• 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.

• 한국축산식품학회지
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• 제43권2호
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• pp.374-381
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• 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.

• 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.

• 소성∙가공
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• 제30권3호
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the 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 and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• 한국철도학회:학술대회논문집
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• 한국철도학회 1999년도 추계학술대회 논문집
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• pp.566-573
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• 1999

This paper is the result of sensitivity analysis of derailment with respect to the selected suspension elements for the rail vehicle. Derailment phenominon has been explained by the derailment quotient. Thus, the sensitivity of derailment is suggested by a response surface model(RSM) which is a functional relationship between derailment quotient and characteristics of suspension elements. To summarize generation of RSM, we can introduce the procedure of sensitivity analysis as follows. First, to form a RSM, a experiment is performed by a dynamic analysis code, VAMPIRE according to a kind of the design of experiments(DOE). Second, RSM is constructed to a 1$\^$st/ order polynomial and then main effect fators are screened through the stepwise regression. Finally, we can see the sensitivity level through the RSM which only consists of the main effect factors and is expressed by the liner, interaction and quadratic effect terms.

• 한국안전학회지
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• 제30권1호
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• pp.8-13
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• 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.

• 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.

• 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.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.69-75
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• 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 Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.