• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2000년도 특별강연 및 춘계학술발표대회 개요집
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• pp.224-227
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• 2000

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1998년도 특별강연 및 춘계학술발표 개요집
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• pp.195-199
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• 1998

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1999년도 특별강연 및 춘계학술발표대회 개요집
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• pp.172-175
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• 1999

• International Journal of Korean Welding Society
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• 제1권1호
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• pp.23-30
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• 2001

Among the various welding conditions, the welding current that is inversely proportional to the tip-to-work-piece distance is an essential parameter as to monitor the GMAW process and to implement the welding automation. Considering the weld pool surface geometry including weld defects, it should modify the signal processing method for automatic seam tracking in horizontal fillet welding. To meet the above necessities, a mathematical model related with the weld pool geometry was proposed as in a conjunction with the two-dimensional heat flow analysis of the horizontal fillet welding. The signal processing method based on the artificial neural network (Adaptive Resonance Theory) was proposed for discriminating the sound weld pool surface from that with the weld defects. The reliability of the numerical model and the signal processing method proposed were evaluated through the experiments of which showed that they are effective for predicting the weld bead shape with or without the weld defects in a horizontal fillet welding.

• 한국공작기계학회논문집
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• 제16권4호
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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.

• 한국생산제조학회지
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• 제6권3호
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• pp.26-35
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• 1997

In this study, the drawing characteristics of circular drawbead are examined with the plane strain elastic-plastic FE Method. Both the clamping load and the drawing load investigated by varying the process variables such as drawbead radius, closing depth and friction condition. The effective strain induced by the draw bead is also investigated. In order to verify the results, the computed results are compared with the existing experimental results. It has been found that both the clamping load and drawing loads are related with the geometry of the bead rather than the lubrication conditions.

• 한국정보통신학회논문지
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• 제9권6호
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• pp.1379-1386
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• 2005

현재 산업현장에서 활용하는 용접용 로봇은 대부분 오프라인(off-line)으로 작업을 수행하고 있어 생산성과 용접 품질 향상에 그 기능을 충분하게 발휘하지 못하는 실정이다. 현재에는 용접 품질 향상을 위하여 용접 매카니즘이 많이 연구되어 많은 수학적인 해석과 물리적인 해석방법을 도입하여 비선형적인 용접 메카니즘을 연구하고 있다. 이러한 여러 가지 비선형적인 문제와 해석상의 어려움에도 불구하고 용접의 결함을 보완하기 위해 보다 정확한 용접데이터를 생성하기 위하여 고감도의 센서를 도입하여 신호처리 하고 있으며, 이를 이용하여 용접시스템에 포함시키는 피드백제어시스템(feed-back control system)을 구성하여 용접선 추적 및 용접 비드(bead) 형상제어에 응용하고 있다. 또한, 최근에는 인공지능제어기술이 발달되어 인간의 학습능력과 의사결정능력을 대신하는 신경회로망(neural network)과 퍼지이론(fuzzy logic)을 도입하여 용접기술을 개발하고 발전시키고 있다. 본 연구에서는 신경회로망이론을 이용하여 실시간으로 용접시스템을 모니터링하고 퍼지제어기에 의하여 용접결함을 보정하는 지능시스템을 개발방법을 제시하고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.189-193
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• 1997

Generally, though we use the vision sensor or arc sensor in welding process, it is difficult to define the welding parameters which can be applied to the weld quality control. Especially, the important parameters is Arc Voltage, Welding Current, Welding Speed in arc welding process and they affect the decision of weld bead shape, the stability of welding process and the decision of weld quality. Therefore, it is difficult to determine the unique relationship between the weld bead geometry and the combination of various welding condition. Due to the various difficulties as mentioned, we intend to use Fuzzy Logic and Neural Network to solve these problems. Therefore, the combination of Fuzzy Logic and Neural network has an effect on removing the weld defects, improving the weld quality and turning the desired weld bead shape. Finally, this system can be used under what kind of welding process adequately and help us make an estimate of the weld bead shape and remove the weld defects.

• 한국공작기계학회논문집
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• 제17권6호
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• pp.49-55
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• 2008

Arc welding process is one of the most important technologies to join metal plates. Robotic welding offers the reduced manufacturing cost sought, but its widespread use demands a means of sensing and correcting for inaccuracies in the part, the fixturing and the robot. A number of problems that need to be addressed in robotic arc welding processes include sensing, joint tracking, and lack of adequate models for process parameter prediction and quality control. Problems with parameter settings and quality control occur frequently in the GMA(Gas Metal Arc) welding process due to the large number of interactive process parameters that must be set and accurately controlled. The objectives of this paper are to realize the mapping characteristics of bead width using a sensitivity analysis and develop the neural network and multiple regression method, and finally select the most accurate model in order to control the weld quality(bead width) for fillet welding. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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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.