• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.662-674
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• 2017

This paper describes the multidisciplinary design optimization (MDO) process of a tailless unmanned combat aerial vehicle (UCAV) using global variable fidelity aerodynamic analysis. The developed tailless UAV design framework combines multiple disciplines that are based on low-fidelity and empirical analysis methods. An automated high-fidelity aerodynamic analysis is efficiently integrated into the MDO framework. Global variable fidelity modeling algorithm manages the use of the high-fidelity analysis to enhance the overall accuracy of the MDO by providing the initial sampling of the design space with iterative refinement of the approximation model in the neighborhood of the optimum solution. A design formulation was established considering a specific aerodynamic, stability and control design features of a tailless aircraft configuration with a UCAV specific mission profile. Design optimization problems with low-fidelity and variable fidelity analyses were successfully solved. The objective function improvement is 14.5% and 15.9% with low and variable fidelity optimization respectively. Results also indicate that low-fidelity analysis overestimates the value of lift-to-drag ratio by 3-5%, while the variable fidelity results are equal to the high-fidelity analysis results by algorithm definition.

• Journal of Applied Reliability
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• v.4 no.1
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• pp.39-64
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• 2004

Taguchi's multi-characteristic experiments seek proper choice of levels of contollable factors which satisfy that all reponses of characteristics in a desirable range simultaneously. This aim can be achieved by response surface techniques that allow more flexible in modeling than traditional Taguchi's parameter design. In this article, a multi-response surface modeling and analysis techniques is proposed to deal with the multi-characteristic optimization problem in experimentation with Taguchi's controllable and noise factors.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.300-306
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• 1987

This paper deals with a modeling of manufacturing system and a model analysis by a SIMAN language. A flow of production process is analyzed, and a mathematical model on the basis of the analyzed data is simulated by a SIMAN language. An object of this study is to achieve an optimization of production a reduction of cost, and an improvement of quality by a applicable line-balancing technique and an optimization technique in a real factor induced an analysis and synthesis of the result of simulation.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.165-194
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• 1999

Taguchi's parameter design seeks proper choice of levels of controllable factors (Parameters in Taguchi's terminology) that makes the qualify characteristic of a product optimal while making its variability small. This aim can be achieved by response surface techniques that allow flexibility in modeling and analysis. In this article, a collection of response surface modeling and analysis techniques is proposed to deal with the multiresponse optimization problem in experimentation with Taguchi's signal and noise factors.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.61-67
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• 2011

This study is concerned with the shape optimization of structural members frequently found in critical area in a structure system, that is, highly stressed zone. Isogeometry analysis is well known to be the very efficient way to integrate the geometric modeling(CAD) and computational analysis(CAE). This can be accomplished by directly using the geometric modeling by NURBS(Non-Uniform Rational Basis Spline). In this study, an efficient computer code adopting the isogeometry concept has been developed for the structural analysis, in which CAD information can be directly used in the finite element modeling. In order to show the validity of the present code, the present results are compared with those by using the commercial package, that is, MSC/NASTRAN. The present isogeometric analysis procedure has been integrated with the optimization procedure to deal with the optimization problem found in the context of structural mechanics. The present system has been successfully applied to the shape optimization of cantilever structure having bracket. From the present study, it can be seen the validity of the present approach and computer codes developed in this study. This paper ends with some discussions about the practical usefulness of the present approach which is based on isogeometry analysis, and extension of the present study.

• Korean Management Science Review
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• v.11 no.1
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• pp.59-72
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• 1994

In this paper we examine potentials of a spreadsheet program, one of the most widly available software system, as a mathematical optimization modeling tool. For an illustrative example, a shortest path problem is modeled on Lotus-123 for practical use and an implementational framework and a general guide to the spreadsheet modeling of network analysis is provided.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.168-171
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• 2006

본 논문에서는 Particle Swarm Optimization 기법을 이용한 뉴로-퍼지 시스템의 파라미터 동정을 실시한다. PSO의 학습 및 군집 특성을 이용하여 시스템을 학습한다. 유전 알고리즘과 같은 무작위 탐색법을 이용하며 하나의 해 군집에 대해 다수 객체들이 탐색하는 기법을 통하여 최적해 부분의 탐색성능을 높여 전체 모델의 학습성능을 개선하고자 한다. 제안된 기법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

• Korean Journal of Computational Design and Engineering
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• v.11 no.4
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• pp.265-272
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• 2006

For the design and analysis of 3D object featuring complexity and irregularity in shape, sectional digital images measured by an industrial CT scanner are employed to generate a finite element model with uniform voxels. The voxel model plays a key role in developing an integrated reverse engineering system including geometric modeling, simulation and optimization. Design examples applied to topology optimization show that the proposed approach can provide a remarkable reduction in time cost at the conceptual and detail design stages.

• Korean Journal of Computational Design and Engineering
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• v.19 no.2
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• pp.148-156
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• 2014

Generally, over 95% of manufacturing cost is determined in the design and manufacturing preparation step, especially a great part of productivity is determined in the manufacturing preparation step. In order to improve the manufacturing competitiveness, we have to verify the problems that can be occurred in the production step and remove the unnecessary factors in the manufacturing preparation step. Thus, manufacturing industries are adopting digital manufacturing system based on modeling & simulation. In this paper, we introduce e-FEED system (electronic based Front End Engineering and Design) that is the integrated design and analysis system for optimized manufacturing line development based on simulation automation and explain the work process (Design, Analysis and Optimization) about manufacturing line development using e-FEED system. Also, the effect is described through the real implementation cases.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.