• Journal of Welding and Joining
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• v.11 no.2
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• pp.62-73
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• 1993

Weld pool oscillation for the full-penetration GTA welding process was investigated for its possible application to weld penetration control through theoretical modeling and experiment. Energy method was used to estimate the natural frequency of the molten pool having the physically-acceptable weld geometry and oscillation modes. An unique experimental system was built which had the data acquisiton and video capabilities so that the pool oscillation signals and molten pool surfaces could be monitored continuously. Pool oscillation was detected through arc voltage and arc light emission simultaneously. The signal from arc light emission showed good coherence with that from arc voltage, and arc light generated the higher quality signal. The molten pool was found to oscillate in different oscillation modes based on the travel speed and weld geometry. The natural frequency estimated from the theoretical model agreed reasonably well with the experimental results.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.69-70
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• 2014

In this paper, the 50kw submerged arc welding system has AC/DC durable output power and when AC output power is on, it shows control function on frequency, duty and offset. This system is constructed based on parrallel system, set on modular structure minimize the influence from high power into system. Also, it applied the sequence for optimized welding ability which lead it to increased weld quality.

• Journal of Welding and Joining
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• v.14 no.1
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• pp.99-106
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• 1996

Development of an automatic TIG welding system incorporating a vision sensor and torch control mechanism leads to an improved welding quality and greater production efficiency. The automatic welding system should be greatly restricted in its size and weight for the LNG(Liquefied Natural Gas) storage tank and also provide a unique torch rotating mechanism which keeps the torch tip in the constant position while the angle is changed continuously to maintain the welding torch substantially perpendicular to the weld line. The developed system is driven by two translation axes X, Z and one rotational axis. A moving line window method is adopted to the image recognition of the corrugated membranes with specular reflection. This method decides original laser stripe patterns in image which is affected by multi-reflection. A self-teaching algorithm, which guides the automatic welding machine with the information provided by the CCD camera without any previous learning of a reference trajectory, was developed for tracking the corrugated membrane of the LNG tank along the weld line.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.87-92
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• 2007

The full automation 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 models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ 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 Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• 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.

• Coupled systems mechanics
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• v.11 no.3
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• pp.267-284
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• 2022

Due to the high usage of ABS in industries, such as aerospace, auto, recreational devices, boat, submarines, etc., the purpose of this project was to find a way to weld this material, which gives advantages, such as affordable, high speed, and good connection quality. In this experimental project, the friction welding method was applied with parameters such as numerical control (NC) machine with two different speeds and three cross-sections, including a flat surface, cone, and step. After the end of the welding process, samples were then applied for both tensile and bending tests of materials, and the results showed that, with increasing the machining velocity Considering of samples, the friction of the surface increased and then caused to increase in the surface temperature. Considering mentioned contents, the melting temperature of composite materials increased. This can give a chance to have a better combination of Nanomaterial to base melted materials. Thus, the result showed that, with increasing the weight percentage (wt %) of Nanomaterials contents, and machining velocity, the mechanical behavior of welded area for all three types of samples were just increased. This enhancement is due to the better melting process on the welded area of different Nano contents; also, the results showed that the shape of the welding area could play a significant role, and by changing the shape, the results also changed drastically.A better shape for the welding process was dedicated to the step surface.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.699-702
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• 2000

The automation and control of robotic welding process is a very complex assignment because the system is affected by a number of variables which are very difficult to determine or predict in practice. Not only the optimization of the robotic welding process is considered from the point of view of the time and the cost of manufacturing. as well as quality of the weldment. the human factors of the production and many other factors must taken into consideration. hi order to determine the optimal parameters of robotic welding process, it is necessary to build a computer model representing all parameters influencing the welding process as well as the mutual dependence between them. This paper presents an approach to modeling the robotic welding process in which all parameters affecting the welding process are included using a neural network. A detailed analysis of the simulation results has been carried out to evaluate the proposed neural network model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.11
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• pp.1813-1819
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• 2001

Partial penetration welding Joint defines that groove welds without steel backing, welded from on side, and groove weeds welded from both sides but without back gouging, that is. it has an unwelded portion at the root of the weld. In this study we analysed fur residual stress and displacement distribution on partial penetration welding condition of thick plate metal. For 25.4mm thick plate, theoretical residual stress and displacement analysis by finite element method using ABAQUS was carried out and compared with the experimental result using hole-drilling method. In results of the condition of partial penetration, it appeared that longitudinal stress at welding area was a little difference and transverse stress did not have any effect by partial penetration multi-pass welding. From a point of welding distortion in partial penetration multi-pass welding, it seemed to be better to control root face smaller than 6.35mm.

• Proceedings of the KWS Conference
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• 1998.10a
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• pp.129-132
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• 1998

The dynamic resistance monitoring in primary circuit or T/C is one of the important issues in that in-process and real time quality assurance of resistance spot weld is needed to increase the product reliability. It is well known that tile dynamic resistance curve gives us very useful information about nugget growth and weldability. In the present paper, a new dynamic resistance detecting method is presented as a practical manner of weld quality assurance using instantaneous current and voltage measured by primary circuit. Primary dynamic resistance patterns are basically similar to those of the secondary, but there is evident advantage such as no extra devices are needed to obtain the quality assurance index and eventually feedback control will be possible caused by T/C based monitoring system.