for the complete multi-torch multi-layer weld. To achieve this, a laser vision sensor, a rotating torch and an image processing algorithm have been made. Also, the multitorch welding system can be applicable for the fine grained steel because of the high welding speed and lower heat input compare to a conventional welding process.