• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.589-599
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• 2013

The concept of robust optimization is based on Taguchi's method. Especially, robustness indices of objective function pursue an insensitive and conservative design when there are variations on design variables and parameters. To accomplish the purpose, various robustness indices on the objective function have been developed. However, it can be caused limitations to develop the robustness index, because there is difference between the Taguchi's method and robust optimization. In this paper, an investigation is performed to identify the characteristics and the drawbacks of the previous studies. To achieve the purpose, evaluations are conducted by using the examples which have both a deterministic optimum and a robust optimum. Moreover, a new viewpoint as well as a robustness index using a supremum value of the objective function is proposed based on the investigation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.535-543
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• 2014

In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.10
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• pp.1109-1114
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• 2012

The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

• Journal of the Korean Magnetics Society
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• v.24 no.5
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• pp.160-164
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• 2014

In this paper, an efficient robust design utilizing an enhanced Taguchi method is proposed to reduce cogging torque of a BLDC motor in the presence of design uncertainty. To overcome defects of the conventional Taguchi method in dealing with a generalized robust design problem, a penalty function and an optimal level searching technique are newly introduced. In order to verify the proposed method, a 5 kW, rated speed of 2,300 rpm, rated torque of 20 Nm BLDC motor for driving electric vehicles is optimized. Then, the robust design is compared with conceptual and deterministic ones in terms of the cogging torque, rated torque and torque ripple.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.24-32
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• 2006

Using a deterministic design optimization, the effect of uncertainty can result in violation of constraints and deterioration of performances. For this reason, design optimization is required to guarantee reliability for constraints and ensure robustness for an objective function under uncertainty. Therefore, this study drew Monte Carlo Simulation(MCS) for the evaluation of reliability and robustness, and selected an artificial neural network as an approximate model that is suitable for MCS. Applying to the aero-structural optimization problem of aircraft wing, we can explore robuster optima satisfying the sigma level of reliability than the baseline.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.9
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• pp.1305-1310
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• 2010

In a practical design process, the method of extracting the design space information of the complex system for verifying, improving, and optimizing the design process by taking into account the design variables and their shape tolerance is very important. Finite element analysis has been successfully implemented and integrated with design of experiment such as D-Optimal array; thus, a response surface model and optimization tools have been obtained, and design variables can be optimized by using the model and these tools. Then, to guarantee the robustness of the design variables, a robust design should be additionally performed by taking into account the statistical variation of the shape tolerance of the optimized design variables. In this study, a new approach based on the use of the response surface model is proposed; in this approach, the standard normal distribution of the shape tolerance is considered. By adopting this approach, it is possible to simultaneously optimize variables and perform a robust design. This approach can serve as a means of efficiently modeling the trade-off among many conflicting goals in the applications of finite element analysis. A case study on the robust optimal design of disc brakes under thermal loadings was carried out to solve multiple objective functions and determine the constraints of the design variables, such as a thermal deformation and weight.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.427-430
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• 2009

풍력 터빈 블레이드용 익형의 경우 운용 조건에서 높은 양항비를 가지도록 설계되나 풍속, 풍향의 변동에 의해 운용조건에 변화가 발생할 경우 성능의 저하가 발생할 수 있다. 따라서 운용조건의 변동이 발생하더라도 공력 성능이 크게 변하지 않는 익형이 요구된다. 본 연구에서는 이러한 운용조건의 불확실성을 고려하여 풍력 터빈 블레이드용 익형의 신뢰성 기반 강건 최적 설계를 수행하였다. 익형 설계를 위해서 여러 익형 형상 변수들을 고려할 수 있는 익형 모델링 함수를 정의하였고 기저형상으로는 NREL에서 개발한 S809 익형을 사용하였다.

• 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.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.215.2-215.2
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• 2005

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.267-280
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• 2007

Various single-valued design optimality criteria such as D-, G-, and V-optimality are used often in constructing optimal experimental designs for mixture experiments in a constrained region R where lower and upper bound constraints are imposed on the ingredients proportions. Even though they are optimal in the strict sense of particular optimality criterion used, it is known that their performance is unsatisfactory with respect to the prediction capability over a constrained region. (Vining et at., 1993; Khuri et at., 1999) We assume the quadratic polynomial model as the mixture response surface model and are interested in finding efficient designs in the constrained design space for a mixture. In this paper, we make an expanded list of candidate design points by adding interior points to the extreme vertices, edge midpoints, constrained face centroids and the overall centroid. Then, we want to propose a robust design with respect to D-optimality, G-optimality, V-optimality and distance-based U-optimality. Comparing scaled prediction variance quantile plots (SPVQP) of robust designs with that of recommended designs in Khuri et al. (1999) and Vining et al. (1993) in the well-known examples of a four-component fertilizer experiment as well as McLean and Anderson's Railroad Flare Experiment, robust designs turned out to be superior to those recommended designs.