• 산업공학
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• 제23권3호
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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.

• 한국강구조학회 논문집
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• 제20권1호
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• pp.21-31
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• 2008

응답면기법(RSM, Response surface method)은 복잡한 구조물의 매우 작은 발생확률이나 신뢰성해 석에서 폭넓게 사용된다. MCS(Monte-Carlo Simulation)방법은 어떤 시스템의 평가에서도 사용될 수 있으나 해석시간이 파괴확률의 역수에 비례하게 되어 발생확률이 매우 희박한 시스템의 평가에 불리하다. 확률유한요소해석법은 이러한 MCS의 한계점을 해결해 줄 수 있는 대안이 될 수 있다. 그러나 이 방법은 평균과 표준편차 등이 모델링 (내부 프로그래밍)된 특별한 프로그램에서만 적용 가능하며 임의의 범용소프트웨어의 응답을 모델링하거나 임의의 프로그램의 특성을 이용할 수가 없다. RSM방법은 복잡한 구조시스템에서 응답에 대한 회귀모델을 구성하여 효율적인 해석단계를 통해 시간과 노력을 획기적으로 절감시킬 수 있다. 그러나 RSM의 정확도는 한계상태방정식의 선형성과 축점간의 거리에 영향을 받게 된다. 이런 단점을 해결하기 위해 한계상태방정식의 선형성과 무관하게 정확한 수렴해를 구하기 위한 개선된 적응적 응답면기법을 개발하고 선형과 2차형식의 응답면방정식에 대한 2가지 예를 들어 검증하였다. 검증결과 가장 효율적인 RSM기법을 결정하였다. 개발된 선형적응적가중응답 면기법 (linear adaptive weighted response surface method, LAW-RSM)은 비적응적이거나 비가중형식의 2차 RSM기법에 비해서 정해의 신뢰성지수에 가장 근접한 정확성과 수렴성을 나타낸다.

• 소성∙가공
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• 제30권1호
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• pp.27-34
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• 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.

• 한국항공우주학회지
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• 제36권5호
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• pp.438-447
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• 2008

본 논문에서는 이동최소자승법을 이용한 근사모델을 사용하여 신뢰성 최적설계를 수행하였다. 신뢰성 최적설계의 수행을 위한 반응표면 생성에는 RSM 과 Kriging이 사용될 수 있다. RSM은 계산시간은 빠르나 비선형성이 강한 문제에 약하며 Kriging은 비선형성이 강한 문제에 적용할 수 있으나 계산시간이 오래 걸리는 단점이 있다. 이 두 방법을 보완한 방법인 이동최소자승법(MLSM)을 이용하여 신뢰성 최적설계를 위한 반응표면을 생성하였다. 이동최소자승법을 이용한 신뢰성 최적설계기법은 Rosenbrock function 과 six-hump carmel back function으로 검증하였고 다른 기법과 비교하였다. 이동최소자승법을 이용하여 무인항공기 배기 덕트의 신뢰성 최적설계를 수행하였고 이는 항공우주구조물의 최적설계에 유용할 것으로 보여 진다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1882-1887
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• 2003

The response surface method (RSM) became one of famous meta modeling techniques, however its approximation errors give designers several restrictions. Classical RSM uses the least squares method (LSM) to find the best fitting approximation models from the all given data. This paper discusses how to construct RSM efficiently and accurately using moving least squares method (MLSM) with sensitivity information. In this method, several parameters should be determined during the construction of RSM. Parametric study and optimization for these parameters are performed. Several difficulties during approximation processes are described and numerical examples are demonstrated to verify the efficiency of this method.

• 한국항공운항학회지
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• 제13권3호
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• pp.22-33
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• 2005

Response Surface Method (RSM) constructs approximate response surfaces using sample data from experiments or simulations and finds optimum levels of process variables within the fitted response surfaces of the interest region. It will be necessary to get the most suitable response surface for the accuracy of the optimization. The application of RSM plan experimental designs. The RSM is used in the sequential optimization process. The first goal of this study is to improve the plan of central composite designs of experiments with various locations of axial points. The second is to increase the optimal efficiency applying a modified method to update interest regions.

