• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.527-538
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• 2019

This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

• Journal of Korean Society for Quality Management
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• v.37 no.1
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• pp.29-39
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• 2009

This article investigates economic-statistical design of VSSI(variable sampling size and interval) cause-selecting charts considering two assignable causes. We consider a process which is composed of two dependent sub-processes. In each sub-process, two kinds of assignable cause may exist. We propose a procedure for designing VSSI cause-selecting charts, based on Lorenzen and Vance model. Computational experiments show that the VSSI cause-selecting chart is superior to the FSSI cause-selecting chart in the economic-statistical characteristics, even under two assignable causes.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.125-136
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• 2008

In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by real/computational experiments such as structural analysis, fluid mechanic analysis, thermodynamic analysis, and so on. These experiments are, in general, considerably expensive. In order to make the number of these experiments as few as possible, optimization is performed in parallel with predicting the form of objective functions. Response Surface Methods (RSM) are well known along this approach. This paper suggests to apply Support Vector Machines (SVM) for predicting the objective functions. One of most important tasks in this approach is to allocate sample data moderately in order to make the number of experiments as small as possible. It will be shown that the information of support vector can be used effectively to this aim. The effectiveness of our suggested method will be shown through numerical example which is well known in design of engineering.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.54-59
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• 2004

A measure for good products manufactured by grinding process is the surface roughness that is affected by a lot of operating parameters such as types of abrasive, grain size, bond material, wheel speed, table speed, depth of cut, hardness of workpiece and stiffness of grinding machine. In this study, an application of the design of experiments was tried for evaluating the effect of operating parameters on the surface roughness. The workpiece was a high speed tool steel(SKH51) and the surface grinding was conducted. In order to obtain the best surface roughness within constraints of the working range, the optimal grinding conditions were selected. The usefulness of this method was evaluated by the statistical strategy.

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.122-128
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• 2006

One feature of a computer simulation experiment, different from a physical experiment, is that the output is often deterministic. Moreover the codes are computationally very expensive to run. This paper deals with the design and analysis of computer experiments(DACE) which is a relatively new statistical research area. We model the response of computer experiments as the realization of a stochastic process. This approach is basically the same as using a spatial linear model. Applications to the optimal mechanical designing and model calibration problems are illustrated. Algorithms for selecting the best spatial linear model are also proposed.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.201-212
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• 2006

The technique of foldover is usually used by experimenters to de-alias the effects that are interesting in follow-up experiment. Employing a $2^{k-p}$ design with resolution III or higher, Li and Lin (2003) developed an algorithm and used computer programs to search its corresponding optimal foldover design for selected 16-run and 32-run experiments. Based on the minimum aberration criterion, the strong combined-optimal design, defined by Li and Lin, is the better choice of the initial design. In this article, we apply the technique of blocking to find the strong combined-optimal designs. Furthermore, we will tabulate all 16-run and 32-run strong combined-optimal designs and their corresponding core foldover plans for practical use. Some new designs that have not appeared in the other literature but constructed by the technique of blocking are also proposed in this article.

• Journal of Microbiology and Biotechnology
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• v.22 no.12
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• pp.1767-1775
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• 2012

The Cr(VI) removal capability of Acinetobacter junii VITSUKMW2 isolated from the Sukinda chromite mine site was evaluated and enhanced using statistical design techniques. The removal capacity was evaluated at different pH values (5-11) and temperatures ($30-40^{\circ}C$) and with various carbon and nitrogen sources. Plackett-Burman design was used to select the operational parameters for bioremediation of Cr(VI). Three parameters (molasses, yeast extract, and Cr(VI) concentration) were chosen for further optimization using central composite design. The optimal combination of parameters was found to be 14.85 g/l molasses, 4.72 g/l yeast extract, and 54 mg/l initial Cr(VI), with 99.95% removal of Cr(VI) in 12 h. A. junii VITSUKMW2 was shown to have significant potential for removal of Cr(VI).

• Journal of the Korean Society for Precision Engineering
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• v.30 no.11
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• pp.1179-1185
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• 2013

In this study, the design optimization of the automotive side member is performed to maximize the crash energy absorption efficiency per unit weight. Design parameters which seriously influence on the frontal crash performance are selected through the sensitivity analysis using the Plackett-Burman design method. And also the design variables, which are determined from the sensitivity analysis, are optimized by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using micro-genetic algorithm. The proposed optimization technique shows that the automotive side member structure can be designed considering the frontal crash performance.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.13-28
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• 2002

Higher efficiency and effectiveness of Research & Development phases can be attained using advanced statistical methodologies. In this work statistical methodologies are combined with a deterministic approach to engineering design. In order to show the potentiality of such integration, a simple but effective example is presented. It concerns the problem of optimising the performances of a paper helicopter. The design of this simple device is not new in quality engineering literature and has been mainly used for educational purposes. Taking full advantage of fundamental engineering knowledge, an aerodynamic model is originally formulated in order to describe the flight of the helicopter. Screening experiments were necessary to get first estimates of model parameters. Subsequently, deterministic evaluations based on this model were necessary to set up further experimental phases needed to search (or a better design. Thanks to this integration of statistical and deterministic phases, a significant performance improvement is obtained. Moreover, the engineering knowledge かms out to be developed since an explanation of the “why” of better performances, although approximate, is achieved. The final design solution is robust in a broader sense, being both validated by experimental evidence and closely examined by engineering knowledge.