• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1999년도 춘계학술대회논문집
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• pp.62-65
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• 1999

To reduce the cost of finite element analyses for sheet forming a 3D hybrid membrance/sheel method has been developed to study the springback of anisotropic sheet metals. in the hybrid method the bending strains and stresses were analytically calculated as post-processing using incremental shapes of the sheet obtained previously from the membrane finite element analysis. To calculate springback a shell finite element model was used to unload the final shape of the sheet obtained from the membran code and the stresses and strains that were calculated analytically. For verification the hybrid method was applied to predict the springback of a 2036-T4 aluminum square blank formed into a cylindrical cup. the springback predictions obtained with the hybrid method was in good agreement with results obtained using a full shell model to simulateboth loading an unloading and the experimentally measured data. The CPU time saving with the hybrid method over the full shell model was 75% for the punch stretching problem.

• 대한기계학회논문집A
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• 제36권2호
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• pp.179-186
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• 2012

자동차 산업에서 대기오염을 줄이고 연비를 향상시키기 위해 경량화가 중요한 과제로 여겨지고 있다. 이를 위해 알루미늄 소재의 적용이 증가하고 있다. 판재를 차체에 적용하기 위해서는 주로 프레스 가공 공정을 거치게 된다. 이때, 재료, 제품설계 및 프레스 공정의 부적절한 가공 변수의 사용으로 인하여 파단, 주름, 및 스프링 백 등에 의한 다양한 형태의 가공 불량이 발생한다. 따라서 이들 변수들의 적절한 조화 뿐 만 아니라 엄격한 공정 관리가 요구된다. 이에 본 연구에서는 자동차 판재에 주로 사용되는 Al5454 재료에 대한 이론적으로 유도한 소성 불안정 조건을 구하고, MATLAB을 이용하여 성형 한계도를 도출하였다. 또한, 장출 인장 실험을 통해 얻어진 실험값과 이론적으로 도출한 성형 한계도와의 비교를 수행하였다.

• 대한기계학회논문집A
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• 제37권10호
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• pp.1239-1249
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• 2013

본 연구에서는 zircaloy-4 판재에 대해 바우싱거 효과를 고려한 경화거동 예측모델을 구축했다. 금속소재 가공에서 인장 후 압축 시 항복응력이 감소한다. 이에 스프링백 해석 시 바우싱거 효과를 반드시 고려해야 한다. Simple shear 시험에서 적정 시편크기 및 적정 조임토크에 대한 결정법을 제시했다. 5 가지 재료에 대한 simple shear 시험을 통해 응력-변형률 곡선을 구했다. 또한 유한요소해석을 활용해 simple shear 하중-변위 곡선으로부터 유효응력-변형률 곡선으로 변환과정을 소개했다. 등방/운동성 경화 조합모델을 활용해 simple shear 순/역방향 시험을 모사했다. 이때 각 경화계수에 따른 하중-변위 곡선 변화를 관찰하고, zircaloy-4에 대한 경화계수를 결정했다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회 2차
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• pp.21-31
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• 2010

Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to get it practically on considering various geography and geotechnical condition. So construction management of information concept is required to manage immediately on the field condition because it is very time-consuming to establish the countermeasure of underground reinforcement and the pattern change of Bo. Therefore, when construction is on tunnel area, examination of accurate safety and prediction of behavior is performed to overcomes the limit of predicting behavior by using Artificial Neural Network(ANN) in this study. Firstly, the field data was secured. Secondly, suitable structure was made on multi-layer perceptrons among the ANN. Thirdly, learning algorithm-propagated applies to ANN. The data for the learn of field application using ANN was used by considering impact factors, which influenced the behavior of tunnel, and performing credibility analysis. crown displacement, spring displacement, subsurfacement, and rock bolt axial force are predicted at the tunnel construction and on-site application was confirmed by using ANN from analyzing and comparing with measurement value of on-site. In this study, the data from Seoul Highway $\bigcirc\bigcirc$ tunnel section was applied to the ANN Theory, and the analysis on the investigate value and the reasoning for the value associated with field application was performed.