• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1287-1288
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• 2013

• Journal of Welding and Joining
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• v.15 no.6
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• pp.116-123
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• 1997

In GMA welding processes, bead contour and penetration patterns are criterion to estimate weld quality. Bead geometry is commonly defined with width, height and depth. When weaving is taken into account, selection of welding conditions is known to be difficult. Thus, empirical or trial-and-error method are usually introduced. This study examined the correlation of welding process variables including weaving parameters with bead geometry using srtificial neural networks(ANN). The main task of the Ann estimator is to realize the mapping characteristics from the sampled welding process variables to the actual bead geometry through training. After the neural network model is constructed, welding process variables for desired bead geometry is selected by inverse model. Experimental varification of the inverse model is conducted through actual welding.

• Journal of Welding and Joining
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• v.12 no.1
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• pp.59-72
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• 1994

This paper presents an on-line quality monitoring and control method to obtain a uniform weld quality in gas metal arc welding (GMAW) processes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to assess the integrity of the weld quality. Since a good quality weld is characterized by a relatively high depth-to-width ratio in its dimensions, the second geometrical parameter is regulated to a desired one. The monitoring variables are the surface temperatures measured at various points on the top surface of the weldment which are strongly related to the formation of the weld pool The relationship between the measured temperatures and the weld pool size is implemented on the multilayer perceptrons which are powerful for realization of complex mapping characteristics through training by samples. For on-line quality monitoring and control, it is prerequisite to estimate the weld pool sizes in the region of transient states. For this purpose, the time history of the surface temperatures is used as the input to the neural estimator. The control purpose is to obtain a uniform weld quality. In this research, the weld pool size is directly regulated to a desired one. The proposed controller is composed of a neural pool size estimator, a neural feedforward controller and a conventional feedback controller. The pool size estimator predicts the weld pool size under growing. The feedforward controller compensates for the nonlinear characteristics of the welding process. A series of simulation studies shows that the proposed control method improves the overall system response in the presence of changes in torch travel speed during GMA welding and guarantees the uniform weld quality.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1028-1033
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• 1991

This paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use of quality monitoring and control in GMA welding processes. The estimator utilizes surface temperatures measured at various points on the top surface of the weldment as its input. The main task of the neural net is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training, A series of bead-on plate welding experiments were performed to assess the performance of the neural estimator.

• Journal of Welding and Joining
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• v.34 no.2
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• pp.67-72
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• 2016

Aluminum alloy is one of light weight material and it is used to make LNG tank and ship. However, in order to weld aluminum alloy high density heat source is needed. In this paper, I-butt welding of Al 5083 with 6mm thickness using Plasma-MIG welding was carried out. The experiment was performed to investigate the influence of plasma-MIG welding parameters such as plasma current, wire feeding rate, MIG-welding voltage and welding speed on the tensile strength of weld. In addition we suggested 3 strength estimation models which are second order polynomial regression model, multiple nonlinear regression model and neural network model. The estimation performance of 3 models was evaluated in terms of average error rate (AER) and their values were 0.125, 0.238, and 0.021 respectively. Neural network model which has training concept and reflects non -linearity was best estimation performance.

• 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.

• Journal of the Korea Institute of Building Construction
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• v.5 no.3 s.17
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• pp.131-138
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• 2005

The study is focused on the settling the developing direction of korean gas pressure welding machine which can be applied in korean construction sites with moderate and high performance. Gas pressure welding is more economical and has good performances than other steel bar jointing methods, as arc welding and mechanical joint etc. Therefore in Japan, the gas pressure welding, which has less loss of steel bars and low performance of joints, when connecting the D29 and thicker steel bars, Is one of the typical connection of steel reinforcement. But in Korea, the gas pressure welding method is not widely used caused by the shortage of skilled workers, and this situation in Korea can not be solved in short period. The training of the skilled workers takes long period(around $6\~10$ years), and there is no certification system for gas pressure welding. So to activate the gas pressure welding in Korea, the development of the automatic gas pressure welding machine is necessary, which gives regular performance of the steel bar joints and can be operated by not sufficient skilled workers. The automatic gas pressure welding machine was developed in Japan, but this machine has many problems when applied in korean construction sites. Therefore, it is necessary to develop a korean automatic gas pressure welding machine to overcome this problems. To develop korean automatic gas pressure welding machine, the problems, which shows when applied in korean construction sites, need to be investigated. According to the investigation, counterproposals are presented for the pragmatical development of the korean automatic gas pressure welding machine.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.2
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• pp.156-165
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• 2024

Objectives: This study aims to identify and analyze the exposure status of welding students in specialized high school welding labratories, compare it with the exposure to welding hazards of industrial workers, and seek to improve the educational environment for youth through domestic and international exposure standards. Methods: This study compares the level of exposure to hazardous factors in a welding laboratory of a vocational high school in Jeollanam-do and a welding process in a general industrial site by measuring the work environment. A 10-question survey was conducted to review the effects of welding hazards on the human body, carcinogenicity information, international (US, UK, France) exposure standards, general characteristics between the two groups, and awareness of occupational health. Results: Exposure to hazardous factors in both groups was below the standards set by MOEL. Specialized high school students were exposed to higher levels than workers, and some hazardous factors exceeded the standards when compared to international exposure standards. During the survey, students were less aware of the hazards of welding, safety and health education, and the need for work environment measurement than workers. Conclusions: For the respiratory protection of students in vocational high school welding labs, it is necessary to create a comfortable training environment. Exposure standards for harmful factors should be strictly applied, such as overseas standards, or exposure should be limited by setting a limit on the number of hours of welding practice per week. In addition, it is necessary to conduct safety and health education for welding students to raise their awareness of the importance of measuring the working environment and wearing appropriate protective equipment.