• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.1101-1104
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• 1995

Through welding fabrication, user can feel an surficaial and capable unsatisfaction because of welded defects, Generally speaking, these are called weld defects. For checking these defects effectively without time loss effectively, weldability estimation system setup isan urgent thing for detecting whole specimen quality. In this study, by laser vision camera, catching a rawdata on welded specimen profiles, treating vision processing with these data, qualititative defects are estimated from getting these information at first. At the same time, for detecting quantitative defects, whole specimen weldability estimation is pursued by multifeature pattern recognition, which is a kind of fuzzy pattern recognition. For user friendly, by weldability estimation results are shown each profiles, final reports and visual graphics method, user can easily determined weldability. By applying these system to welding fabrication, these technologies are contribution to on-line weldability estimation.

• 한국공작기계학회논문집
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• 제16권4호
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• pp.22-28
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• 2007

The welded defects are usually called user's unsatisfaction for appearance and functional usage. For checking these defects effectively without time loss, setup of weldability estimation system is an important for detecting whole specimen quality. In this study, after catching a rawdata on welded specimen profiles and treating vision processing with these data, the qualitative defects are estimated from getting these information by laser vision camera at first. At the same time, the weldability estimation for whole specimen is produced. For user friendly, the weldability estimation results are shown each profiles, final reports and visual graphics method. So, user can easily determined weldability. By applying these system to welding fabrication, these technologies are contribution to on-line setup of weldability estimation system.

• 한국산학기술학회논문지
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• 제9권3호
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• pp.605-610
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• 2008

용접구조물의 시공시 발견되는 외관상, 성능상 사용자에게 불만족을 발생시키는 결함들을 일반적으로 총칭하여 용접부의 결함이라 한다. 이들 결함들을 시간의 손실 없이 효과적으로 검출해내어 용접부 전체의 품질을 효과적으로 판정하기 위한 용접성 평가 시스템 구축이 어느 때보다 절실하다. 본 연구에서는 레이저 비전 카메라를 이용하여, 용접부의 형상에 대한 원 데이터를 채취하고 이를 화상처리하고, 여기서 얻은 정보로 1차적으로 정량적 결함을 판별하고, 동시에 분류된 결함을 정성적으로 판단하기 위해 퍼지 패턴인식의 한 단계인 다중 패턴 인식을 이용하여 용접 물 전체의 결함을 판정하였으며, 사용자의 편의를 위해 용접성의 결과가 화상별, 최종집계와 함께 이를 그래픽의 형태로 표시하여, 손쉽게 용접성 양부를 결정할 수 있게 하였으며, 이는 또한 생산라인에 적용하여 실시간 용접성 판별을 할 수 있는 기반 연구 성과를 제시하였다.

• 한국공작기계학회논문집
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• 제16권1호
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• pp.60-68
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• 2007

It has been conducted by laser vision sensor for weldability estimation of front-bead after doing high speed butt laser welding of any condition. It has been developed a real time GUI(Graphic User Interface) system for weldability application in the basis of texts and field qualify levels. In the reference of bead imperfections, defects absolute position and defects intensity index of front-bead in the basis of formability reference, it has been produced a weldability estimation and defects intensity index of back-bead by back propagation neural network. In the result of by comparing measuring data by laser vision sensor of back-bead and data by back propagation neural network of one, it has been shown the similar results. Finally, under knowledge of welding condition in production line, it has been conducted a weldability estimation of back-bead only in knowledge of informations of front-bead data without using laser vision sensor or welding inspection experts and furthermore it can be used data for final inspection results of back-bead.

• Journal of Welding and Joining
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• 제16권5호
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• pp.76-85
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• 1998

The resistance spot welding process has been used for joining the sheet metal in automotive engineering. In the resistance spot welding, the weld quality is very important, because the quality of weld is one of the most important factors to the automobile quality. The size of he molten nugget has been utilized to estimate the weld quality. However, it is not easy to find the weld defects. For weldability estimation, we have to use the nondestructive method such as X-ray or ultrasonic inspection. But these kinds of approaches are not suitable for detecting the defects in real time. The purpose of this study is to develop the real time monitoring of the weld quality in the resistance spot welding. Obtained data were used to estimate weldability using fuzzy algorithm. It is sound that this monitoring and estimation system can be useful to improve the weld quality in the resistance spot welding process and it is possible to estimate the weldability in real time.

• 한국정밀공학회지
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• 제22권9호
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• pp.49-59
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• 2005

This paper describes a development of the extrusion process and estimation of the weldability for multi cell tubes used to cooling system of automobiles. A study on extrusion process is performed through the 3D FE simulation in non-steady state and extrusion experimentation. Also, nano-indentation test is employed to estimate the weldability of tubes. Especially, An evaluation of the weldability using the nano-indentation is accomplished as compared with nano-hardness in welded part and in the others. Finally, the pattern of the mandrel defection is investigated according to shapes of the porthole and/or chamber.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1997년도 특별강연 및 추계학술발표 개요집
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• pp.176-178
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• 1997

• 한국산학기술학회논문지
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• 제12권3호
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• pp.1070-1077
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• 2011

본 논문은 초음파 가공에 의한 용착성을 가공조건의 영향에 관하여 나타내었다. 한파장 혼의 최적화를 이용한 Ni-Cu 이종금속 박판의 용착성 평가는 초음파 가공 방법을 이용하여 확인된다. 초음파 가공변수 설정에 따른 인장시험을 통한 최적의 용착조건을 제시하였으며 SEM 사진과 EDX-ray 분석에 의한 용착성을 평가하였다. 실험적 연구는 초음파 가공 후 인장강도의 측정, SEM사진 분석으로 수행된다. 또한 가공시간, 가압력, 진폭의 가공변수들은 본 연구에 적용되었다.

• 한국공작기계학회논문집
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• 제16권4호
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• pp.79-86
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• 2007

The shape of excessive penetration mainly depends on welding conditions(welding current and welding voltage), and welding process(groove gap and welding speed). These conditions are the major affecting factors to width and height of back bead. In this paper, back-bead prediction and weldability estimation using artificial neural network were investigated. Results are as follows. 1) If groove gap, welding current, welding voltage and welding speed will be previously determined as a welding condition, width and height of back bead can be predicted by artificial neural network system without experimental measurement. 2) From the result applied to three weld quality levels(ISO 5817), both experimented measurement using vision sensor and predicted mean values by artificial neural network showed good agreement. 3) The width and height of back bead are proportional to groove gap, welding current and welding voltage, but welding speed. is not.

• Journal of Welding and Joining
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• 제23권4호
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• pp.59-65
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• 2005

The resistance spot welding is one of the most commonly used welding process for joining the sheet metal in automotive manufacturing process due to higher deposition rates and higher economy achieved. Control variables in the resistance spot welding for achieving high quality are welding current, welding force and welding time. Generally at the manufacturing scene, welding current Vs welding time lobe diagram is used to estimate weldability because controlling welding force is not practical due to economical reasons. However new automotive steel plates have been developed to make lightweight automobiles and to improve resistance against rusting. Also the weldability of these steel plates are worst than the existing steel plates because of changing bare metal and surface plating effect. In consequence of above mentioned reasons, it is necessary to use welding force to present the lobe diagram. In this study, we obtained the welding force Vs welding current lobe diagram for commonly used GA steel plate and found that the second order repression model of tensile shear strength was useful in reducing the number of experiments, and the indentation, and thickness change during welding were used as a response to estimate quantitatively expulsion.