• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 춘계학술대회 논문집
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• pp.131-134
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• 2004

The hub hole is usually formed with a stretch flanging process followed by a blanking process of a hole. Since the hole is made by blanking, the blanked surface is so rough that the formability in the region is rather poor. The emerging task is to identify the formability of the blanked region in the forming simulation and to relate the criterion to the real forming process by experiments. In this paper, the blanked region of a hole surface is modeled by a defected-edge finite element for stretch flanging simulation. The analysis deals with the level of defect in the blanked region in order to identify the formability in the real process. The analysis provides the formability depending on the level of defect and seeks the way to match the level of defect to that of the real surface. The approach makes the analysis possible to deal with the formability of the high strength steel and predict the fracture at the hole surface during the stretch flanging simulation.

• 전자공학회논문지T
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• 제35T권3호
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• pp.103-112
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• 1998

본 논문에서는 전파경로를 해석할 때 기하광학적 영상법과 전파송출법의 단점을 보완하여 계산속도를 향상시킴은 물론 전파의 입사각과 반사각에 따른 전파경로, 진행파의 수평경로 그리고 반사횟수를 동시에 처리 할 수 있는 알고리즘을 제안하였다. 제안 알고리즘을 활용한 전파예측모델을 제안하고, 반복된 반사에 의해 전파가 진행하는 경우, 임의 지점의 전파경로 손실을 쉽게 계산할 수 있다. 마지막으로 광주광역시 광산구 월곡동에 있는 SK텔레콤 전남지사 주변의 실제 도로 상황을 샘플로 취하여 제안 전파예측 모델을 시뮬레이션하여, 일반적인 타당성을 입증하였다.

• Nuclear Engineering and Technology
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• 제44권3호
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• pp.323-330
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• 2012

The development of data-based models requires uncertainty analysis to explain the accuracy of their predictions. In this paper, an uncertainty analysis of the support vector regression (SVR) model, which is a data-based model, was performed because previous research showed that the SVR method accurately estimates the collapse moments of wall-thinned pipe bends and elbows. The uncertainty analysis method used in this study was an analytic uncertainty analysis method, and estimates with a 95% confidence interval were obtained for 370 test data points. From the results, the prediction interval (PI) was very narrow, which means that the predicted values are quite accurate. Therefore, the proposed SVR method can be used effectively to assess and validate the integrity of the wall-thinned pipe bends and elbows.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.333-336
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• 2002

In many cases, fuzzy modeling has a defect that the design procedure cannot be theoretically justified. To overcome this difficulty, we suggest a new design method for fuzzy model by combining genetic algorithm(GA) and mush set theory. GA, which has the advantages is optimization, and rule base. However, it is some what time consuming, so are introduce rough set theory to the rule reduction procedure. As a result, the decrease of learning time and the considerable rate of rule reduction is achieved without loss of useful information. The preposed algorithm is composed of three stages; First stage is quasi-optimization of fuzzy model using GA(coarse tuning). Next the obtained rule base is reduced by rough set concept(rule reduction). Finally we perform re-optimization of the membership functions by GA(fine tuning). To check the effectiveness of the suggested algorithm, examples for time series prediction are examined.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1995년도 추계학술발표회논문집(2)
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• pp.879-884
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• 1995

The results of performance assessment analyses have shown that the long-lived radionuclides such as I-129 control the potential individual dose impact to the public. I-129 is difficult-to-measure(DTM) in low-level waste because it is non-gamma emitting radionuclides and exists at extremely low concentrations in radioactive waste generated by nuclear reactors. In this study, computer modeling technique to predict release rate of I-129 is developed to provide another tools far performance assessment of land disposal facilities and characteristics of radwaste. Model suggested in this study will give conservative values of I-129 release rate far determination of radwaste characteristics. More detailed approach is implemented to account for release conditions of fuel source-nuclides. 1-131 concentration measured from reactor coolant and released fraction from tramp fuel have dominant roles in calculating release rate of I-129 with fuel defect conditions.

• 한국주조공학회지
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• 제41권6호
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• pp.521-527
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• 2021

본 연구는 고압 다이캐스팅 공정에서 제품 결함을 사전에 예측하기 위한 기계 학습 기반의 공정 관리 모델 개발에 관한 연구이다. 모델은 이전 사이클에서의 온도를 입력받고, 사이클에 걸쳐서 나타나는 특징을 인식하여 다음 사이클의 결함 발생 여부를 예측한다. 기어 박스 형상에 대하여 제안된 알고리즘을 적용하여, 3 사이클의 정보를 통해서 98 .9%의 정확도와 96.8 %의 재현율로 제품 수축 결함을 사전에 예측하였다.

• 실천공학교육논문지
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• 제15권2호
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• pp.419-427
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• 2023

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.

• 대한기계학회논문집A
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• 제26권10호
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• pp.1971-1981
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• 2002

Two-dimensional solidification analysis during rheology forming process of semi-solid aluminum alloy has been studied. Two-phase flow model to investigate the velocity field and temperature distribution is proposed. The proposed mathematical model is applied to the die shape of the two types. To calculate the velocities and temperature fields during rheology forming process, the each governing equations correspondent to the liquid and solid region are adapted. Therefore, each numerical model considering the solid and liquid coexisting region within the semi-solid material have been developed to predict the defects of rheology forming parts. The Arbitrary Boundary Maker And Cell(ABMAC) method is employed to solve the two-Phase flow model of the Navier-Stokes equation. Theoretical model basis of the two-phase flow model is the mixture rule of solid and liquid phases. This approach is based on using the liquid and solid viscosity. The Liquid viscosity is pure liquid state value, however solid viscosity is considered as a function of the shear rate, solid fraction and power law curves.

• 한국기계가공학회지
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• 제12권6호
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• pp.69-76
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• 2013

This paper presents the development of the FCF method for the manufacturing of final products using numbers related to the minimum amount of work. The utilized product is a drum clutch, which is part of the transmission of an automobile. A double acting press is secured first and a prediction of the forming load on the practical material is made through an experiment with a plasticine model. Also, a finite element simulation using product shape and properties is performed, as well as a press experiment. A double acting press is manufactured that is suitable for a double acting experiment with a conventional hydraulic press(200 tons). A peripheral device for the press is additionally designed for experimental purposes. And, the press has as its essential points the drive speed, stroke control, etc., all of which influence the forming and is modified. Especially, a laser system is used for velocity measurement of two punches. The forming load of a practical material is predicted in order to derive a forming load formula for cold conditions on the basis of approximate similarity theory. Finite element analysis of the relative velocity ratio(RVR), etc., for most suitable flow defect(unfilling, etc.) prevention is achieved as well. The results are verified through a press experiment.

• Journal of Magnetics
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• 제22권1호
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.