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조건 평균법에 의한 난류 예혼합 화염편 구조와 전파 속도 예측
박영도(Yeongdo Park),허강열(Kang Y. Huh) 한국연소학회 2021 한국연소학회지 Vol.26 No.4
A governing equation is derived from the equation of the reaction progress variable for the conditional flamelet structure and the turbulent burning velocity in turbulent premixed combustion. Direct numerical simulations are conducted for constant density flames to validate the newly derived conditional flamelet equation. Good agreement is shown between the conditional flamelet equation and the flamelet structures and turbulent burning velocities from DNS. They are validated for laboratory flames as well by numerical solutions of the modified laminar premixed flame code in Cantera. Reasonable agreement is shown with a tuning constant for turbulent diffusivity to consider the uncertainty involved in each experimental setup and conditions in the references.
인공신경망을 이용한 저항 점용접 너겟 직경 예측에 관한 연구
김종규(Jongkyu Kim),구자훈(JaHun Ku),박영도(Yeongdo Park),김영창(Youngchang Kim),황영민(Youngmin Hwang),김희수(Heesoo Kim),Siva Prasad Murugan,구남국(Namkug Ku) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.6
Resistance spot welding, which has the advantages of low cost and high productivity, is the most common method used in the automobile industry for joining steel sheets. However, in practice, resistance spot welds are typically tested for welding quality using destructive rather than non-destructive inspection methods because of their lower cost. However, in destructive inspection, quality defects can be found only after the completion of the process. Accordingly, several studies are currently being conducted to predict the quality of welding in real time. Welding quality is determined by the diameter of the nugget, and its size depends on several independent variables. In this study, a linear regression model and artificial neural network model were constructed to predict the nugget diameter. An electric power pattern was obtained from the results of a welding experiment, and nine types of electric power characteristic values were extracted from the obtained electric power pattern as independent variables. From the nine electric power characteristic values, six having the highest correlation with the nugget diameter were determined as final independent variables through correlation analysis. The linear regression model was constructed using multiple linear regression analysis, and the artificial neural network model was built using a deep neural network model with two hidden layers and nodes of 64 and 16. In this study, the error between the actual measured and predicted nugget diameters was taken as 0.2 ㎜ or less as a good predictive value. When the linear regression model was used to predict the nugget diameter, only approximately 36% were predicted well. By contrast, when the artificial neural network was used, approximately 86% were predicted well. Thus, the artificial neural network model yielded better results. It was determined that with more welding data and information on steel types, the proposed welding quality prediction system could be improved.
고강도강의 이종재질 및 두께간 프로젝션용접부 전단파단하중 예측
하상언(Sangun Ha),한기열(Giyeol Han),Siva Prasad Murugan,박영도(Yeongdo Park),이형일(Hyungyil Lee) 대한기계학회 2018 대한기계학회 춘추학술대회 Vol.2018 No.12
This study aims at predicting shear fracture load of the projection weld part, which is welded with dissimilar materials and thickness of high strength steel. After projection welding experiment, lap shear test was performed to obtain shear fracture load. The nugget size, plate thickness and tensile strength of base material were proportional to the shear fracture load. If both upper and lower welded specimen has high tensile strength and thickness, lower shear fracture load was obtained than predicted value and partial interfacial fracture was occurred. The predicted shear fracture load for interfacial fracture and pullout fracture were suggested through dimensional analysis, and the mean error is 5.4 and 6.4 % respectively compared with the experiment. The failure mode was analyzed with the relationship of plate thickness and the nugget size, and the critical nugget size for pullout fracture was suggested as 5.3 m t using the average thickness of the upper and lower welded specimen.
하상언(Sangun Ha),한정무(Jungmu Han),양준호(Junho Yang),박영도(Yeongdo Park),이형일(Hyungyil Lee) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
The aim of this study is to analyze welding behavior of AA3003 in resistance projection welding (RPW). In automobile industry, the demand for aluminum is increasing to reduce the weight of vehicle. However, owing to the high thermal and electrical conductivities of aluminum, welding behavior is different with conventional steels. The RPW is one of resistance welding processes, where a projection is formed on part of a plate to induce melting in a low current area. Although RPW is widely used, the research is limited compared with resistance spot welding, and few researches deal with application of RPW to aluminum. In this paper, the effect of various welding variables on the RPW behavior of aluminum is investigated by constructing 3D finite element (FE) model, which considers heat loss and projection stamping process using Abaqus (6.13ver). Finally, we compare nugget size between welding experiments and FE analyses, and determine welding variables that affect welding behavior.