• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.4
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• pp.10-15
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• 2013

For design optimization, engineers should require the accurate information of design space and then explore the design space and carry out optimization. Recently, the total design framework, based on design of experiments and optimization, is widely used in industry areas to explore the design space above all. For optimizing turbofan engine design point, the response surface model is constructed by using the 7 level orthogonal array which satisfies the statistical uniformity and orthogonality and gets the dense design space information. The multi-objective genetic algorithm is used to find the optimal solution within the given constraints for finding global optimal one in response surface model. The optimal solution from response surface model is verified with GasTurb simulation result.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.145-161
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• 2021

Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg·m-3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.

• Structural Engineering and Mechanics
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• v.67 no.3
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• pp.301-310
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• 2018

In this paper, a multi-objective multiparameter optimization procedure is developed by combining rigorously developed metamodels with an evolutionary search algorithm-Genetic Algorithm (GA). Response surface methodology (RSM) is used for developing the metamodels to replace the tedious finite element analyses. A nine-node isoparametric plate bending element is used for conducting the finite element simulations. Highly accurate numerical data from an author compiled FORTRAN finite element program is first used by the RSM to develop second-order mathematical relations. Four material parameters-${\frac{E_1}{E_2}}$, ${\frac{G_{12}}{E_2}}$, ${\frac{G_{23}}{E_2}}$ and ${\upsilon}_{12}$ are considered as the independent variables while simultaneously maximizing fundamental frequency, ${\lambda}_1$ and frequency separation between the $1^{st}$ two natural modes, ${\lambda}_{21}$. The optimal material combination for maximizing ${\lambda}_1$ and ${\lambda}_{21}$ is predicted by using a multi-objective GA. A general sensitivity analysis is conducted to understand the effect of each parameter on the desired response parameters.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1586-1595
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• 2018

For the problem that the complexity of 3-D modeling and multi parameter optimization, as well as the uncertainty of the winding factor of axial flux permanent magnet generator with coreless windings. The complex 3-D model was simplified into 2-D analytic model, and an analytical formula for the winding factor that adapting different coreless stator winding is proposed in this paper. The analytical solution for air-gap magnetic fields, no-load back EMF, electromagnetic torque, and efficiency are calculated by using this method. The multiple objective and multivariable optimization of the maximum fundamental and the minimum harmonic content of back EMF are performed by using response surface methodology. The proposed optimum design method was applied to make a generator. The generator was tested and the calculated results are compared with the proposed method, which show good agreements.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.6
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• pp.461-468
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of the given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a surrogate model that has a regression model performed on a data sampling of the simulation. In general, metamodels(surrogate model) take the form y($\chi$)＝f($\chi$)＋$\varepsilon$, where y($\chi$) is the true output, f($\chi$) is the metamodel output, and is the error. In this paper, a second order polynomial equation is used as the RSM(response surface model) for high speed train that have twenty-nine design variables and forty-six responses. After the RSM is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called VMM(variable matric method) This paper shows that the RSM is a very efficient model to solve the complex optimization problem.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.895-905
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• 2023

Mud tanks used for storing and supplying mud in mud supply systems are essential to secure structural stability according to the mud loads inside the tank. In terms of structural stability of the mud tank can be ensured by increasing the thickness of the structure. However, increasing the thickness may cause a problem of increasing production costs. In addition, this increases the weight of the tank, which can cause problems with the trailer loading weight limitation during transportation. To satisfy both these problems and structural stability, the mud tank should be optimally designed. Therefore, this study conducted an optimum design in consideration of the load of the mud tank through the structural analysis and response surface optimization method in ANSYS.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1793-1799
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• 2003

An optimal design of a multi-layered plate structure to endure high-velocity impact has been suggested by using size optimization after numerical simulations. The NET2D, a Lagrangian explicit time-integration finite element code for analyzing high-velocity impact, was used to find the parameters for the optimization. Three different materials such as mild steel, aluminum for a multi-layered plate structure and die steel for the pellet, were assumed. In order to consider the effects of strain rate hardening, strain hardening and thermal softening, Johnson-Cook model and Phenomenological Material Model were used as constitutive models for the simulation. It was carried out with several different gaps and thickness of layers to figure out the trend in terms of those parameters' changes under the constraint, which is against complete penetration. Also, the measuring domain has been shrunk with several elements to reduce the analyzing time. The response surface method based on the design of experiments was used as optimization algorithms. The optimized thickness of each layer in which perforation does not occur has been obtained at a constant velocity and a designated total thickness. The result is quite acceptable satisfying both the minimized deformation energy and the weight criteria. Furthermore, a conceptual idea for topology optimization was suggested for the future work.

• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.4
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• pp.184-189
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• 2004

In this paper, multi-object optimization based on a progressive quadratic response surface method (PQRSM) and a time stepping finite element method (FEM) is proposed. The new PQRSM and FEM are able to decide optimal geometric and electric variables of the switched reluctance motor (SRM) with two objective functions: torque ripple minimization and average torque maximization. The result of the optimum design for SRM demonstrates improved performance of the motor and enhanced relationship between torque ripple and average torque.