• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1022-1027
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• 2004

In this study, the shape of plate-fin type heat sink is numerically optimized to acquire the minimum pressure drop under the required temperature rise. To do this, a new sequential approximate optimization (SAO) is proposed and it is integrated with the computational fluid dynamics (CFD). In thermal/fluid systems for constrained nonlinear optimization problems, three fundamental difficulties such as high cost for function evaluations (i.e., pressure drop and thermal resistance), the absence of design sensitivity information, and the occurrence of numerical noise are confronted. To overcome these problems, the progressive quadratic response surface method (PQRSM), which is one of the sequential approximate optimization algorithms, is proposed and the heat sink is optimize by means of the PQRSM.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.3
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• pp.58-63
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• 2005

In order to minimize weight of vehicle seat, an optimum design of aluminum seat is presented while satisfying stress and fatigue life constraints. In this study, the analysis model is validated by comparing it's stress with that of test. Then, two-level orthogonal array is used to estimate the design sensitivity for 7 design variables. Finally, the sequential approximate optimization (SAO) is performed using the constructed RSM models. The approximate RSM models are sequentially updated using the analysis results corresponding to the approximate optimum obtained during the SAO. After 14 analyses, the SAO gives an optimal design that can reduce 16.7$\%$ of weight while increasing 369$\%$ of fatigue life and satisfying stress constraint.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.10
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• pp.1079-1086
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• 2013

Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• Structural Engineering and Mechanics
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• v.51 no.6
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• pp.989-1003
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• 2014

In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.33-44
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• 2006

As many scientific phenomena are now investigated using complex computer models, the effective use of Kriging on physical problems has been expanded to provide global approximations for optimization problems. This paper is focused on the two types of strategies to improve efficiency and accuracy of approximate optimization models using Kriging. These methods are performed by the stochastic process, stochastic-localization method(SLM), as the criterion to move the local domains and the design of experiments(DOE), the classical design and space-filling design. The proposed methodology is applied to the designs of 3-bar truss, Sandgren's pressure vessel, and honeycomb upper platform of a satellite structure.

• Korean Journal of Computational Design and Engineering
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• v.21 no.4
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• pp.389-396
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• 2016

The necessity for environment-friendly material development has emerged in the recent automotive field due to stricter regulations on fuel economy and environmental concerns. Accordingly, the automotive industry is paying attention to carbon fiber reinforced plastic (CFRP) material with high strength and stiffness properties while the lightweight. In this study, we determine a shape of lower control arm (LCA) for maximizing the strength and stiffness by optimizing the thickness of each layer when the stacking angle is fixed due to the CFRP manufacturing problems. Composite materials are laminated in the order of $0^{\circ}$, $90^{\circ}$, $45^{\circ}$, and $-45^{\circ}$ with a symmetrical structure. For the approximate optimal design, we apply a sequential two-point diagonal quadratic approximate optimization (STDQAO) and use a process integrated design optimization (PIDO) code for this purpose. Based on the physical properties calculated within a predetermined range of laminate thickness, we perform the FEM analysis and verify whether it satisfies the load and stiffness conditions or not. These processes are repeated for successive improved objective function. Optimized CFRP LCA has the equivalent stiffness and strength with light weight structure when compared to conventional aluminum design.

• Korean Journal of Computational Design and Engineering
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• v.16 no.6
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• pp.397-406
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• 2011

Since tolerance allocation in a mobile phone camera manufacturing process greatly affects production cost and reliability of optical performance, a systematic design methodology for allocating optimal tolerances is required. In this study, we proposed the tolerance optimization procedure for determining tolerances that minimize production cost while satisfying the reliability constraints on important optical performance indices. We employed Latin hypercube sampling for evaluating the reliabilities of optical performance and a function-based sequential approximate optimization technique that can reduce computational burden and well handle numerical noise in the tolerance optimization process. Using the suggested tolerance optimization approach, the optimal production cost was decreased by 30.3 % compared to the initial cost while satisfying the two constraints on the reliabilities of optical performance.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1063-1068
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• 2008

A lens system of a camera module for mobile phones is comprised of the composition and design of various shapes of lens. To improve responses such as the modular transfer function (MTF), a lens system should always be constructed by considering uncertainty that can be caused by manufacturing and assembly error. In this study, tolerance optimization using the Latin Hypercube Sampling (LHS) technique is performed. In order to reduce the computational burden of the tolerance optimization process and decrease the influence from numerical noise effectively, we use the Progressive Quadratic Response Surface Modeling (PQRSM), which is one of Sequential Approximate Optimization (SAO) techniques. Using this method, we achieved optimal tolerance for each lens and obtained reliability for satisfying user‘s requirements. In addition, through the design process the manufacturing and assembly cost of a lens system was reduced.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1217-1224
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• 2005

To improve the accuracy of a metamodel, additional sample points can be selected by using a specified criterion, which is often called sequential sampling approach. Sequential sampling approach requires small computational cost compared to one-stage optimal sampling. It is also capable of monitoring the process of metamodeling by means of identifying an important design region for approximation and further refining the fidelity in the region. However, the existing critertia such as mean squared error, entropy and maximin distance essentially depend on the distance between previous selected sample points. Therefore, although sufficient sample points are selected, these sequential sampling strategies cannot guarantee the accuracy of metamodel in the nearby optimum points. This is because criteria of the existing sequential sampling approaches are inefficient to approximate extremum and inflection points of original model. In this research, new sequential sampling approach using the sensitivity of metamodel is proposed to reflect the response. Various functions that can represent a variety of features of engineering problems are used to validate the sensitivity approach. In addition to both root mean squared error and maximum error, the error of metamodel at optimum points is tested to access the superiority of the proposed approach. That is, optimum solutions to minimization of metamodel obtained from the proposed approach are compared with those of true functions. For comparison, both mean squared error approach and maximin distance approach are also examined.