• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.97-108
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• 2018

It has been routine practice in regression analysis to check the validity of the assumed model by the use of regression diagnostics tools. Outliers and influential observations often distort the regression output in an undesired manner. Jang and Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014) proposed a graphical method (called a firework plot) so that there could be an exploratory visualization of the trace of the impact of the possible outliers and influential observations on individual regression coefficients and the overall residual sum of the squares measure. This paper further extends a graphical approach to a multi-response surface methodology problem.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.336-345
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• 2012

In crack growth life, uncertainties are caused by variance of geometry, applied loads and material properties. Therefore, the reliability estimation for these uncertainties is required to keep the robustness of calculated life. The stress intensity factors are the most important variable in crack growth life calculation, but its equation is hard to know for complex geometry, therefore they are processed by the finite element analysis which takes long time. In this paper, the response surface is considered to increase efficiency of the reliability analysis for crack growth life of a turbine wheel. The approximation model of the stress intensity factors is obtained by the regression analysis for FEA data and the response surface of crack growth life is generated for selected factors. The reliability analysis is operated by the Monte Carlo Simulation for the response surface. The results indicate that the response surface could reduce computations that need for reliability analysis for the turbine wheel, which is hard to derive stress intensity factor equation, successfully.

• Composites Research
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• v.17 no.2
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• pp.1-8
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• 2004

The present paper is concerned with the optimum design of taper spring, in which the static spring rate of the fiber-reinforcement composite material spring is fitted to that of the steel leaf spring. The thickness and width of springs were selected as design variables. The object functions of the regression model were obtained through the analysis with a common analytic program. After regression coefficients were calculated to get functions of the regression model, optimal solutions were calculated with DOT. E-glass/epoxy and carbon/epoxy were used as fiber reinforcement materials in the design, which were compared and analyzed with the steel leaf spring. The result of the static spring rates show that optimized composite leaf springs agree with steel leaf spring within 1%.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.7 s.250
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• pp.603-611
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• 2006

This paper presents the response surface optimization method using three-dimensional Wavier-Stokes analysis to optimize the blade shape of an axial flow fan. Reynolds-averaged Wavier-Stokes equations with $k-{\epsilon}$ turbulence model are discretized with finite volume approximations using the unstructured grid. Regression analysis is used for generating response surface, and it is validated by ANOVA and t-statistics. Four geometric variables, i.e., sweep and lean angles at mean and tip respectively were employed to improve the efficiency. The computational results are compared with experimental data and the comparisons show generally good agreements. As a main result of the optimization, the total efficiency was successfully improved. Also, detailed effects of sweep and lean on the axial flow fan are discussed.

• 유체기계공업학회:학술대회논문집
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• 2004.12a
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• pp.578-582
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• 2004

This paper presents the response surface optimization method using three-dimensional Navier-Stokes Analysis to optimize the shape of a axial flow fan. Reynolds-averaged Navier-Stokes equations with k-$\epsilon$ turbulence model are discretized with finite volume approximations. Regression analysis is used for generating response surface, and it is validated by ANOVA. Five geometric variables, i.e., distribution of sweep angle at mean and tip, lean angle at mean and tip, and spanwise location of mean were employed to optimize the efficiency. The computational results are compared with experiment data. As a main result of the optimization, the efficiency was successfully improved.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.661-666
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• 2000

Optimization of the elastic joint of train is performed according to the minimization of ten responses which represent driving safety and ride comfort of train and analyzed by using the each response se surface model from stochastic design of experiments. After the each response surface model is constructed, the main effect and sensitivity analyses are successfully performed by 2nd order approximated regression model as described in this paper. We can get the optimal solutions using by nonlinear programming method such as simplex or interval optimization algorithms. The response surface models and the optimization algorithms are used together to obtain the optimal design of the elastic joint of train. the ten 2nd order polynomial response surface models of the three translational stiffness of the elastic joint (design factors) are constructed by using CCD(Central Composite Design) and the multi-objective optimization is also performed by applying min-max and distance minimization techniques of relative target deviation.

• Food Science and Biotechnology
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• v.18 no.1
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• pp.31-35
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• 2009

Total folate content was determined by microbiological assay using Lactobacillus casei spp. rhamnosis (ATCC 7469) with a 96-well microplate technique. Using roasted peanut and red kidney beans as representative legume samples, response surface methodology (RSM) was supplied to optimize the trienzyme procedures for the determination of folate in legumes. After response surface regression (RSREG), the second-order polynomial equation was fitted to the experimental data. Ridge analysis showed that the optimal digestion times were <2 hr for $Pronase^{(R)}$ and $\alpha$-amylase, and <5 hr for conjugase to obtain maximal folate values for legume samples. This study confirms that established digestion times for cereal products (AOAC Method 2004.05) of 3 for protease and 2 hr for $\alpha$-amylase are applicable to legumes. Conjugase treatment can be reduced to 5 from 16 hr and the conjugase level to 5 from 20 mg per sample, providing significant cost saving.

• Journal of the Korean institute of surface engineering
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• v.40 no.4
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• pp.185-189
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• 2007

A scanning electron microscope (SEM) is a complex system, consisting of many sophisticated components. For a systematic characterization, a $2^4$ full factorial experiment was conducted. The SEM components examined include condenser lens 1 and 2 (denoted as A and B, respectively), and Objective lens (coarse and fine-denoted as C and D respectively). A statistical analysis was conduced to investigate factor effects and variations In response surfaces. Among four factors, main effect analysis revealed that A and D were Identified as the dominant factor. Moreover, B showed conflicting effect against C. The $R^2$ of statistical regression model constructed was about 69.6%. The model generated 3D response surface plots facilitated understanding of complex tactor effects.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.961-966
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• 2011

Circular variables are those that have a period in its range. Their examples include direction of animal migration, and time of drug administration, just to mention a few. Statistical analysis of circular variables is quite different from that of linear variable due to its periodic nature. In this paper, the author proposes new circular regression models using geodesic lines on the surface of the sample space of the response and the predictor variables.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.81-89
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• 2023

The purpose of this study is to demonstrate the potential enhancement of the flavonoid contents from Agastache rugosa, which can be obtained as raw materials for functional products in the food medicine industry by identifying important factors for efficient preparation to save costs and time in terms of economic factors. For this reason, response surface methodology using Box-Behnken design was used to optimize the extraction conditions for the maximum yield of seven major compounds from A. rugosa. The optimum conditions were obtained with an ethanol concentration of 60.0%, a temperature of 50 ℃, and an extraction time of 33.6 min, meaning that the regression analysis fits the experimental data well. Under these conditions, the seven major compounds 1-7 had observed values of 2.169, 2.135, 0.697, 2.485, 0.105, 1.247, and 0.551%, respectively. These results show that the observed values are in good agreement with the predicted values in the regression model. This process for optimization study exhibited a basic protocol for obtaining stable ingredients from A. rugosa that are appropriate for the development of effective functional products.