• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.9-12
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• 2009

최근 무인 로봇 및 산업 자동화의 비약적인 발전으로 용접 분야에서도 무인화 및 자동화 시스템 구축이 활성화 되고 있다. 본 논문에서는 아크 용접 시스템의 주요한 용접 인자인 용접전류, 용접전압 정보를 PSoC 기반의 WirelessUSB를 이용하여 무선으로 모니터링 시스템에 전송하고 이를 신경회로망에 적용하여 용접 현상을 모니터링 하였다. 또한 산업 현장에도 일반화된 TCP/IP 통신을 이용하여 원격으로 관리가 가능하도록 구현하였다.

• Journal of Welding and Joining
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• v.14 no.4
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• pp.24-32
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• 1996

용접공정 해석을 위한 접근방법중에서 우선적으로 결정해야할 사항으로는 비선형적인 요소와 복잡한 물리현상들을 실제적으로 해석하기위한 측정변수의 선정과 이러한 변수를 사용하여 물리적인 현상을 적절히 표현할 수 있는 알고리즘의 개발 등 을 들 수 있다. 최근까지의 연구결과를 바탕으로 해서 측정변수들의 예를 들면 용접 전류(welding current), 아크전압(arc voltage), 음향신호(acoustic signal), 아크 광(arc light) 그리고 온도(temperature)등이 있다. 용접공정을 분석하기 위한 알고 리즘으로는 확률론적 접근(statistical approach), 다양한 실험치를 이용한 인공지능 적 접근(artificial intelligence approach) 그리고 경험치를 바탕으로 인덱스(index) 을 선정하여 이를 직접 사용하는 방법 및 인공지능과 결합된 형태를 이용하는 방법등 이 있다. 또한 용접공정의 특성을 분석하기 위해서는 크게 금속이행모드(metal transfer mode), 아크의 안정성(arc stability) 그리고 용접품질(weld quality) 등을 판별할 수 있는 알고리즘의 개발이 필수적이라 할 수 있다. 본 논문에서는 용접공정 분석과 관련된 최근까지의 연구동향 및 용접신호의 특성을 좀더 심도있게 분석하기 위해 구축해야 할 필수 요건 등을 소개하고자 하며 이를 사용자가 손쉽게 이용할 수 있는 사용자 인터페이스 프로그램을 개괄적으로 설명하고자 한다.

• Journal of Welding and Joining
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• v.13 no.1
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• pp.127-137
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• 1995

The possibility of new GMAW process using economic shielding gases including CO$_{2}$ gas was investigated on the effect of shielding gas on weldabilty. In the optimum welding condition using 600A power source, FCAW process showed low depositions rate, 114 g/min at 300A, but new GMAW using other mixed shielding gases exhibited high deposition rate, 208-224 g/min at 450A. TIME gas, Ar+CO$_{2}$ gas and Ar+CO$_{2}$+O$_{2}$ gas as a shielding gas were able to be used to the very high welding current(450A), moreover TIME gas and Ar+CO$_{2}$ gas showed the highest arc stability among shielding gases studied in this experiments. The weld penetration was performed by axial spray transfer mode of weld droplet. On the basis of workability, weldability and economic point of view, Ar mixture (80%Ar+20%CO$_{2}$) gas was recommended as a shielding gas for the development and application of new GMAW process. This shielding gas showed the low spatter, good weld quality, stable arc and low cost at the region of high welding current.

• Journal of Welding and Joining
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• v.21 no.7
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• pp.27-33
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• 2003

• Journal of Welding and Joining
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• v.18 no.4
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• pp.4-10
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• 2000

• Journal of Welding and Joining
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• v.16 no.3
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• pp.44-54
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• 1998

In this study a new arc sensor algorithm for automatic weld seam tracking was proposed, which uses the relative welding current variation according to the tip-to-workpiece distance in GMA welding. Since the new developed arc sensor algorithm is not sensitive to unstable factors of arc signal, the system is expected to get rid of the problems of already existing arc sensor system which include the difficulty of modeling the process for various welding conditions and limitation of application to thick plate welding. Thus the system is applicable not only to thick plate welding but also to thin plate welding. To implement the new arc sensor algorithm the system parameters which include sampling time, averaging range, weighting factor of moving averaging, basic compensation time, and basic compensation distance were determined by experimental analysis. Consequently this system has shown the successful tracking capability for the various welding conditions.

• Proceedings of the KWS Conference
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• 1995.10a
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• pp.79-83
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• 1995

• Journal of the Society of Naval Architects of Korea
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• v.35 no.4
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• pp.65-75
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• 1998

The $CO_2$ welding with 100% $CO_2$ gas is commonly used because of its cost and efficiency. Arc phenomena and spattering ratio of the $CO_2$ welding are influenced by various factors such as chemical compositions of welding wire, shielding gas, welding condition and welding power source etc.. Spattering ratio is predominantly influenced by the welding condition which determines a droplet transfer rode. In this study, arc phenomena and spattering ratio are investigated by using two type of FCW(titania type, semi-metal type). Then, the welding quality and optimum welding condition can be selected. From this study, the following results ware obtained; 1) In low current range(140A), FCW up to welding voltage(22V) resulted in a typical short circuit transfer, increase of spattering ratio and growth of spatter diameter. 2) In high current range(320A), the arc stability in titania FCW of a typical globular transfer is better than that of semi-metal FCW.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.3
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• pp.272-280
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• 2002

The recent research topics on $CO_2$ arc welding power supplies are focused mainly on the reduction of spatter generation. The method for reducing the spatters can be approached in several ways: finding the new materials fur consumable electrode and shielding gases, researching on the circuit topology and the controller. In this paper, the metal transfer and several control methods for the $CO_2$ GMAW were described. And it was proposed and compared with other schemes that the control method for reducing spatter by controlling the welding current when short-circuit and re-ignition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1326-1331
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• 2006

Weld root gap is a important fact of a falling-off weld quality in various kind of weld defect. The welding quality can be controlled by monitoring important parameters, such as, the Arc voltage, welding current and welding speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using neural networks for detecting and monitoring of weld root gap and bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and detect the welding defects.