• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.6
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• pp.29-37
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• 2016

This paper proposes a shape optimization of the metal boss for a composite motor case using finite element analysis. For the structural safety and the weight reduction of the composite motor case, under the internal pressure, the fiber stress in the dome area and the tightening bolt stress are constrained and the boss weight is set to objective function, respectively. The response surface models are constructed for the performance characteristics by using response surface method. The significance of the design variables about the performance characteristics is evaluated through the ANOVA(analysis of variance) and the goodness of fit test for the constructed model is performed through the regression analysis. The SQP(sequential quadratic programming) algorithm is used for the optimization and the proposed method is verified by performing structural analysis for the optimum shape.

• Food Science and Preservation
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• v.10 no.3
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• pp.314-319
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• 2003

This study was conducted to the optimize salting process of eggplant for development new product and enhancement quality for export. Three variables by five level central composite design and response surface methodology were used to determine optimum conditions for salting time, temperature and salt concentration. Optimization of the process was conducted using the combination of the moisture content, salinity and color of surface and inside of salted eggplant. The regression polynomial model was suitable (P>0.05) by Lack-of-Fit analysis with highly significant. To optimize the process, based on surface response and contour plots, the individual contour plots of the response variables were superimposed. The optimum conditions for this process were 6 days and 15$^{\circ}C$ at 30％ concentration under the optimum of restricted variables as moisture content was below 84％, salinity was below 14％, L and b value of surface were 10 to 20 and below 0, L value and b value of inside were 70 to 75 and 16 to 18.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.57-69
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• 2000

This paper discusses the local influence approach to the linear regression models with AR(2) errors. Diagnostics for the linear regression models with AR(2) errors are proposed and developed when simultaneous perturbations of the response vector are allowed- That is, the direction of maximum curvature of local influence analysis is obtained by studying the curvature of a surface associated with the overall discrepancy measure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• Journal of Environmental Science International
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• v.19 no.7
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• pp.889-900
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• 2010

The experimental design and response surface methodology (RSM) have been applied to the investigation of the electro-UV complex process for the disinfection of E. coli in the water. The disinfection reactions of electro-UV process were mathematically described as a function of parameters power ($X_1$), NaCl dosage ($X_2$), initial pH ($X_3$) and disinfection time ($X_4$) being modeled by use of the Box-Behnken technique. The application of RSM using the Box-Behnken technique yielded the following regression equation, which is an empirical relationship between the residual E. coli number and test variables in actual variables: Ln (CFU) = 23.57 - 0.87 power - 1.87 NaCl dosage - 2.13 pH - 2.84 time - 0.09 power time - 0.07 NaCl dosage pH + 0.14 pH time + 0.03 $power^2$ + 0.47 NaCl $dosage^2$ + 0.20 $pH^2$+ 0.33 $time^2$. The model predictions agreed well with the experimentally observed result ($R^2$ = 0.9987). Graphical response surface and contour plots were used to locate the optimum point. The estimated ridge of maximum response and optimal conditions for the E. coli disinfection using canonical analysis was Ln 1.06 CFU (power, 15.40 W; NaCl dosage, 1.95 g/L, pH, 5.94 and time, 4.67 min). To confirm this optimum condition, the obtained number of the residual E. coli after three additional experiments were Ln 1.05, 1.10 and Ln 1.12. These values were within range of 0.62 (95% PI low)~1.50 (95% PI high), which indicated that conforming the reproducibility of the model.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.125-136
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• 2008

In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by real/computational experiments such as structural analysis, fluid mechanic analysis, thermodynamic analysis, and so on. These experiments are, in general, considerably expensive. In order to make the number of these experiments as few as possible, optimization is performed in parallel with predicting the form of objective functions. Response Surface Methods (RSM) are well known along this approach. This paper suggests to apply Support Vector Machines (SVM) for predicting the objective functions. One of most important tasks in this approach is to allocate sample data moderately in order to make the number of experiments as small as possible. It will be shown that the information of support vector can be used effectively to this aim. The effectiveness of our suggested method will be shown through numerical example which is well known in design of engineering.

• Journal of Powder Materials
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• v.29 no.6
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• pp.459-467
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• 2022

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.84-92
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• 1995

A method is proposed for the simultaneous optimization of several response functions that depend on the same set of controllable variables and are adequately represented by a response surface model (polynomial regression model) with the same degree and with constraint that the individual responses have the target values. First, the multiple responses data are checked for linear dependencies among the responses by eigenvalue analysis. Thus a set of responses with no linear functional relationships is used in developing a function that measures the distance estimated responses from the target values. We choose the optimal condition that minimizes this measure. Also, under the different degree of importance two step procedures are proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1265-1271
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• 2014

The lamp cooling system of the exposure has effect on the exposure efficiency and device lifetime. In this paper, we performed the numerical analysis about the thermal flow in the lamp housing of the exposure apparatus for the cooling air inflow rate. We set up the velocity of cooling air of side and bottom as the independent variables because cooling performance of the lamp housing is affected by the velocity of the cooling air side and bottom. The cooling state of lamp housing depend on three dependent variables; the temperature at top mirror and exhaust gas, ellipsoidal mirror. Response surface methodology was used in order to establish the efficient cooling analysis plan. The regression equation predicting the variables temperature of lamp housing according to the cooling air velocity were drawn. The velocity of cooling air to reach the optimum temperature of the lamp housing were derived.

• Journal of the Korean GEO-environmental Society
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• v.12 no.9
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• pp.55-61
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• 2011

This study investigate the use of ultraviolet(UV) light with hydrogen peroxide($H_2O_2$) for Methyl Tert Butyl Ether(MTBE) degradation in photolysis reactor. The process in general demands the generation of OH radicals in solution at the presence of UV light. These radicals can then attack the MTBE molecule and it is finally destroyed or converted into a simple harmless compound. The MTBE removal by photolysis were mathematically described as the independent variables such as irradiation intensity, initial concentration of MTBE and $H_2O_2$/MTBE ratio, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. Regression analysis term of Analysis of Variance(ANOVA) shows significantly p-value(p<0.05) and high coefficients for determination values($R^2$=94.60%) that allow satisfactory prediction of second-order regression model. And Canonical analysis yields the stationery point for response, with the estimate ridge of maximum responses and optimal conditions for Y(MTBE removal efficiency, %) are $x_1$=25.75 W of irradiation intensity, $x_2$=7.69 mg/L of MTBE concentration and $x_3$=11.04 of $H_2O_2$/MTBE molecular ratio, respectively. This study clearly shows that RSM is available tool for optimizing the operating conditions to maximize MTBE removal.