• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.7
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• pp.885-895
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• 2004

Generally, automatic design is carried out with computer simulation and the simulation models are established by investigating the correlations between the simulation and real experiments. Therefore, the experiment results are utilized as complimentary data although they are considered to be precise. Orthogonal arrays have been adopted for discrete design. A method is proposed to directly exploit the experiment results in the design process with orthogonal arrays. Experiments are allocated to some rows of an orthogonal array and computer simulations are allocated to the others. A rule for the allocation is found to keep the orthogonality. Error analysis of the design results is performed. Mathematical examples are made to verify the validity of the proposed method. Error models are defined with the examples and the design solutions from the examples are discussed.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.277-299
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• 2002

Most statistical designs, such as orthogonal designs and optimal designs, are based on a specific statistical model. It is very often that the experimenter does not completely know the underlying model between the response and the factors. In computer experiments, the underlying model is known, but too complicated. In this case we can treat the model as a black box, or model to be unknown. Both cases need a space filling design. The uniform design is one of space filling designs and seeks experimental points to be uniformly scattered on the domain. The uniform design can be used for computer experiments and also for industrial experiments when the underlying model is unknown. In this paper we shall introduce the theory and method of the uniform design and related data analysis and modelling methods. Applications of the uniform design to industry and other areas are discussed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.125-131
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• 2005

The purpose of this study is an optimization of gate valve made by forging method instead of welding method. In this study, we propose an optimal shape design to improve the mechanical efficiency of gate valve. In order to optimize more efficiently and reliably, the meta-modeling technique has been developed to solve such a complex problems combined with the DACE (Design and Analysis of Computer Experiments). The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Also, we prove reliability of the DACE model's application to gate valve by computer simulations using FEM(Finite Element Method).

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.122-128
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• 2006

One feature of a computer simulation experiment, different from a physical experiment, is that the output is often deterministic. Moreover the codes are computationally very expensive to run. This paper deals with the design and analysis of computer experiments(DACE) which is a relatively new statistical research area. We model the response of computer experiments as the realization of a stochastic process. This approach is basically the same as using a spatial linear model. Applications to the optimal mechanical designing and model calibration problems are illustrated. Algorithms for selecting the best spatial linear model are also proposed.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.51-56
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• 2006

A computer code was developed to simulate both the thermal stresses introduced during the post-filling stage and the in-plane deformation after ejection process by finite element method based on the plane stress theory. The computer simulation was applied to the mold design of a 2 inch light guide plate (LGP) for thin film transistor (TFT)-liquid crystal displays (LCD). With injection molding experiments based on the design of experiments (DOE) technique, the influences of the processing conditions in injection molding on brightness and uniformity of the LGP were investigated, and the optimal processing parameters were selected to increase the brightness and uniformity. The verification experiment showed that the brightness and uniformity of the LGP were increased dramatically under the selected optimal processing conditions.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.619-632
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• 2002

In recent study on design of experiments, the complicate metamodeling has been studied because defining exact model using computer simulation is expensive and time consuming. Thus, some designers often use approximate models, which express the relation between some inputs and outputs. In this paper, we review and compare the complicate metamodels, which are expressed by the interaction of various data through trying many physical experiments and running a computer simulation. The prediction model in this paper employs interpolation schemes known as ordinary kriging developed in the fields of spatial statistics and kriging in Design and Analysis of Computer Experiments (DACE) model. We will focus on describing the definitions, the prediction functions and the algorithms of two kriging methods, and assess the error measures of those by using some validation methods.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.339-344
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• 2001

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM (Finite Element Method) and BEM (Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta-modeling technique has been developed for solving such a complex problems combined with the DACE (Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building meta-models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty-six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging meta-model of a train suspension. After each Kriging meta-model is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called SQP (Sequential Quadratic Programming).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.233-241
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• 1998

With the increasing complexity of man-made systems, computer simulation has become a widely used tool for the design and analysis of complex system. When computer simulation is used for the purpose of system design, the analysts want to develop strategies to achieve some pre-defined target condition for an output of interest while simultaneously minimizing its variance. The goal of this research is to develop and demonstrate a new method for the design of robust systems with multiple responses using the fuzzy satisfaction function and computer simulation experiments.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.119-126
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• 2006

Kriging model is widely used as design DACE(analysis and computer experiments) model in the field of engineering design to accomplish computationally feasible design optimization. In this paper, the optimization of gate valve was performed using Kriging based approximation model. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. In addition, we describe the definition, the prediction function and the algorithm of Kriging method and examine the accuracy of Kriging by using validation method.