• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 춘계학술발표회논문집(2)
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• pp.210-215
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• 1998

본 연구는 핫셀(Hot-cell)에서의 활용을 전제로 핵연료 봉단용접기술로 개발되고 있는 래이저(Laser) 용접기술을 핵연료봉 퍼복재인 Zircaloy-4에 적용하여 그 용접성에 대한 기초적 특성을 분석하고, 관련 용접변수들의 용접성에 미치는 영향을 알아보고자 하였다. 사용된 용접기는 평균출력 150W급인 펄스형 Nd:YAG 레이저 용접기였으며, 보호가스 (shielding gas) 종류와 유량(flow rate), 용접속도(travel speed), 초점위치(focus position), 빔 파워(beam power), 시편의 표면거칠기(specimen surface roughness) 등의 용접변수가 용입 깊이와 용접비드 폭, 기계적 특성, 그리고 용접결함에 미치는 영향을 조사하였다. 그 결과 용접변수로 범 파워 125W이상, 초점위치 2mm, 그리고 보호가스로는 He가스가 적절하였으며, 시편의 표면거칠기가 거칠수록 용입깊이가 깊었다. 본 연구를 통해 핵연료봉 피복재 Zircaloy-4의 레이저 용접시 신뢰성 있는 용접조건을 확립하기 위한 기초자료를 얻을 수 있었다.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1998년도 특별강연 및 춘계학술발표 개요집
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• pp.150-154
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• 1998

The purpose of this paper is to investigate optimum welding conditions of STS 304 thin sheet by GTA welding and control 6 $\delta$--fenite which is harmful in mechanical processing, corrosion problem and can be formed brittle a phase in using long term at high temperature. One series of automatic welds was made using argon plus 10, 20, 30 % nitrogen to ensure a fully austenite deposit. Results obtained were summarized as follows: 1) 6 $\sigma$ferrite content in the weld metals is influenced largely by the nitrogen content. 2) Additions of nitrogen to the shielding gas can significantly reduce the amount of retained delta ferrite and result in an increase in hot cracking. 3) Bead width was increased when Ar + $N_2$ shielding gas was used and travel speed was increased. 4) Ar+$N_2$ shielding gas made weld metal ductile and reduce 6 -$\delta$-ferrite.

• Journal of Welding and Joining
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• 제18권4호
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• pp.48-54
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• 2000

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor information about weld characteristics and process parameters as well; as t modify those parameters to hold weld. The objectives of this paper are to realize the mapping characteristics of bead width through the neural network and multiple regression method as well as to select the most accurate model in order to control the weld quality(bead width0. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 춘계학술대회 논문집
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• pp.365-372
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• 2004

1)고강도강 BOP 시험 결과 강종에 차이 없이 용접속도에 따라 유사한 비드 형상을 가지는 것으로 나타났다. 한편 1.2t 시험편의 적정 용접속도는 $4\~8m/min$으로 나타났다. 2) 용접속도가 빠를수록 비드폭은 작아지며, 경도값은 높게 나타났다. 그리고, 최고 경도값은 강의 성분(Ceq)에 비례하는 것으로 나타났음, 그 결과 TRIP80(Ceq=0.53)의 경우 최고 520Hv이상의 경도값을 가지고 있었다. 3) LDH test 결과 DP강의 모재대비 용접부의 성형성이 약 $90\%$ 정도로 나타났으며, TRIP 및 일반 고강도강은 약 $80\%$의 성형성을 갖는 것으로 확인되었다. 4) He을 보호가스로 사용시 기공의 형성은 크게 억제되었다. 그리고 Ar 사용시에는 기공 형성이 He에 비해 많이 형성되는 것으로 나타나 사용시 주의가 필요한 것으로 보인다. 기공 형성은 TRIP > 60C/45R > DP 순서로 나타나 DP강이 가장 좋은 레이저 용접성을 가지고 있었다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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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.

• 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.

• 한국정밀공학회지
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• 제32권2호
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• pp.209-217
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• 2015

Generally welding is one of the most important processes to have a strong influence on the quality and productivity from a manufacture-based industry such as shipbuilding, automotive and machinery. The GMA(Gas Metal Arc) welding process involves large number of interdependent welding parameters which may affect product quality, productivity and cost effectiveness. To solve such problems, mathematical models are required to select the welding parameters for GMA welding process. In this study, the GMA welding process was studied using the information generated during the welding. The statistical analysis of a generalized regression approach was conducted by the following three methods: Firstly using the mathematical model (linear regression, 2nd regression); Secondly GA(Genetic Algorithm) with intelligent models; And finally using response surface analysis of models to develop the relationships between welding parameters and bead width as welding quality.

• Journal of Welding and Joining
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• 제25권6호
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• pp.64-70
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• 2007

Recently, several models to control weld quality, productivity and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead width as well as apply for the on-line quality control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, the system will extend to other welding process and the rule-based expert system which can be incorporated with integration of an optimized system for the robotic welding system.

• 대한기계학회논문집A
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• 제34권10호
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• pp.1507-1512
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• 2010

본 연구에서는 조선용 6mm 아연코팅강판의 $CO_2$ 레이저 겹치기 용접시 발생하는 유기 플라즈마를 마이크로폰과 포토다이오드로 측정하였다. 이때 겹치기 갭간극에 따른 용접조건을 RMS한 신호와 비교 분석하였다. 이를 통해 아연증발량이 증가함에 따라 RMS값도 증가하였으며, 겹침부의 조건에 따라 결함 발생시 RMS의 급격한 변화도 확인할 수 있었다. 또한 용접조건에 따른 Raw signal의 FFT값을 구한 후, 이때 구해진 주파수값을 밴드폭으로 설정하여 Raw signal을 필터링한 뒤의 RMS값을 용접비드와 대응하여 필터링하지 않은 RMS와의 차이점도 비교 분석하였다. 이를 통해 기존의 방법들보다 신뢰성 높은 In-process 모니터링이 가능함을 확인하였다.

• 한국생산제조학회지
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• 제25권1호
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• pp.30-35
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• 2016

Tandem arc welding is a guarantor for high efficiency and cost saving since the quantity of wire which is deposited in the welding is approximated 30% greater that in conventional welding. The welding process is now being successfully applied in many industries. However, in the case of tandem arc welding, good quality and high productivity should depend on the welding parameters. Therefore, an intelligent algorithms for the automatic tandem arc welding process has been necessarily required. In this study, a predictive model based on the neural network by using the data acquired during tandem gas metal arc (GMA) welding process has been developed. To verify the reliability of the developed predictive model, a mutual comparison with the surface of the top-bead width obtained from actual experiments has been analyzed.