• International Journal of Automotive Technology
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• v.8 no.1
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• pp.93-102
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• 2007

The frontal crash optimization of an engine room member using the response surface method was studied. The engine room member is composed of the front side member and the sub-frame. The thicknesses of the panels on the front side member and the sub-frame were selected as the design variables. The purpose of the optimization was to reduce the weight of the structure, under the constraint that the objective quantity of crash energy is absorbed. The response surface method was used to approximate the crash behavior in mathematical form for optimization procedure. To research the effect of the regression method, two different methodologies were used in constructing the response surface model, the least square method and the moving least square method. The optimum with the two methods was verified by the simulation result. The precision of the surrogate model affected the optimal design. The moving least square method showed better approximation than the least square method. In addition to the deterministic optimization, the reliability-based design optimization using the response surface method was executed to examine the effect of uncertainties in design variables. The requirement for reliability made the optimal structure be heavier than the result of the deterministic optimization. Compared with the deterministic optimum, the optimal design using the reliability-based design optimization showed higher crash energy absorption and little probability of failure in achieving the objective.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.109-116
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• 2002

The solution of the aircraft wing spar cross-section area optimization problem is obtained by the response surface method. The object function of the problem is wing total weight, design variables are spar cross-section areas, constraints are the conditions that the stresses at the each spar is less than the allowable stress. D-Optimal condition is utilized to obtain the experimental points to construct the response surfaces. D-Optimal experimental points are obtained by the commercial software "Deign-Expert". Response values for the object function and constraints for each experimental point are calculated by the NASTRAN. Response surfaces for object function and constraints are approximated from the response values by the least square method. The optimization solution is obtained by the DOT for the response surfaces of object function and constraints. The optimization results obtained from the response surface are compared with the results obtained by the NASTRAN SOL200.

• Transactions of Materials Processing
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• v.30 no.1
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.342-350
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• 2013

Dual response surface optimization (DRSO), inspired by Taguchi's philosophy, attempts to optimize the process mean and variability by using response surface methodology. Researches on DRSO were extensively done in 1990's and have been matured recently. This paper reviews the existing DRSO methods from the decision making perspective. More specifically, this paper classifies the existing DRSO methods based on the optimization criterion and the timing of preference articulation. Also, some of case studies are reviewed. Extension to multiresponse optimization, triple response surface optimization, and application of data mining method are suggested as future research issues.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.10a
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• pp.145-150
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• 2003

The present study aims to optimize the performance of the Two-Stage Gas Gun by using the experimentally obtained data. RSM(Response Surface Method) was adopted in the optimization process to find the operating parameter than can maximize the projectile speed with the minimum number of tests. To decide the test points which results can consist of the response surface, 3$^{k}$ full factorial method was used, and the design variables were chosen with piston mass and 2$^{nd}$ driver fill pressure. The response surface was composed by nine test results and consequently the optimization was done with GENOCOP III, inherently GA code, in order to seek the optimal test point. The optimal test condition from the response surface was verified by the experiment. Results showed that the optimization process with response surface can successfully predict the test results fairly well. This study shows the possibility of performance optimization for the experimental facilities using numerical optimization algorithm.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.635-638
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• 2002

In this study, three-dimensional imcompressible viscous flow analysis and optimization using response surface method are presented for the design of a jet fan. Steady, imcompressible, three-dimensional Reynolds averaged Wavier-Stokes equations are used as governing equations, and standard $k-{\varepsilon}$ turbulence model is chosen as a turbulence model. Governimg equations are discretized using finite volume method. Sweep angles are used as design variables for the shape optimization of the impeller in response surface method. The experimental points which are needed to construct response surface are obtained from the D-optimal design and finally the shape of impeller Is achieved from using a numerical optimization for the response surface which is obtained from CFD.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.237-250
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• 1999

Response surface analysis has been a popular tool conducted by engineers in many processes. In this paper, response surface function, named partial least squares response surface function is proposed. Partial least squares response surface function is a function of partial least squares components and the response surface modeling is used in either a first-order or a second-order model. Also, this approach will have the engineers be able to do the response surface modeling and the process optimization even when the number of experimental runs is less than the number of model parameters. This idea is applied to the nondesign data and an application of partial least squares response surface function to the process optimization is considered.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.2
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• pp.13-18
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• 2015

In this paper the DRC model is presented for solving multi objective problem. The proposed model is a combination of data envelopment analysis, Cuckoo algorithm and the response surface method. Due to reasons like costs, time and irreversible damages, it is not possible to analyze each and every one of the proposed models in practice, so the simulation is used. Since the number of experiments for simulation process is high then the optimization has gone to practice and directs the simulation process. The response surface method is used as one of the approaches of simulation optimization. Furthermore, data envelopment analysis is used to consider several response surfaces as efficiency response surface. Then this efficiency response surface is solved by Cuckoo algorithms. The main advantage of DRC model is to make one efficiency response surface function instate of multi surface function for every output and also using the advantages of Cuckoo algorithms. In order to demonstrate the effectiveness of the proposed approach, the branches of Refah bank in Mashhad is analyzed and the results are presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.894-900
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• 2000

Optimization of chassis frame is performed according to the minimization of eleven responses representing one total frame weight, three natural frequencies and seven strength limits of chassis frame that are analyzed by using each response surface model from D-optimal design of experiments. After each response surface model is constructed form D-optimal design and random orthogonal array, the main effect and sensitivity analyses are successfully carried out by using this approximated regression model and the optimal solutions are obtained by using a nonlinear programming method. The response surface models and the optimization algorithms are used together to obtain the optimal design of chassis frame. The eleven-polynomial response surface models of the thirteen frame members (design factors) are constructed by using D-optimal Design and the multi-disciplinary design optimization is also performed by applying dual response analysis.