• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.381-386
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• 2003

Gradually engineering designers are determined based on computer simulations. Modeling of the computer simulation however is too expensive and time consuming in a complicate system. Thus, designers often use approximation models called metamodels, which represent approximately the relations between design and response variables. There arc general metamodels such as response surface model and kriging metamodel. Response surface model is easy to obtain and provides explicit function. but it is not suitable for highly nonlinear and large scaled problems. For complicate case, we may use kriging model that employs an interpolation scheme developed in the fields of spatial statistics and geostatistics. This class of into interpolating model has flexibility to model response data with multiple local extreme. In this study. metamodeling techniques are adopted to carry out the shape optimization of a funnel of Cathode Ray Tube. which finds the shape minimizing the local maximum principal stress Optimum designs using two metamodels are compared and proper metamodel is recommended based on this research.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.193-201
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• 2003

The problem of brake judder is typically caused by defects of quality manufacturing. DFSS (Design for six sigma) is a design process for quality improvement. DFSS will result in more improved but less expensive quality products. This paper presents an implementation of DFSS for quality improvement of the brake judder of heavy-duty trucks. Carrying out 5 steps of DFSS, the major reasons for defects of quality are found. The numerical approximation of the brake system is derived by means of the response surface method. Its quality for brake judder is improved by using the sigma based robust design methodology. Results are compared between the conventional deterministic optimal design and the proposed sigma based robust design. The proposed one shows that manufacturing cost may increase as the quality level increase. The proposed one, however, is more economical in aspect of the overall cost since the probability of failure dramatically goes down.

• Structural Engineering and Mechanics
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• v.34 no.4
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.143-148
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• 1999

Design optimization is performed to calculated the draw-bead force for satisfying the design re-quirements. For an analysis tool a rigid-plastic finite element method with modified membrane element is adopted. response surface methodology is utilized for constructing the approximation surface for the optimum searching of draw bead force in sheet metal forming process. the algorithm developed is ap-plied to a design of the draw bead forces in a deep drawing process. The results show that the design of process parameters is applicable in complex metal forming analysis. It is also noted that the present algo-rithm enhances the stable optimum solution with small times of optimization iteration.

• 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.

• International Journal of Fluid Machinery and Systems
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• v.3 no.2
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• pp.150-159
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• 2010

Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

• The KSFM Journal of Fluid Machinery
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• v.14 no.5
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• pp.63-68
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• 2011

This paper presents an optimization procedure for performance improvement of a tunnel ventilation jet fan. Optimization techniques based on response surface approximation (RSA) are employed to improve the aerodynamic performance of a tunnel ventilation jet fan. For numerical analysis, three-dimensional Renolds- averaged Navier-Stokes (RANS) equations with shear stress transport turbulence model are discretized by using finite volume approximations and solved on hexahedral grids to evaluate the total efficiency at the operating condition as the objective function. Four geometric variables defining the meridional length and the thickness profile at the hub and shroud in the jet fan rotor are selected as design variables for the numerical optimization. The results of the numerical optimization show that the total efficiency of the optimized model is significantly improved in comparison with the base model.

• Journal of Mechanical Science and Technology
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• v.18 no.1
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• pp.37-44
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• 2004

This paper presents an approach to optimally design the air bearing surface (ABS) of the head slider by using the approximation methods. The reduced basis concept is used to reduce the number of design variables. In the numerical calculation, the progressive quadratic response surface modeling (PQRSM) is used to handle the non-smooth and discontinuous cost function. A multi-criteria optimization problem is formulated to enhance the flying performances over the entire recording band during the steady state and track seek operations. The optimal solutions of the sliders, whose target flying heights are 12 nm and 9 nm, are automatically obtained. The flying heights during the steady state operation become closer to the target values and the flying height variations during the track seek operation are smaller than those for the initial one. The pitch and roll angles are also kept within suitable ranges over the recording band.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.336-345
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• 2012

In crack growth life, uncertainties are caused by variance of geometry, applied loads and material properties. Therefore, the reliability estimation for these uncertainties is required to keep the robustness of calculated life. The stress intensity factors are the most important variable in crack growth life calculation, but its equation is hard to know for complex geometry, therefore they are processed by the finite element analysis which takes long time. In this paper, the response surface is considered to increase efficiency of the reliability analysis for crack growth life of a turbine wheel. The approximation model of the stress intensity factors is obtained by the regression analysis for FEA data and the response surface of crack growth life is generated for selected factors. The reliability analysis is operated by the Monte Carlo Simulation for the response surface. The results indicate that the response surface could reduce computations that need for reliability analysis for the turbine wheel, which is hard to derive stress intensity factor equation, successfully.