• Transactions of Materials Processing
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• v.30 no.1
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• pp.27-34
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• 2021

In this study, response surface method (RSM) was used in modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goals of optimization were the maximum forming angle, minimum thickness reduction, and minimum surface roughness, with varying values in response to changes in production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model for modeling the variations in the forming angle, thickness reduction, and surface roughness in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process based on experimental design. The results showed that RSM can be effectively used to control the forming angle, thickness reduction, and surface roughness.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.438-447
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• 2008

This study is focused on reliability based design optimization (RBDO) using moving least squares. A response surface is used to derive a limit-state equation for reliability based design optimization. Response surface method (RSM) with least square method (LSM) or Kriging will be used as a response surface. RSM is fast to make the response surface. On the other hand, RSM has disadvantage to make the response surface of nonlinear equation. Kriging can make the response surface in nonlinear equation precisely but needs considerable amount of computations. The moving least square method (MLSM) is made of both methods (RSM with LSM+Kriging). Numerical results by MLSM are compared with those by LMS in Rosenbrock function and six-hump carmel back function. The RBDO of engine duct of smart UAV is pursued in this paper. It is proved that RBDO is useful tool for aerospace structural optimal design problems.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.13 no.3
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• pp.22-33
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• 2005

Response Surface Method (RSM) constructs approximate response surfaces using sample data from experiments or simulations and finds optimum levels of process variables within the fitted response surfaces of the interest region. It will be necessary to get the most suitable response surface for the accuracy of the optimization. The application of RSM plan experimental designs. The RSM is used in the sequential optimization process. The first goal of this study is to improve the plan of central composite designs of experiments with various locations of axial points. The second is to increase the optimal efficiency applying a modified method to update interest regions.

• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.2
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• pp.116-129
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• 2001

Traditional occupant analysis has been performed with a pre-determined crash puse which is produced from a test and the involved components are designed based on the analysis resuls. The method has limitations in that the design does not have much freedom. Howrver, if a good crash pulse is proposed, the body structure can be modified to generate the crash pulse. Therefore, it is assumed that the crash pulse can be changed to imptove the occupant crash performance. A preferable crash pulse is determined to minimize the occupant injuty. A constraint is established to keep the phenomena of physics valid. The response surface method(RSM) is adopted for the optimization process. An RSM in a commercial code is utilzed by interfacing with an in-house occupant analysis program called SAFE(Safety Analysis For occupant crash Enviroment). Design of involved components called is carried out through optimization with the RSM. The advantages of the RSM are investigated as opposed to other methods, and the tesults are compared. Also, the design under the new crach pulse is compared with that trom the pre-detetmined pulse.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• The KSFM Journal of Fluid Machinery
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• v.16 no.4
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• pp.22-27
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• 2013

The purpose of this paper is to perform the structural design of the small scale vertical-axis wind turbine (VAWT) blade using a response surface method(RSM). First, the four design factors that have a strong influence on the structural response of blade were selected. Analysis conditions were calculated by using the central composite design(CCD), which is a typical design of experiment for the response surface method(RSM). Also, the significance of the central composite design(CCD) was verified using analysis of variance(ANOVA). The finite element analysis was performed for the selected analytical conditions for the application of response surface method(RSM). Finally, a optimization problem was solved with a objective function of blade weight and a constraint of allowable stress to achieve a optimal structural design of blade.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.580-585
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• 2001

A response surface method(RSM) is utilized for structural optimization and implemented on a parametric CAD platform. Once an approximation of the performance function is made, no formal design sensitivity analysis is necessary. The approximation gives the designer the sensitivity information and furthermore intuition on the performance functions. The scheme for the design of experiment chosen for the RSM has a large influence on the accuracy of converged solutions and the amount of computation. The D-optimal design criterion as implemented in this paper is found efficient for the structural optimization. The program is developed on a parametric CAD platform and tested using several shape design problems of such as a torque arm and a belt clip. It is observed that the RSM used provides a faster convergence than other approximation methods for design sensitivity.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.69-75
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• 2006

This paper deals with the optimum design solution on reduction of torque ripple for a Synchronous Reluctance Motor with concentrated winding using response surface methodology. The coupled Finite Elements Analysis (FEA) & Preisach model have been used to evaluate the nonlinear solution. Comparisons are given with characteristics of a SynRM according to the stator winding, slot number, open width of slot, slot depth, teeth width variation in concentrated winding SynRM, respectively. This paper presents an optimization procedure using Response Surface Methodology (RSM) to determine design parameters for reducing torque ripple. RSM has been achieved to use the experimental design method in combination with finite Element Method (FEM) and well adapted to make analytical model for a complex problem considering a lot of interaction of design variables. Moreover, Sequential Quadratic Problem (SQP) method is used to solve the resulting of constrained nonlinear optimization problem.

• International Journal of Automotive Technology
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• v.6 no.6
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• pp.643-655
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• 2005

The frontal crash optimization of S-shaped closed-hat section member using the homogenization method, design of experiment (DOE) and response surface method (RSM) was studied. The optimization to effectively absorb more crash energy was studied to introduce the reinforcement design. The main focus of design was to decide the optimum size and thickness of reinforcement. In this study, the location of reinforcement was decided by homogenization method. Also, the effective size and thickness of reinforcements was studied by design of experiments and response surface method. The effects of various impact velocity for reinforcement design were researched. The high impact velocity reinforcement design showed to absorb the more crash energy than low velocities design. The effect of size and thickness of reinforcement was studied and the sensitivity of size and thickness was different according to base thickness of model. The optimum size and thickness of the reinforcement has shown a direct proportion to the thickness of base model. Also, the thicker the base model was, the effect of optimization using reinforcement was the bigger. The trend curve for effective size and thickness of reinforcement using response surface method was obtained. The predicted size and thickness of reinforcement by RSM were compared with results of DOE. The results of a specific dynamic mean crushing loads for the predicted design by RSM were shown the small difference with the predicted results by RSM and DOE. These trend curves can be used as a basic guideline to find the optimum reinforcement design for S-shaped member.