• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.5
• /
• pp.438-447
• /
• 2008

This study is focused on reliability based design optimization (RBDO) using moving least squares. A response surface is used to derive a limit-state equation for reliability based design optimization. Response surface method (RSM) with least square method (LSM) or Kriging will be used as a response surface. RSM is fast to make the response surface. On the other hand, RSM has disadvantage to make the response surface of nonlinear equation. Kriging can make the response surface in nonlinear equation precisely but needs considerable amount of computations. The moving least square method (MLSM) is made of both methods (RSM with LSM+Kriging). Numerical results by MLSM are compared with those by LMS in Rosenbrock function and six-hump carmel back function. The RBDO of engine duct of smart UAV is pursued in this paper. It is proved that RBDO is useful tool for aerospace structural optimal design problems.

• Transactions of Materials Processing
• /
• v.30 no.1
• /
• pp.27-34
• /
• 2021

In this study, response surface method (RSM) was used in modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goals of optimization were the maximum forming angle, minimum thickness reduction, and minimum surface roughness, with varying values in response to changes in production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model for modeling the variations in the forming angle, thickness reduction, and surface roughness in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process based on experimental design. The results showed that RSM can be effectively used to control the forming angle, thickness reduction, and surface roughness.

• The KSFM Journal of Fluid Machinery
• /
• v.16 no.4
• /
• pp.22-27
• /
• 2013

The purpose of this paper is to perform the structural design of the small scale vertical-axis wind turbine (VAWT) blade using a response surface method(RSM). First, the four design factors that have a strong influence on the structural response of blade were selected. Analysis conditions were calculated by using the central composite design(CCD), which is a typical design of experiment for the response surface method(RSM). Also, the significance of the central composite design(CCD) was verified using analysis of variance(ANOVA). The finite element analysis was performed for the selected analytical conditions for the application of response surface method(RSM). Finally, a optimization problem was solved with a objective function of blade weight and a constraint of allowable stress to achieve a optimal structural design of blade.

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

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.55 no.2
• /
• pp.69-75
• /
• 2006

This paper deals with the optimum design solution on reduction of torque ripple for a Synchronous Reluctance Motor with concentrated winding using response surface methodology. The coupled Finite Elements Analysis (FEA) & Preisach model have been used to evaluate the nonlinear solution. Comparisons are given with characteristics of a SynRM according to the stator winding, slot number, open width of slot, slot depth, teeth width variation in concentrated winding SynRM, respectively. This paper presents an optimization procedure using Response Surface Methodology (RSM) to determine design parameters for reducing torque ripple. RSM has been achieved to use the experimental design method in combination with finite Element Method (FEM) and well adapted to make analytical model for a complex problem considering a lot of interaction of design variables. Moreover, Sequential Quadratic Problem (SQP) method is used to solve the resulting of constrained nonlinear optimization problem.

• International Journal of Automotive Technology
• /
• v.6 no.6
• /
• pp.643-655
• /
• 2005

The frontal crash optimization of S-shaped closed-hat section member using the homogenization method, design of experiment (DOE) and response surface method (RSM) was studied. The optimization to effectively absorb more crash energy was studied to introduce the reinforcement design. The main focus of design was to decide the optimum size and thickness of reinforcement. In this study, the location of reinforcement was decided by homogenization method. Also, the effective size and thickness of reinforcements was studied by design of experiments and response surface method. The effects of various impact velocity for reinforcement design were researched. The high impact velocity reinforcement design showed to absorb the more crash energy than low velocities design. The effect of size and thickness of reinforcement was studied and the sensitivity of size and thickness was different according to base thickness of model. The optimum size and thickness of the reinforcement has shown a direct proportion to the thickness of base model. Also, the thicker the base model was, the effect of optimization using reinforcement was the bigger. The trend curve for effective size and thickness of reinforcement using response surface method was obtained. The predicted size and thickness of reinforcement by RSM were compared with results of DOE. The results of a specific dynamic mean crushing loads for the predicted design by RSM were shown the small difference with the predicted results by RSM and DOE. These trend curves can be used as a basic guideline to find the optimum reinforcement design for S-shaped member.

• Food Science of Animal Resources
• /
• v.43 no.2
• /
• pp.374-381
• /
• 2023

In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.

• Proceedings of the KIEE Conference
• /
• 2002.07b
• /
• pp.964-966
• /
• 2002

This paper presents a magnet circuit design procedure to reduce mover mass of the moving coil type linear direct motor (LDM). The procedure of optimization is based on the response surface methodology (RSM) and Sequential Quadratic Problem (SQP). This procedure of optimization is verified by the comparison of the result of the initial design between the result of the optimum design.

• Proceedings of the KSME Conference
• /
• 2001.06c
• /
• pp.580-585
• /
• 2001

A response surface method(RSM) is utilized for structural optimization and implemented on a parametric CAD platform. Once an approximation of the performance function is made, no formal design sensitivity analysis is necessary. The approximation gives the designer the sensitivity information and furthermore intuition on the performance functions. The scheme for the design of experiment chosen for the RSM has a large influence on the accuracy of converged solutions and the amount of computation. The D-optimal design criterion as implemented in this paper is found efficient for the structural optimization. The program is developed on a parametric CAD platform and tested using several shape design problems of such as a torque arm and a belt clip. It is observed that the RSM used provides a faster convergence than other approximation methods for design sensitivity.

• Journal of the Korean Society of Safety
• /
• v.30 no.1
• /
• pp.8-13
• /
• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.