• Transactions of Materials Processing
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• v.29 no.5
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• pp.241-250
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• 2020

In this study, the methodology of the springback prediction of automotive parts applied 3rd generation AHSS was investigated using the response surface model analysis based on a regression model, and the meta model analysis based on a Kriging model. To design the learning data set for constructing the springback prediction models, and the experimental design was conducted at three levels for each processing variable using the definitive screening designs method. The hat-shaped member, which is the basic shape of the member parts, was selected and the springback values were measured for each processing type and processing variable using the finite element analysis. When the nonlinearity of the variables is small during the hat-shaped member forming, the response surface model and the meta model can provide the same processing parameter. However, the accuracy of the springback prediction of the meta model is better than the response surface model. Even in the case of the simple shape parts forming, the springback prediction accuracy of the meta model is better than that of the response surface model, when more variables are considered and the nonlinearity effect of the variables is large. The efficient global optimization algorithm-based Kriging is appropriate in resolving the high computational complexity optimization problems such as developing automotive parts.

• Journal of Dairy Science and Biotechnology
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• v.26 no.2
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• pp.51-56
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• 2008

Using central composite design, we have designed optimization of the manufacturing of processed butter. And response surface analysis by least-square regression was used Statistical Analysis System(SAS). Central composite design can be achieved by response surface techniques that allow flexibility in modeling and analysis. Response surface methodology(RSM) was used to optimize hardness(%) using as independent variables; the content of butter($X_1$), ranging from 50 to 90(%), the content of soybean oil($X_2$), from 0 to 20(%), and the hydrogenated soybean oil($X_3$) from 0 to 4(%). The results on the regression coefficients calculated for overrun by response surface by least-square regression(RSREG) were followed. It was considered that the linear regression was significant(p<0.01). As for the processed butter, the regression model equation for the hardness(Y, %) to the change of an independent variable could be predicted as follow: $Y=60.88-8.92X_2-{29.3X_2}^2$. The optimal for the manufacturing of processed butter were determined at the content of butter of 88.22%, soybean oil of 6.71% and hydrogenated soybean oil of 2.36%, respectively. Optimum compositions were resulted in hardness of 65.78 N. Finally the reference sample(Butter in the morning, Seoul Dairy Co-op.) and processed butter manufacturing under the optimal conditions were compared with spreadability test. The spreadability scores result from reference sample and butter under optimal conditions was not found a significant difference.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.11 s.170
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• pp.1871-1877
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• 1999

An optimal design method using a response surface method for dynamic characteristics of a washer is presented. The proposed method uses the design of experiment and a computer model is used for the experiment. The value of the cost function is estimated using a computer model for each case of the design variable variation. An orthogonal array is used to obtain best cases to be considered with minimum number of experimentation. Using these experimental values, optimal design is performed using a response surface method. The method used in this paper can be applied to any complicated mechanical systems that can be modelled and analyzed by a computer program. The method is applied to the design of dynamic characteristics of a washer.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1430-1435
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• 2003

Satellite upper platform is optimized by response surface method which has non-gradient, semi-glogal, discrete and fast convergency characteristics. Sampling points are extracted by design of experiments using Central Composite Method and Factorial Design. Also response surface is generated by the various regression functions. Structure analysis is execuated with regard for static and dynamic environment in launching stage. As a result response surface method is superior to other optimization method with respect to optimum value and cost of computation time. Also a confidence is varified in the various regression models.

• Food Science and Biotechnology
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• v.14 no.1
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• pp.28-31
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• 2005

To extract apple pectins, apple pomace (AP) was extruded under 14 different conditions of screw speed (250-350 rpm), feed rate of 30-40 kg/hr, and 20-30% moisture content using twin-screw extrusion. Response surface methodology (RSM), based on three variables by three-level factorial design, was employed to investigate effects of screw speed, feed rate, and moisture on dependent variables of extrudates, soluble dietary fiber (SDF), yield of anhydrogalacturonic acid ($Y_{AGA}$) representing pectin, and intrinsic viscosity ([${\eta}$]). Second order models were used to generate three-dimensional response surface for dependent variables, and their coefficients of determination ($R^2$) ranged from 0.96 to 0.99. Moisture content showed highest effect on solubilization of AP.

