• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.51-56
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• 2009

Optimization with metamodel is one of numerical optimization methods. Response surface method is performed for making metamodel. The Hcks-Henne function is used for designing 2D shape of the airfoil and spring analogy is used to change the grid according to the change in shape of the airfoil. Aerodynamic coefficient required for response surface method are obtained by using Navier-Stokes solver with $\kappa-\omega$ shear stress transport turbulence model. For the baseline airfoils, OA 312, OA 309, and OA 407 airfoils select and optimize to improve aerodynamic performance.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1028-1033
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• 2004

The heat and flow characteristics in a single-phase parallel-flow heat exchanger was examined numerically to obtain its optimal shape. A response surface method was introduced to predict its performance approximately with respect to design parameters over design domain. Design parameters are inflow and outflow angle of the working fluid and horizontal and vertical location of inlet and outlet. The evaluation of the relative priority of the design parameters was performed to choose three important parameters in order to use a response surface method. A JF factor was used as an evaluation characteristic value to consider the heat transfer and the pressure drop simultaneously. The JF factor of the optimum model, compared to that of the base model, was increased by about 5.3%.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.10a
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• pp.145-150
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• 2003

The present study aims to optimize the performance of the Two-Stage Gas Gun by using the experimentally obtained data. RSM(Response Surface Method) was adopted in the optimization process to find the operating parameter than can maximize the projectile speed with the minimum number of tests. To decide the test points which results can consist of the response surface, 3$^{k}$ full factorial method was used, and the design variables were chosen with piston mass and 2$^{nd}$ driver fill pressure. The response surface was composed by nine test results and consequently the optimization was done with GENOCOP III, inherently GA code, in order to seek the optimal test point. The optimal test condition from the response surface was verified by the experiment. Results showed that the optimization process with response surface can successfully predict the test results fairly well. This study shows the possibility of performance optimization for the experimental facilities using numerical optimization algorithm.

• IE interfaces
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• v.23 no.3
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• pp.257-263
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• 2010

Taguchi defined a good quality as 'A correspondence of product characteristic's expected value to the objective value satisfying the minimum variance condition.' For his good quality, he suggested Taguchi Method which is called Robust design which is irrelevant to the effect of these noise factors. Taguchi Method which has many success examples and which is used by many manufacturing industry. But Optimal solution of Taguchi Method is one among the experiments which is not optimal area of experiment point. On the other hand, Response Surface Method (RSM) which has advantage to find optimal solution area experiments points by approximate polynomial regression. But Optimal of RSM is depended on initial point and RSM can not use many factors because of a great many experiment. In this paper, we combine the Taguchi Method and the Response Surface Method with each advantage which is called Taguchi-RSM. Taguchi-RSM has two step, first step to find first solution by Taguchi Method, second step to find optimal solution by RSM with initial point as first step solution. We give example using catapults.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.9
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• pp.1314-1319
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• 2004

In this study, parameter optimization of micro-static mixer with a cantilever beam was accomplished for maximizing the mixing efficiency by using successive response surface approximations. Variables were chosen as the length of cantilever beam and the angle between horizontal and the cantilever beam. Sequential approximate optimization method was used to deal with both highly nonlinear and non-smooth characteristics of flow field in a micro-static mixer. Shape optimization problem of a micro-static mixer can be divided into a series of simple subproblems. Approximation to solve the subproblems was performed by response surface approximation, which does not require the sensitivity analysis. To verify the reliability of approximated objective function and the accuracy of it, ANOVA analysis and variables selection method were implemented, respectively. It was verified that successive response surface approximation worked very well and the mixing efficiency was improved very much comparing with the initial shape of a micro-static mixer.

• Journal of the Korean institute of surface engineering
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• v.51 no.5
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• pp.303-308
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• 2018

Currently hydrofluoric acid (HF) based glass etch method is widely used for anti-glare (AG) surface treatment since it can effectively alleviate the specular reflection problem with relatively low processing cost. However, due to the environmental regulation and safety problem, it is essential to develop alternative technology to replace this method. For this, in this paper, we propose sand blasting based AG surface treatment method for display glass. To characterize the sand blasting process, surface roughness, haze, surface durability, and flatness are considered as process outputs and central composite design (CCD) method and response surface model (RSM) method are applied to model each process output. Models for surface roughness and haze showed 96.44% and 97.24% of R-squared values, respectively and they can be applied to optimize AG surface treatment process for various haze level requirements of display industries.

• Korean Management Science Review
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• v.24 no.2
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.869-870
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• 2010

• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.135-141
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• 2008

Independent suspension axle and final reduction gear for special-purpose vehicles such as a armored vehicles are almost imported in Germany etc. so, developing them is necessary to save cost. In severe condition (open fields, water surface driving, obstacle pass), special-purpose vehicles must work well. Drop box, axle and final reduction gear performed static analysis. We know that is possible weight reduction. The purpose of this paper is to find out the optimal shape of final reduction gear's case by means of response surface methodology. The response surface method is the statistical method which can be applied to the non-sensitivity based optimization. The response surface which is constructed by the least square method contains only the polynomial terms so that the global maximum and minimum points are easily obtained.