• 대한기계학회논문집A
• /
• 제29권9호
• /
• pp.1243-1252
• /
• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 대한기계학회논문집A
• /
• 제30권7호
• /
• pp.787-793
• /
• 2006

Recently kriging model has been widely used in the DACE (Design and Analysis of Computer Experiment) because of prominent predictability of nonlinear response. Since DACE has no random or measurement errors contrast to physical experiment, space filling experimental design that distributes uniformly design points over whole design space should be employed as a sampling method. In this paper, we examine the maximum entropy experimental design that reveals the space filling strategy in which defines the maximum entropy based on Gaussian or exponential. The influence of these two correlation functions on space filling design and their model parameters are investigated. Based on the exploration of numerous numerical tests, enhanced maximum entropy design based on exponential correlation function is suggested.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2004년도 추계학술대회
• /
• pp.864-869
• /
• 2004

It is net easy to predict the shrinkage rate of a plastic injection mold in its design process. The shrinkage rate should be considered as one of the important performances to produce the reliable products. The shrinkage rate can be determined by suing the CAE tools in the design produces. However, since the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. In this study, the surrogate models based on the DACE is used in lien of the original models, facilitating design optimization.

• 한국소음진동공학회논문집
• /
• 제15권10호
• /
• pp.1123-1130
• /
• 2005

We propose an optimal design to improve the capacity by reducing the noise of the intake system. The length and radius of each component of the current Intake system are selected as control factors. We accept the output from computer simulator with orthogonal arrays. Then, the Kriging estimates are computed. From this, we exploit the optimal design of the intake system by adapting simulated annealing. From the results of this optimal design, we conclude that Kriging method with the orthogonal arrays is the efficient method for Design and Analysis of Computer Experiments and we propose the useful results for the low noise intake system.

• 대한기계학회논문집A
• /
• 제33권8호
• /
• pp.813-818
• /
• 2009

Design analysis and computer experiments (DACE) model is widely used to express efficiently nonlinear responses in the field of engineering design. As a DACE model, kriging 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 the global model. The local model is determined by correlation coefficient with the pre-sampled points, where the accuracy and robustness of the kriging model depends on the selection of proper correlation coefficients. Therefore, to achieve the robust kriging model, the range of the correlation coefficients is explored with respect to the degrees of the global model. Based on this study we propose the proper orders of the global model and range of parameters to make accurate and robust kriging model.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2008년도 추계학술대회A
• /
• pp.701-705
• /
• 2008

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.

• 한국정밀공학회지
• /
• 제22권7호
• /
• pp.177-184
• /
• 2005

Recently, the engineering designer of injection mould has become more and more dependent on the CAE. In the design factors of injection mould, the shrinkage rate should be considered as one of the important performances to produce the reliable products. therefore the shrinkage rate can be mostly calculated by the MoldFlow and Pro-engineering. in the design process. However it is not easy to predict the shrinkage rate of a plastic injection mold in its design process because the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. In this study, the surrogate models, DACE model, based on the Kriging in order to optimize the shrinkage rate of electric microwave oven window is used in lieu of the original models, facilitating design optimization.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2004년도 춘계학술대회
• /
• pp.1035-1040
• /
• 2004

In the proto design stage of a new car, the performance of an occupant protection system is often evaluated by CAE instead of the real test. CAE predicts and recommends the appropriate design values hence reducing the number of the real tests. However, the existing researches using CAE in predicting the performances do not consider the uncertainties of parameters, in which inconsistency between the actual test results and CAE exists. In this research, the optimization procedure of a protection system such as airbag and load limiter is suggested for the frontal collision. The DACE modeling known as Kriging interpolation is introduced to obtain the meta model of the system followed by the tabu search method to determine a global optimum. Finally, the distribution of a suggested design is determined through the Monte-Carlo Simulation.

• 한국수의병리학회:학술대회논문집
• /
• 한국수의병리학회 2005년도 Proceedings of The 2nd Asian Society of Veterinary Pathology Symposium(Vol.2) and 2005 Annual Meeting of The Korean Society of Veterinary Pathology(Vol.9)
• /
• pp.85-85
• /
• 2005

• 한국소음진동공학회논문집
• /
• 제19권5호
• /
• pp.509-515
• /
• 2009

This paper proposes an optimal design scheme to reduce the noise of the engine cooling fan by adapting Kriging with two meta-heuristic techniques. An engineering model has been developed for the prediction of the noise spectrum of the engine cooling fan. The noise of the fan is expressed as the discrete frequency noise peaks at the BPF and its harmonics and line spectrum at the broad band by noise generation mechanisms. The object of this paper is to find the optimal design for noise reduction of the engine cooling fan. We firstly show a comparison of the measured and calculated noise spectra of the fan for the validation of the noise prediction program. Orthogonal array is applied as design of experiments because it is suitable for Kriging. With these simulated data, we can estimate a correlation parameter of Kriging by solving the nonlinear problem with genetic algorithm and find an optimal level for the noise reduction of the cooling fan by optimizing Kriging estimates with simulated annealing. We notice that this optimal design scheme gives noticeable results. Therefore, an optimal design for the cooling fan is proposed by reducing the noise of its system.