• Journal of Welding and Joining
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• v.34 no.1
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• pp.21-25
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• 2016

The automotive vehicle is made through the following processes such as press shop, welding shop, paint shop, and general assembly. Among them, the most important process to determine the quality of the car body is the welding process. Generally, more than 400 pressed panels are welded to make BIW (Body In White) by using the RSW (Resistance Spot Welding) and GMAW (Gas Metal Arc Welding). Recently, as the needs of light-weight material due to the $CO_2$ emission issue and fuel efficiency, new joining technologies for aluminum, CFRP (Carbon Fiber Reinforced Plastic) and etc. are needed. Aluminum parts are assembled by the spot welding, clinching, and SPR (Self Piercing Rivet) and friction stir welding process. Structural adhesive boning is another main joining method for light-weight materials. For example, one piece aluminum shock absorber housing part is made by die casting process and is assembled with conventional steel part by SPR and adhesive bond. Another way to reduce the amount of the car body weight is to use AHSS (Advanced High Strength Steel) panel including hot stamping boron alloyed steel. As the new materials are introduced to car body joining, productivity and quality have become more critical. Productivity improvement technology and adaptive welding control are essential technology for the future manufacturing environment.

• Proceedings of the KWS Conference
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• 2010.05a
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• pp.43-46
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• 2010

As the nuclear power plant has been constructed continuously for several decades in Korea, the welding technology for components manufacturing and installation has been improved largely. Standardization for weld test and qualification was also established systematically according to the concerned code. The welding for the main components requires the high reliability to keep the constant quality level, which means the repeatability of weld quality. Therefore the weld process qualified by thorough test and evaluation is able to be applied for manufacturing. Narrow gap SAW and GTAW process are usually applied for girth seam welding of pressure vessel like Reactor vessel, steam generator, and etc. For the surface cladding with stainless steel and Inconel material, strip welding process is mainly used. Inside cladding of nozzles is additionally applied with Hot wire GTAW and semi-auto welding process. Especially the weld joint having elliptical weld line on curved surface needs a specialized weld system which is automatically rotating with adjusting position of the head torch. The small sized pipe, tube, and internal parts of reactor vessel requests precise weld processes like an automatic GTAW and electron beam welding. Welding of dissimilar materials including Inconel690 material has high possibility of weld defects like a lack of fusion, various types of crack. To avoid these kinds of problem, optimum weld parameters and sequence should be set up through the many tests. As the life extension of nuclear power plant is general trend, weld technologies having higher reliability is required gradually. More development of specialized welding systems, weld part analysis and evaluation, and life prediction for main components should be taken into a consideration extensively.

• Journal of Welding and Joining
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• v.35 no.2
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• pp.43-51
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• 2017

The automatic welding systems, have received much attention in recent years, because they are highly suitable not only to increase the quality and productivity, but also to decrease manufacturing time and cost for a given product. Automatic welding work in semiconductor or space industry to be carried out in pipe line and butt joint mostly and plasma arc welding(PAW) is actively applied. To get the desired quality welds in automated welding system is challenging, a mathematical model is needed that has complete control over the relevant process parameters in order to obtain the required mechanical properties. However, In various industries the welding process mathematical model is not fully formulated for the process parameter and on the welding conditions, therefore only partial variables can be predicted. Therefore, this paper investigates the interaction between the welding parameters and mechanical properties for predicting the weld bead geometry by analyzing the S/N ratio.

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

This paper describes several advances in sensor and process control techniques for applications in Submerged Arc Welding (SAW), which combine to give a fully automatic system capable of controlling and adapting the overall welding process. This technology has been applied in longitudinal and spiral pipe mills and in pressure vessel production.

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.300-302
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• 2001

This paper describes several advances in vision sensor and process control techniques for applications in Submerged Arc Welding(SAW) which combine to give a fully automatic system capable of controlling and adapting the overall welding process.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.49-60
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• 1999

This paper addresses a welding task sequencing for robot arc welding process planning. Although welding task sequencing is an essential step in the welding process planning, it has not been considered through a systematic approach, but it depends rather on empirical knowledge. Thus, an effective task sequencing for robot arc welding is required. Welding perations can be classified by the number of welding robots. Genetic algorithms are applied to tackle those welding task sequencing problems. A genetic algorithm for traveling salesman problem (TSP) is utilized to determine welding task sequencing for a MultiWeldline-SingleLayer problem. Further, welding task sequencing for multiWeldline-MultiLayer welding is investigated and appropriate genetic algorithms are introduced. A random key genetic algorithm is also proposed to solve multi-robot welding sequencing : MultiWeldline with multi robots. Finally, the genetic algorithm are implemented for the welding task sequencing of three dimensional weld plate assemblies. Robot welding operations conforming to the algorithms are simulated in graphic detail using a robot simulation software IGRIP.

• Journal of Welding and Joining
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• v.23 no.6
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• pp.49-54
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• 2005

Inner Structured and Bonded(ISB) panel, a kind of metallic sandwich panel, consists of two thin skin plates bonded to a micro-patterned inner structure. Its overall thickness is $1\~3mm$and it has attractive properties such as ultra-lightweight, high efficiency in stiffness-to-weight and strength-to-weight ratio. In many previous studies, resistance welding, brazing and adhesive bonding are studied for joining the panel. However these methods did not consider productivity, but focused on structural characteristics of joined panels, so that the joining process is very complicated and expensive. In this paper, a new joining process with resistance welding is developed. Curved surface electrodes are used to consider the productivity and the stopper is used between electrodes during welding time to maintain the shape of inner structure. Welding time, gap of electrodes and distance between welding points are selected as the process parameters. By measuring the tensile load with respect to the variation of welding time and gap of electrodes, proper welding conditions are studied. Welding time is proper between 1.5-2.5cycle. If welding time is too long, then inner structures are damaged by overheating. Gap of electrode should be shorter than threshold value fur joint strength, when total thickness of inner structure and skin plate is 3.3mm, the threshold distance is 3.0mm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.47-56
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• 2019

A deep neural network (DNN) model was proposed to predict the upset in the inertia friction welding process using a database comprising results from a series of FEM analyses. For the database, the upset length, upset beginning time, and upset completion time were extracted from the results of the FEM analyses obtained with various of axial pressure and initial rotational speed. A total of 35 training sets were constructed to train the proposed DNN with 4 hidden layers and 512 neurons in each layer, which can relate the input parameters to the welding results. The mean of the summation of squared error between the predicted results and the true results can be constrained to within 1.0e-4 after the training. Further, the network model was tested with another 10 sets of welding input parameters and results for comparison with FEM. The test showed that the relative error of DNN was within 2.8% for the prediction of upset. The results of DNN application revealed that the model could effectively provide welding results with respect to the exactness and cost for each combination of the welding input parameters.

• Proceedings of the KWS Conference
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• 1999.10a
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• pp.83-86
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• 1999

• Proceedings of the KWS Conference
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• 2003.11a
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• pp.147-149
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• 2003

Many factors, such as welding current, welding time, force, electrode shape, the output stability of a welding machine, are closely related with micro spot series welding. Of those factors, the output stability evaluation of the welding machine would be checked first. in this study, we applied 6$\sigma$ to evaluation for the output stability of the welding machine. We estimated output stability and control stability of the welding machine by using Cp(process capability), Ppk and 4-block diagram. Therefore the problem as solved in micro spot series welding process and the problem of output control in welding machine by 6$\sigma$ tool.