• Journal of the korean Society of Automotive Engineers
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• v.17 no.2
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• pp.24-34
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• 1995

여기서는 최근의 자동차 금형의 최적설계 기술개발을 위한 국내의 연구동향을 중심으로 살펴보고 실제 CAE 기술의 적용 예를 소개하고자 한다. 1. 금형설계에서의 CAE 기술의 도입. 2. CAE 기술로서의 FEA 해석. 3. CAE 도입의 기술적 제 문제. 4. 프레스 공정의 2차원/3차원 해석. 5. 최적 블랭크 형상설계기술. 6. 강건설계를 위한 CAE 기술의 응용.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.99-104
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• 2008

This study performed robust design optimization of fighter wing planform, considering uncertainty based on neural network model. To construct uncertainty model, aerodynamic performance and their sensitivity were evaluated by 3-dimensional Euler equations and adjoint variable method at experimental points selected from central composite design. In addition, because a neural network model has the advantage of capturing non-linear characteristic, it was possible to predict sensitivity of the aerodynamic performance efficiently and accurately . From the results of robust design optimization, it could be confirmed that the robustness of the objective function and constraints were improved if the variation of uncertainty and sigma level were increased.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.60-66
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• 2010

In the view of robust design optimization, RAE2822 airfoil was designed to achieve not only the maximum lift-to-drag ratio but also insensitivity of that. While the RAE2822 is flying at the cruise speed, Mach variation is considered as the operational uncertainty. In order to explore the design space, metamodels were introduced instead of consecutively computing the gradient. Also a metamodel was used to represent the sigma space. Using the metamodel, an optimum value was searched in the view of global optimization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.113-120
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• 2007

Design optimization of layered plates bonding process is conducted by considering uncertainties in a manufacturing process, in order to reduce the crack failure arising due to the residual stress at the surface of the adherent which is caused by different thermal expansion coefficients. Robust optimization is peformed to minimize the mean as well as its variance of the residual stress, while constraining the distortion as well as the instantaneous maximum stress under the allowable reliability limits. In this optimization, the dimension reduction (DR) method is employed to quantify the reliability such as mean and variance of the layered plate bonding. It is expected that the DR method benefits the optimization from the perspectives of efficiency, accuracy, and simplicity. The obtained robust optimal solution is verified by the Monte Carlo simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1437-1443
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• 2003

Most optimization problems do not consider tolerance of design variables and design parameters. Ignorance of these tolerances may not fit for the practical problems and can lead to an unexpected conclusion. That is why we suggest robust optimization considering tolerances in both design variables and problem parameters. Using robust optimization, we designed minimum weight annular finned heat transfer tube subject to constraints on limitation of pressure difference and minimum value of total heat transfer. Consequently, robust optimization satisfies tolerance considered practical problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.262.1-262.1
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• 2005

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1305-1310
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• 2012

The performance of a system can be affected by the variance of noise factors, which arise owing to uncertainties of the material properties and environmental factors acting on the system. For robust design optimization of the system performance, it is necessary to minimize the effect of the variance of the noise factors that are impossible to control. However, present robust design techniques consider the variation of design factors, and not the noise factors, as being important. Furthermore, it is necessary to assume a normal distribution; however, a normal distribution is often not suitable to estimate the variations. In this study, a robust design technique is proposed to consider the variation of noise factors that are estimated as non-normal distributions in a real experiment. As an example of an engineering problem, a deep-sea manganese nodule miner tracked vehicle is used to demonstrate the feasibility of the proposed method.

• Journal of Engineering Education Research
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• v.11 no.1
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• pp.64-75
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• 2008

A new product development and product realization process (PRP) is a circulating and feedback process by generating ideas through creative problem solving process. The early stages of PRP correspond to conceptual design and product development, in which a large portion of product life cycle cost can be saved. The optimal design method for this stage is a robust design suggested by Taguchi. Quality must be 'engineered in' since it can not be 'inspected out.' A robust design is an engineering methodology to improve the quality of a product by minimizing the efforts of variations without eliminating the causes. The objective of this study is to propose a scheme and a case study of robust design for exploring design parameters, and introduce a creative problem solving process. Major research subjects include a creative problem solving process, robust design procedure and their implementation. For the experiment of Taguchi method, a toy catapult is adopted. For the creativity development, a short project is assigned to devise a similar tool with the toy catapult. A reference model is suggested to compare and evaluate their ideas.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1217-1223
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• 2008

Vehicle vibration mostly originates from the road excitation and causes discomfort, fatigue and even injury to a driver. Vehicle ride comfort is one of the most important performance indices to achieve a high-quality vehicle design. Since design parameter variations inevitably result in the vehicle ride comfort variance, the variance characteristics should be analyzed in the early design stage of the vehicle. The vehicle ride comfort is often defined by an index which employs a weighted RMS value of the acceleration PSD of a seat position. The design solution is obtained through two steps in this study. An optimization problem to obtain a minimum ride comfort index is solved first. Then another optimization problem to obtain minimum variance of the ride comfort index is solved. For the optimization problems, the equations of motion and the sensitivity equations are derived basing on a 5-DOF vehicle model. The numerical results show that an optimal solution for the minimum ride comfort is not necessarily same as that of the minimum variance of the ride comfort.