• Smart Structures and Systems
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• v.25 no.6
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• pp.679-691
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• 2020

To identify the bridge information from the response of test vehicles passing on it (also known as the indirect approach) has aroused the interest of many researchers thanks to its economy, easy implementation and less disruption to traffic. The surface roughness of bridge remains an obstacle for such method as it contaminates the vehicle response severely and thereby renders many vehicle-response-based bridge identification methods ineffective. This study aims to eliminate such effect with the responses of two different test vehicles. The proposed method can estimate the surface profile of a bridge based on the acceleration data of the vehicles running on the bridge successively, and obtain the normalized contact point response, which proves to be relatively immune to surface roughness. The frequencies and mode shapes of bridge can be further extracted from the normalized contact point acceleration with spectral analysis and Hilbert transform. The effectiveness of the proposed method is verified numerically with a three-span continuous bridge. The influence of measurement noise is also examined.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.1
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• pp.34-42
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• 2007

Powder blasting technique has been considered one of the most appropriate micro machining methods for hard and brittle materials, since the productivity is high and the heat layers caused by material removal are very thin. Recent development of special purposed parts, such as the parts for semiconductor processing, the parts for LCD, sensors for micro machine fabrication and so on, has been expanded. Thus, it is essential to develop powder blasting technologies for micromachining of hard and brittle materials such as glass, ceramics and so on. In this paper, the characteristics of powder blasted glass surface were tested under various blasting parameters. Finally, we proposed a predictive model for powder blasting process using the neural network and the response surface method. Detail analysis of the simulation results is carried out and the performance of two predictive models is compared.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.3
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• pp.166-171
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• 2008

In this paper, the optimization design module for the brake system of a vehicle is developed. As using this module, design variables, that minimize an object function and satisfy nonlinear constraint conditions, can be found easily. Before an optimization is operated, Plackett-Burman design, one of the factorial design methods, is used to choose the design variables which affect a response function significantly. Using the response surface analysis, second order recursive model function, which informs a relation between design variables and response function, is estimated. In order to verify the reliability of the model function, analysis of variances(ANOVA) table is used. The value of design variables which minimize the model function and satisfy the constraint conditions is predicted through Sequential Quadratic-Programming (SQP) method. As applying the above procedure to a real vehicle simulation model and comparing the values of object functions of a current and optimized system, the optimization results are verified.

• Journal of Microbiology and Biotechnology
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• v.12 no.3
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• pp.449-456
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• 2002

The objective of this study was to optimize medium composition of initial pH, tryptone, glucose, yeast extract, and mineral mixture for production of bacteriocin by Lactobacillus acidophilus ATCC 4356, using response surface methodology. A response surface approach including new statistical and plotting methods was employed for design and analysis of the experiment. An interiorly augmented central composite design was used as an experimental design. A normal-distribution log-link generalized linear model based on a subset fourth-order polynomial ($R^2$=0.94, Mean Error Deviance=0.0065) was used as an analysis model. This model was statistically superior to the full second-order polynomial-based generalized linear model ($R^2$=0.80, Mean Error Deviance=0.0140). Nonlinear programming determined the optimum composition of the medium as initial pH 6.35, typtone $1.21\%$, glucose $0.9\%$, yeast extract $0.65\%$, and mineral mixture $1.17\%$. A validation experiment confirmed that the optimized medium was comparable to the MRS medium in bacteriocin production, having the advantage of economy and practicality.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.6
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• pp.311-322
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• 2018

As a method of seismic-design for pile-supported wharves, equivalent static analysis, response spectrum analysis, and time history analysis method are applied. Among them, the response spectrum analysis is widely used to obtain the maximum response of a structure. Because the ground is not modeled in the response spectrum analysis of pile-supported wharves, the amplified input ground acceleration should be calculated by ground classification or seismic response analysis. However, it is difficult to calculate the input ground acceleration through ground classification because the pile-supported wharf is build on inclined ground, the methods to calculate the input ground acceleration proposed in the standards are different. Therefore, in this study, the dynamic centrifuge model tests and the response spectrum analysis were carried out to calculate the appropriate input ground acceleration. The pile moment in response spectrum analysis and the dynamic centrifuge model tests were compared. As a result of comparison, it was shown that the response spectrum analysis results using the amplified acceleration in the ground surface were appropriate.