• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.876-888
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• 2001

For a large scaled optimization based on response surface methods, an efficient quadratic approximation method is presented in the context of the trust region model management strategy. If the number of design variables is η, the proposed method requires only 2η+1 design points for one approximation, which are a center point and tow additional axial points within a systematically adjusted trust region. These design points are used to uniquely determine the main effect terms such as the linear and quadratic regression coefficients. A quasi-Newton formula then uses these linear and quadratic coefficients to progressively update the two-factor interaction effect terms as the sequential approximate optimization progresses. In order to show the numerical performance of the proposed method, a typical unconstrained optimization problem and two dynamic response optimization problems with multiple objective are solved. Finally, their optimization results compared with those of the central composite designs (CCD) or the over-determined D-optimality criterion show that the proposed method gives more efficient results than others.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.3040-3052
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• 2000

For effective construction of second-order response surface models, an efficient quad ratic approximation method is proposed in the context of trust region model management strategy. In the proposed method, although only the linear and quadratic terms are uniquely determined using 2n+1 design points, the two-factor interaction terms are mathematically updated by normalized quasi-Newton formula. In order to show the numerical performance of the proposed approximation method, a sequential approximate optimizer is developed and solves a typical unconstrained optimization problem having 2, 6, 10, 15, 30 and 50 design variables, a gear reducer system design problem and two dynamic response optimization problems with multiple objectives, five objectives for one and two objectives for the other. Finally, their optimization results are compared with those of the CCD or the 50% over-determined D-optimal design combined with the same trust region sequential approximate optimizer. These comparisons show that the proposed method gives more efficient than others.

• Environmental Engineering Research
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• v.23 no.3
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• pp.309-315
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• 2018

Chlortetracycline (CTC) is one of the most important compounds in antibiotic production, and its distribution has been widely investigated due to health and ecological concerns. This study presents systematic approach to optimize the high-performance liquid chromatography-tandem mass spectrometry for analyzing CTC in a multiple reaction monitoring mode ($479{\rightarrow}462m/z$). One-factor-at-a-time (OFAT) test with response surface analysis (RSA) was used as optimization strategy. In OFAT tests, the fragmentor voltage, collision energy, and ratio of acetonitrile in the mobile phase were selected as major factors for RSA. The experimental conditions were determined using a composite in cube design (CCD) to maximize the peak area. As a result, the partial cubic model precisely predicted the peak area response with high statistical significance. In the model, the (solvent composition) and (collision $energy^2$) terms were statistically significant at the 0.1 ${\alpha}$-level, while the two-way interactions of the independent variables were negligible. By analyzing the model equation, the optimum conditions were derived as 114.9 V, 15.7 eV, and 70.9% for the fragmentor voltage, collision energy, and solvent composition, respectively. The RSA, coupled with the CCD, offered a comprehensive understanding of the peak area that responds to changes in experimental conditions.

• Earthquakes and Structures
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• v.25 no.6
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.28-46
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• 1997

This paper considers robust design strategies for off-line quality control, with the use of experimental design and response surface methodology, in situations where all products have multiple quality characteristics. These strategies can be developed using the desirability concept of desirability functions to determine the settings of the design factors, not only to get the average performances on target but also to minimize variability around the target values.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.207-208
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• 2010

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.2
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• pp.14-21
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• 2019

In recent years, the use of composite structures has become commonplace in various fields such as aerospace, architecture, and civil engineering. In this study, A method is proposed to find optimal position of bolt hole for fastening of composite structure. In the case of composites, stress distribution is very complicated, and design optimization based on this phenomenon increases difficulty. In selecting the optimum position of the bolt hole, the response surface method(rsm), which is a method of optimization, was applied. A response surface was created based on design points by multiple finite element analyzes. The position of the bolt hole that minimizes the stress when bolting on the response surface was found. The distribution of the stress at the position of the optimal hole was much lower than that of the initial design. Based on the results of this study, it is possible to increase the design safety factor of the structure by appropriately selecting the position of the bolt hole according to various load types when designing the structure and civil structure.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.6
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• pp.697-704
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• 2004

Bifurcation behavior of ignition and extinction of catalytic reaction is theoretically investigated in a stagnation-point flow. Considering that reaction takes place only on the catalytic surface, where conductive heat losses are allowed to occur, activation energy asymptotics with a overall one-step Arrhenius-type catalytic reaction is employed. For the cases with and without the limiting reactant consumption, the analysis provides explicit expressions, which indicate the possibility of multiple steady-state solution branches. The difference between the solutions with and without reactant consumption is in the existence of an upper solution branch, and the neglect of reactant consumption is inappropriate for determining extinction conditions. For larger values of reactant consumption, the solution response is all monotone, suggesting that multiple solutions are not possible. It is shown that bifurcation Damkohler numbers increase (decrease) with increasing of conductive heat loss (gain) on the catalytic surface, which means that smaller (larger) values of the strain rate allow the surface reaction to tolerate larger heat losses (gains). Lewis number of the limiting reactant can also significantly affect bifurcation behavior in a similar way to the effect of heat loss.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.95-104
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• 2005

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation which may amplify the variance of response. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameter setting, a new desirability function is proposed by considering POE as well as distance-to-target of response and response variance. The proposed method is illustrated using a rubber product case in Ribeiro et al. (2000).