• Korean Management Science Review
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• v.24 no.2
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.59-79
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• 1999

The fitting of a response surface model and the subsequent exploration of the response surface are usually based on the assumption that the experimental runs are carried out under homogeneous conditions. This, however, may be quite often difficult to achieve in many experiments. To control such an extraneous source of variation, the response surface design should be arranged in several blocks within which homogeneity of conditions can be maintained. In this case, when fitting a response surface model, the least squares estimates of the model's parameters and the prediction variance will generally depend on how the response surface design is blocked. That is, the choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of the prediction variance. In this paper, we propose a measure for evaluating the effect of blocking of response surface designs using cuboidal regions.

• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.168-173
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• 2000

Design of experiments is utilized for exploring the design space and for building response surface models in order to facilitate the effective solution of multi-objective optimization problems. Response surface models provide an efficient means to rapidly model the trade-off among many conflicting goals. In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effects of variations, called uncertainties. However, the evaluation of feasibility robustness often needs a computationally intensive process. To reduce the computational burden associated with the probabilistic feasibility evaluation, the first-order Taylor series expansions are used to derive individual mean and variance of constraints. For robust design applications, these constraint response surface models are used efficiently and effectively to calculate variances of constraints due to uncertainties. Robust optimization of automotive seat is used to illustrate the approach.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.3
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• pp.292-298
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• 2017

Numerical analysis for various design parameters should be preceded by optimal design of composite materials. Numerous studies have been conducted on the bolting of interconnecting beams. In this study, the response surface method was applied to optimize the design of bolted joints connected by laminated wood composite beams. The response surface was created by combining the FEA code for composite analysis and the algorithm for forming the response surface. Optimization on this response surface was performed with a genetic algorithm to derive the results. The determination of the optimum bolt-hole position for the connection of composite beams is an optimization problem. Tsai-Wu composite failure index, maximum deflection, and simple von Mises stress are set as the objective functions. It has been proved that the design results of the optimized bolt-hole are superior to the design performance of the existing conventional bolt-hole position.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.453-458
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• 2005

Weight minimization of double-deck train carbody is imperative to reduce cost and extend life-time of train. It is required to decide 36 thickness of aluminum extruded panels. However, the design variables are two many to tract. moreover, one execution of structural analysis of double-deck carbody is time-consuming. Therefore, we adopt approximation technique to save computational cost of optimization process. Response surface model is used to apporximate static response of double-deck carbody. To obtain plausible response surface model, orthogonal array is empolyed as design of experiment(DOE). Design improvement by approximate model-based optimization is described. Accuracy and efficiency of optimization by using response surface model are discussed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.510-513
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• 2004

This paper represents the response surface model for the cell gap on the flexible liquid crystal display (LCD) process. Using response surface methodology (RSM). D-optimal design is carried out to build the design space and the cell gap is characterized by the quadratic model. The statistical analysis is used to verify the response surface model. This modeling technique can predict the characteristics of the desired response, cell gap, varying with process conditions.

• 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.

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.439-443
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• 2002

In this study, three-dimensional incompressible viscous flow analysis and optimization using response surface method are presented for the design of a jet fan. Steady, incompressible, three-dimensional Reynolds averaged Wavier-Stokes equations are used as governing equations, and standard k-$\epsilon$ turbulence model is chosen as a turbulence model. Governing equations are discretized using finite volume method. Sweep angles and maximum thickness of blade 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 Full Factorial design and relations between design variables and response surface are examined.

• 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.