• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.195-208
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• 2009

A conventional single design optimality criterion has been used to select an efficient experimental design. But, since an experimental design is constructed with respect to an optimality criterion pre specified by investigators, an experimental design obtained from one optimality criterion which is superior to other designs may perform poorly when the design is evaluated by another optimality criterion. In other words, none of these is entirely satisfactory and even there is no guarantee that a design which is constructed from using a certain design optimality criterion is also optimal to the other design optimality criteria. Thus, it is necessary to develop certain special types of experimental designs that satisfy multiple design optimality criteria simultaneously because these multi-optimal designs (MODs) reflect the needs of the experimenters more adequately. In this article, we present a heuristic approach to construct second-order response surface designs which are more flexible and potentially very useful than the designs generated from a single design optimality criterion in many real experimental situations when several competing design optimality criteria are of interest. In this paper, over cuboidal design region for $3\;{\leq}\;k\;{\leq}\;5$ variables, we construct multi-optimal designs (MODs) that might moderately satisfy two famous alphabetic design optimality criteria, G- and IV-optimality criteria using a GA which considers a certain amount of randomness. The minimum, average and maximum scaled prediction variances for the generated response surface designs are provided. Based on the average and maximum scaled prediction variances for k = 3, 4 and 5 design variables, the MODs from a genetic algorithm (GA) have better statistical property than does the theoretically optimal designs and the MODs are more flexible and useful than single-criterion optimal designs.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.11
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• pp.2883-2893
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• 1994

Use of computers in design is a trend in recent years. Mechanism design also uses computers extensively and the concept of optimal mechanism design is developed in many ways. Various authors presented methods based on sensitivity analysis but in these cases, the governing equation of the mechanism has to be derived and calculations become very complicated. In this papers, a method based on the least experimental plan is presented. To make a model of a mechanism, a general purpose mechanism analysis program is used. To obtain an optimal design of a mechanism, the relationship between design variables and the objective function is represented as the nonlinear equation. Optimal design variables are found by solving this derived equation and its result is verified. An example is presented to show the effectiveness of this method.

• Journal of Korean Society for Quality Management
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• v.15 no.2
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• pp.69-73
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• 1987

The aim of this study is to find out the optimal goods for the marketing research through analysing the factor effecting the marketing survey by using the experimental design method. The decisive effecting factors in relation with the marketing survey were investigated as follows; 1. A row effect (Ai; i = 1, 2, ... n) is the design sorts of woman-clothes bias. 2. A column effect (Bi; i = 1, 2, ... n) is the woman-consumer bias. In this paper the experimental design, execution and statistical analys is were conducted to find out the optimal goods for marketing research.

• Transactions of The Korea Fluid Power Systems Society
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• v.6 no.4
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• pp.24-32
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• 2009

For a design of on/off solenoid actuator for valve actuating, designer must have the experimental knowledge as well as general electromagnetic formulas to design object. It is possible for theoretical knowledge to do the out-line design, but it is impossible to optimal design without experimental knowledge which only can be achieved through many repeated experiments. In addition, in present on/off type solenoid actuator field, the smaller, lightening, lower consumption power, high response time are effected as the most important design factor. So, experimental knowledge is more needed for optimal design of solenoid actuator. In this study, we derived the governing equations for optimal design of on/off solenoid actuator for valve actuating and developed a design program composed electromagnetic theories and experimental parameter values for inexperienced designers. And we proved the propriety of this program by experiments.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.4
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• pp.241-246
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• 2013

From the numerical results of density-based topology design optimization, a CAD geometric model is constructed and fabricated using 3D printer to experimentally validate the optimal design. In the process of topology design optimization, we often experience checkerboard phenomenon and complicated branches, which could result in the manufacturing difficulty of the obtained optimal design. Sensitivity filtering and morphology methods are used to resolve the aforementioned issues. Identical volume fraction is used in both numerical and experimental models for precise validation. Through the experimental comparison of stiffness in various designs including the optimal design, it turns out that the optimal design has the highest stiffness and the experimental result of compliance matches very well with the numerical one.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.345-352
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• 2014

In this paper, the CAD data for the optimal shape design obtained by isogeometric shape optimization is directly used to fabricate the specimen by using 3D printer for the experimental validation. In a conventional finite element method, the geometric approximation inherent in the mesh leads to the accuracy issue in response analysis and design sensitivity analysis. Furthermore, in the finite element based shape optimization, subsequent communication with CAD description is required in the design optimization process, which results in the loss of optimal design information during the communication. Isogeometric analysis method employs the same NURBS basis functions and control points used in CAD systems, which enables to use exact geometrical properties like normal vector and curvature information in the response analysis and design sensitivity analysis procedure. Also, it vastly simplify the design modification of complex geometries without communicating with the CAD description of geometry during design optimization process. Therefore, the information of optimal design and material volume is exactly reflected to fabricate the specimen for experimental validation. Through the design optimization examples of elasticity problem, it is experimentally shown that the optimal design has higher stiffness than the initial design. Also, the experimental results match very well with the numerical results. Using a non-contact optical 3D deformation measuring system for strain distribution, it is shown that the stress concentration is significantly alleviated in the optimal design compared with the initial design.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.299-308
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• 2009

It is very rare to conduct an experimental design with a single objective in mind. since we have uncertainties in model and its assumptions. Basically we have three approaches in literature to handle this problem, the mini-max, compound, constrained experimental design. Since Cook and Wong (1994) announced the equivalence between the compound and the constrained design, many constrained experimental design approaches have adopted the approximate design algorithm of compound experimental design. In this paper we attempt to modify the row-exchange algorithm under exact experimental design setting, not approximate experimental design one. This attempt will provide more realistic design setting for the field experiment. In this process we proposed another criterion on how to set the constrained experimental design. A graph to show the general issue of infeasibility, which occurs quite often in constrained experimental design, is suggested.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.929-931
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• 2002

For design of On/Off type solenoid actuator, designer must have the experimental knowledge as well as general electromagnetic formulas to design object. It is possible for theoretical knowledge to do the out-line design, but it is impossible to optimal design without experimental knowledge which only can achieve through many repeated experiments. In addition, in present On/Off type solenoid actuator field, smaller, lightening, lower consumption power, high response time are effected as the most important design factor. So, experimental knowledge is more needed for optimal design of solenoid actuator. In this study, we developed a design program composed electromagnetic theories and experimental parameter values for inexperienced designers. And we proved the propriety of this program by experiments.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.93-102
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• 2015

The D-optimal design for the nonlinear model typically depends on the unknown parameters to be estimated. Therefore, it is strongly recommended in literature to use a sequential experimental design for estimating the parameters. In this paper two stage experimental design is discussed under many different circumstances including estimating parameters. The method is so universal to be applied to any mixture of objectives for any model including linear model. A hybrid approach is suggested to handle more than 2 objectives in two-stage experimental design. The design is discussed in approximate design framework.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.619-627
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• 2002

It is unpractical for the optimal design theory based on the given model and assumption to be applied to the real-world experimentation. Particularly, when the experimenter feels it necessary to consider multiple objectives in experimentation, its modified version of optimality criteria is indeed desired. The constrained optimal design is one of many methods developed in this context. But when the number of constraints exceeds two, there always exists a problem in specifying the lower limit for the efficiencies of the constraints because the “infeasible solution” issue arises very quickly. In this paper, we developed a sequential approach to tackle this problem assuming that all the constraints can be ranked in terms of importance. This approach has been applied to the polynomial regression model.