• Transactions of Materials Processing
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• v.29 no.2
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• Proceedings of the Korean Society for Quality Management Conference
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• 2009.10a
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• pp.3-10
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• 2009

When the measurement method for a performance variable is destructive or expensive, it is profitable to replace the performance variable with a highly correlated surrogate variable. In this paper we propose screening procedures using a surrogate variable with specified producer's and consumer's risks. Blending the basic concepts of acceptance sampling plans and screening procedures, the proposed model can be used effectively by quality professionals. Two models are considered: the normal model with dichotomous performance and continuous surrogate variables, and the bivariate normal model with continuous performance and surrogate variables. It is assumed the surrogate variable given the performance variable is normally distributed in the normal model, and performance and surrogate variables are jointly normally distributed in the bivariate normal model. For the two models, Producer's and consumer's risks are derived, and methods of finding the optimal screening procedures are presented. Numerical examples are also given.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.23-30
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• 2009

When the measurement method for a performance variable is destructive or expensive, it is profitable to replace the performance variable with a highly correlated surrogate variable. In this paper we propose screening procedures using a surrogate variable with specified producer's and consumer's risks. Blending the basic concepts of acceptance sampling plans and screening procedures, the proposed model can be used effectively by quality professionals. Two models are considered: the normal model with dichotomous performance and continuous surrogate variables, and the bivariate normal model with continuous performance and surrogate variables. It is assumed the surrogate variable given the performance variable is normally distributed in the normal model, and performance and surrogate variables are jointly normally distributed in the bivariate normal model. For the two models, producer's and consumer's risks are derived, and methods of finding the optimal screening procedures are presented. Numerical examples are also given.

• Journal of Ocean Engineering and Technology
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• v.35 no.2
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• pp.131-140
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• 2021

The paper deals with comparative study of various surrogate models based approximate optimization in the structural design of the passive type deck support frame under design load conditions. The passive type deck support frame was devised to facilitate both transportation and installation of 20,000 ton class topside. Structural analysis was performed using the finite element method to evaluate the strength performance of the passive type deck support frame in its initial design stage. In the structural analysis, the strength performances were evaluated for various design load conditions. The optimum design problem based on surrogate model was formulated such that thickness sizing variables of main structure members were determined by minimizing the weight of the passive type deck support frame subject to the strength performance constraints. The surrogate models used in the approximate optimization were response surface method, Kriging model, and Chebyshev orthogonal polynomials. In the context of numerical performances, the solution results from approximate optimization were compared to actual non-approximate optimization. The response surface method among the surrogate models used in the approximate optimization showed the most appropriate optimum design results for the structure design of the passive type deck support frame.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.3
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• pp.151-158
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• 1999

Biogenic source emissions refer to naturally occuring emissions from vegetation, microbial activities in soil, lightening, and so on. Vegetation is especially known to emit a considerable amout of volatile organic compounds into the atmosphere. Therefore, biogenic source emissions are an important input to photochemical air quality models. since most biogenic source emissions are calculated at the county-level, they should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation for biogenic source emissions has been to use a "spatial surrogate indicator" such as a county area. In order to examine the applicability of such approximations, this study developed more detailed surrogate indicators to improve the spatial allocation method for biogenic source emissions. Due to the spatially variable nature of biogenic source emissions, Geographic Information Systems(GIS) were introduced as new tools to develop more detailed spatial surrogate indicators. Use of these newly developed spatial surrogate indicators for biogenic source emission allocation provides a better resolution than the standard spatial surrogate indicator.indicator.

• Korean Journal of Veterinary Research
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• v.62 no.1
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• pp.5.1-5.5
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• 2022

Many countries have imposed regulations relating to concerns that hide contamination will affect the cleanliness of abattoirs. However, South Korea has not indicated any clear criteria. The purpose of this study is to use surrogate bacteria to measure the contamination in abattoirs caused by contaminated cattle hides. The swab contact method and plate count method are used. Surrogate bacteria are found in most internal environments after the final process. These surrogates remained on the carcass even after the final washing process. This paper is the first study in South Korea that use surrogate bacteria to analyze contamination levels in abattoirs.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4181-4194
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• 2022

Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.3
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• pp.143-150
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• 1999

Mobile source emissions are important inputs to photochemical air quality models. Since most mobile source emissions are calculated at the county-level, these emission should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation of these emissions has been to use a "spatial surrogate indicator" such as population, since grid-specific emission calculations are very labor-intensive and expensive, plus the necessary data are often not available for such grid resolutions. Accordingly, new spatial surrogate indicators for mobile source emissions(specifically for highway emissions) were developed using Geographic Information Systems(GIS) tools due to the spatially variable nature of mobile source emissions. These newly developed spatial surrogate indicators appear to be more appropriate for the allocation of highway emissions than the population surrogate indicator. It was also revealed that the conventional spatial allocation method underestimates the maximum levels of air pollutant emmissions.mmissions.

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.329-339
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• 2019

Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields; however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive Metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.