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조수길,장준용,김지훈,이민욱,최종수,홍섭,이태희 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.4
Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiencyof commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacksbecause there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplifythe algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to selectthe points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowestobjective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundarysampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located withinextremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergenceproperties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.
Statistically weighted maximin distance design
조수길,장준용,박상현,이태희,이민욱 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.11
In a computational experiment, a metamodel, which is an approximation model, is widely used to perform optimization efficiently. The accuracy of a metamodel significantly depends on the way of choosing sample points. This process is known as the design of experiment (DOE). An important property of DOE is space filling that is developed to obtain information evenly on the overall design domain. However, space filling may be ineffective in optimization because this property does not consider output information. The proposed novel sequential DOE places more sample points in the neighborhood of the interested region in terms of optimization. The proposed method employs the weighted distance concept that considers output information. The weighted distance is evaluated through proposed parameters that are obtained from the basic statistical distribution of output information, e.g., probability density or cumulative distribution function, while satisfying space filling.
심해저 환경인자를 고려한 시험집광기 주행장치의 곱분해 기법 기반의 통계 모멘트를 이용한 효율적인 강건 최적설계
조수길(Su-gil Cho),이민욱(Minuk Lee),임우철(Woochul Lim),최종수(Jong-Su Choi),김형우(Hyung Woo Kim),홍섭(Sup Hong),이태희(Tae Hee Lee) 대한기계학회 2011 대한기계학회 춘추학술대회 Vol.2011 No.10
A deep-sea manganese nodule miner consists of 4 parts: pickup device, crusher, disposal device and tracked vehicle. The tracked vehicle is an essential component in the self-propelled mining system moving on soft soil. Performances of the tracked vehicle are influenced by noise factors; track speed, seafloor slope, shear stress, bottom currents and reaction forces of flexible hose. It is necessary to adopt robust design method that improves performances as well as minimize the variation of performances due to noise factors. Robust design optimization using statistical moments based on multiplicative decomposition method, calculate statistical moments using explicit integral of kriging metamodel and probability density functions of noise factors, can accurately and efficiently search for the robust optimum. In this paper, we adopt robust design optimization using statistical moments based on multiplicative decomposition method for test miner tracked vehicle on collecting operation.
조수길(Su-gil Cho),이민욱(Minuk Lee),이태희(Tae Hee Lee) 대한기계학회 2011 대한기계학회 춘추학술대회 Vol.2011 No.4
제품 생산 시 발생하는 제작 공차, 항복강도와 탄성계수와 같은 재료 물성치의 불확실성, 온도나 습도와 같이 시스템에 작용하는 환경인자 등은 시스템의 성능에 영향을 미친다. 강건 최적설계는 이러한 인자들이 시스템에 미치는 영향을 최소화하면서 성능을 개선하는 설계기법으로 최근 많은 연구가 이루어지고 있다. 하지만 때로는 설계에서 여러 인자들의 분포를 고려해야 하기 때문에 막대한 시간과 계산비용이 드는 문제가 있다. 본 논문에서는 이러한 문제점을 개선하기 위하여 곱분해 기법을 이용한 강건 최적설계를 제안한다. 제안된 기법은 설계영역을 크리깅 메타모델로 근사하고 이를 곱분해 기법에 적용함으로써 효율적이고 정확한 강건 최적설계를 수행할 수 있다. 수학 예제와 공학 예제를 통해 제안된 기법의 정확성과 효율성을 보인다. Performance of the system can be affected by variables which are tolerance of manufacture, uncertainty of the material property and environmental factor acting on the system. Robust design optimization has gained much attention in design of products because it can find the best design solution minimizing the variance of response as well as consider distribution of the variables. However, computational burden and accuracy of optimization has been still a challenging problem. In this paper, robust design optimization using multiplicative decomposition method is proposed in order to resolve the difficulties. Because the proposed method calculates mean and variance directly from the kriging metamodel using multiplicative decomposition method, it can search for a robust optimum design accurately as well as efficiently. Several mathematical and engineering examples are used to demonstrate the proposed method.
조수길(Su Kil Cho),이태희(Tae Hee Lee) 대한기계학회 2008 대한기계학회 춘추학술대회 Vol.2008 No.11
Design analysis and computer experiments (DACE) model is widely used to express efficiently the nonlinear responses in the field of engineering design. Kriging model, a DACE model, can approximately replace a simulation model that is very expensive or highly nonlinear. The kriging model is composed of the summation of a global model and a local model representing deviation from global model. The local model is determined by correlation coefficient of the pre-sampled points, where determination of the correct correlation coefficient has an effect on accuracy and robustness of the kriging model. Therefore, robustness of the correlation coefficient is explored with respect to degrees of the global model. Then we propose the range of correlation coefficient to make correct and robust kriging model and the influence of the correlation coefficients on the degrees of global model with respect to the nonlinearity of the pre-sampled responses.