• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1187-1192
• /
• 2008

In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.11a
• /
• pp.655-659
• /
• 2003

In this study, an improved topology design methodology that is combined with genetic algorithm, response surface method is provided to overcome the limitations of the ordinary topology optimization methods on the complex non-linear problem. the method is applied to a disc brake system for reducing an automobile brake noise. The low frequency that may induces the brake noise under the unstable mode is increased by obtaining the optimal topology. The result is verified by the analysis of variance and confirmed that the estimators for the approximation equations are highly reliable

• Proceedings of the KIEE Conference
• /
• 2003.07b
• /
• pp.681-683
• /
• 2003

The response surface method (RSM) became a popular meta modeling technique, but it always contains the approximation error. Instead of the conventional RSM, the moving least squares method (MLSM) was used to get more accurate models. The characteristics of a single-phase induction motor for the reciprocal compressor are analyzed by using the lumped method Program (LMP). The proposed method is applied to a single-phase induction motor for increasing the efficiency.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.41 no.1
• /
• pp.48-53
• /
• 2013

Polynomial response surface model has been widely used as approximation model which replace physical or numerical experiments in various engineering fields. Generally, low-order model is used to reduce experimental points required to construct the response surfaces, but this approach has limit to represent the highly non-linear phenomena. In this paper, we developed the method to expand modeling capabilities of polynomial response surfaces by increasing order of polynomial and selecting optimum polynomial basis functions. Genetic algorithm is used to choose optimal polynomial basis functions. Developed method was applied to analytic functions with 1 or 2 variables and wind tunnel test data modeling. The results show that this method is applicable to building response surface models for highly non-linear phenomena.

• Advances in aircraft and spacecraft science
• /
• v.6 no.6
• /
• pp.529-550
• /
• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Structural Engineering and Mechanics
• /
• v.56 no.4
• /
• pp.549-567
• /
• 2015

A first-order moment method (FORM) reliability analysis is commonly used for structural stability analysis. It requires the values and partial derivatives of the performance to function with respect to the random variables for the design. These calculations can be cumbersome when the performance functions are implicit. A Gaussian process (GP)-based response surface is adopted in this study to approximate the limit state function. By using a trained GP model, a large number of values and partial derivatives of the performance functions can be obtained for conventional reliability analysis with a FORM, thereby reducing the number of stability analysis calculations. This dynamic renewed knowledge source can provide great assistance in improving the predictive capacity of GP during the iterative process, particularly from the view of machine learning. An iterative algorithm is therefore proposed to improve the precision of GP approximation around the design point by constantly adding new design points to the initial training set. Examples are provided to illustrate the GP-based response surface for both structural and non-structural reliability analyses. The results show that the proposed approach is applicable to structural reliability analyses that involve implicit performance functions and structural response evaluations that entail time-consuming finite element analyses.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.30 no.8 s.251
• /
• pp.1001-1007
• /
• 2006

In this paper we present robust optimal supporting positions for large glass panels used for TFT-LCD monitors when they are stored in a cassette during manufacturing process. The criterion taken is to minimize their maximum deflection. Since they are supported by some supports and have large deformations, contact analysis with a geometrically nonlinear effect is necessary. In addition, the center of a panel can not be positioned exactly as intended and should be considered as uncertainties. To take into account of these effects, the mean and the standard deviation of system response functions, particularly the deflection of the panels, need be calculated. A function approximation moment method (FAMM) is utilized to estimate them. It is a special type of response surface methodology for structural reliability analysis and can be efficiently used to estimate the two stochastic properties, that is, the system performance and the perturbations caused by uncertainties. For a design purpose, they are to be minimized simultaneously by some optimization algorithm to obtain robust optimal supporting positions.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.19 no.6
• /
• pp.55-60
• /
• 2020

The injection molding technique is a processing method widely used for the production of plastic parts. In this study, the gate position, gate size, packing time, and melt temperature were optimized to minimize both the stress and deformation that occur during the injection molding process of medical suction device components. We used a central composite design and Latin hypercube sampling to acquire the data and adopted the response surface method as an approximation method. The efficiency of the optimization of the injection molding problem was determined by comparing the results of a genetic algorithm, sequential quadratic programming, and a non-dominant classification genetic algorithm.

• Proceedings of the KIEE Conference
• /
• 2007.04c
• /
• pp.74-76
• /
• 2007

A hybrid optimization method is proposed for cogging torque reducing in BLDC motor. The proposed hybrid optimization method comprises a response surface method (RSM) and a gradient search method (GSM). The RSM is effective and global method in optimization problem but having large approximation error. The GSM is accurate and fast search method for optimal solution but having local behavior. To reduce approximation error and computation time a hybrid method (RSM+GSM) is proposed method. To illustrate the effectiveness of the proposed method, a comparison between conventional RSM and the proposed hybrid method is made. A simulation results verify that the hybrid method can achieve favorable design performance.

• Proceedings of the KSME Conference
• /
• 2005.11a
• /
• pp.137.2-137.2
• /
• 2005