• Structural Engineering and Mechanics
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• 제72권2호
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• pp.203-216
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• 2019

Reliability analysis of composite structures considering random variation of involved parameters is quite important as composite materials revealed large statistical variations in their mechanical properties. The reliability analysis of such structures by the first order reliability method (FORM) and Monte Carlo Simulation (MCS) based approach involves repetitive evaluations of performance function. The response surface method (RSM) based metamodeling technique has emerged as an effective solution to such problems. In the application of metamodeling for uncertainty quantification and reliability analysis of composite structures; the finite element model is usually formulated by either classical laminate theory or first order shear deformation theory. But such theories show significant error in calculating the structural responses of composite structures. The present study attempted to apply the RSM based MCS for reliability analysis of composite shell structures where the surrogate model is constructed using higher order shear deformation theory (HSDT) of composite structures considering the uncertainties in the material properties, load, ply thickness and radius of curvature of the shell structure. The sensitivity of responses of the shell is also obtained by RSM and finite element method based direct approach to elucidate the advantages of RSM for response sensitivity analysis. The reliability results obtained by the proposed RSM based MCS and FORM are compared with the accurate reliability analysis results obtained by the direct MCS by considering two numerical examples.

• Structural Engineering and Mechanics
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• 제76권6호
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• pp.675-685
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• 2020

As the classical response surface method (RSM), the polynomial RSM is so easy-to-apply that it is widely used in reliability analysis. However, the trade-off of accuracy and efficiency is still a challenge and the "curse of dimension" usually confines RSM to low dimension systems. In this paper, based on the univariate decomposition, the polynomial RSM is executed in a new mode, called as DPRSM. The general form of DPRSM is given and its implementation is designed referring to the classical RSM firstly. Then, in order to balance the accuracy and efficiency of DPRSM, its adaptive order revision around the most probable point (MPP) is proposed by introducing the univariate polynomial order analysis, noted as RDPRSM, which can analyze the exact nonlinearity of the limit state surface in the region around MPP. For testing the proposed techniques, several numerical examples are studied in detail, and the results indicate that DPRSM with low order can obtain similar results to the classical RSM, DPRSM with high order can obtain more precision with a large efficiency loss; RDPRSM can perform a good balance between accuracy and efficiency and preserve the good robustness property meanwhile, especially for those problems with high nonlinearity and complex problems; the proposed methods can also give a good performance in the high-dimensional cases.

• 한국유체기계학회 논문집
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• 제16권4호
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• pp.22-27
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• 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.

• Structural Engineering and Mechanics
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• 제49권1호
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• pp.111-128
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• 2014

The stochastic response surface method (SRSM) and the response surface method (RSM) are often used for structural reliability analysis, especially for reliability problems with implicit performance functions. This paper aims to compare these two methods in terms of fitting the performance function, accuracy and efficiency in estimating probability of failure as well as statistical moments of system output response. The computational procedures of two response surface methods are briefly introduced first. Then their capabilities are demonstrated and compared in detail through two examples. The results indicate that the probability of failure mainly reflects the accuracy of the response surface function (RSF) fitting the performance function in the vicinity of the design point, while the statistical moments of system output response reflect the accuracy of the RSF fitting the performance function in the entire space. In addition, the performance function can be well fitted by the SRSM with an optimal order polynomial chaos expansion both in the entire physical and in the independent standard normal spaces. However, it can be only well fitted by the RSM in the vicinity of the design point. For reliability problems involving random variables with approximate normal distributions, such as normal, lognormal, and Gumbel Max distributions, both the probability of failure and statistical moments of system output response can be accurately estimated by the SRSM, whereas the RSM can only produce the probability of failure with a reasonable accuracy.