• Advances in nano research
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• v.12 no.4
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• pp.387-403
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• 2022

Silica nanoparticles, which have a broad range of sizes and specific surface features, have been used in many industrial applications. This study was conducted to synthesize monodispersed silica nanoparticles directly from tetraethyl orthosilicate (TEOS) with an alkaline catalyst (NH₃) based on the sol-gel process and the Stöber method. A central composite design (CCD) is used to build a second-order (quadratic) model for the response variables without requiring a complete three-level factorial experiment. The process was then optimized to achieve the minimum particle size with the lowest concentration of TEOS. Dynamic light scattering and scanning electron microscopy were used to analyze the size, dispersity, and morphology of the synthesized nanoparticles. After optimization, a confirmation test was carried out to evaluate the confidence level of the software prediction. The results revealed that the predicted optimization is consistent with experimental procedures, and the model is significant at the 95% confidence level.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Tribology and Lubricants
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• v.23 no.5
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• pp.234-239
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• 2007

Recently one of the automobile customer's greatest concerns is quietness in a vehicle along with various functional requirements such as comfort, stability, mobility etc. Therefore car makers place more weight on vehicle noise. Especially, in the case of diesel engine, as the noise level of engine becomes more silent by the development of engineering technology. The noise of alternator with vacuum pump has been able to be noticed enough becoming an issue on vehicles. In this study vacuum pump noise on alternator was identified andclassified into five types. DOE was applied to induce the statistical analysis result to reduce the vacuum pump noise. It was done by subjective listening. Design and statistical analysis are done with Minitab software. This work provides dominant elements of vacuum pump noise on alternator and can be the basis of furture studies.

• The KSFM Journal of Fluid Machinery
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• v.15 no.6
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• pp.58-63
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• 2012

This paper presents a numerical study on the performance improvement of axial-flow pump with guide vanes. Design optimization for guide vanes in an axial-flow pump has been studied through the implementation of a commercial CFD code and DOE (design of experiments). We also discussed how to improve the performance of the axial-flow pump by designing the guide vanes. Geometric design variables were defined by the meridional plane and vane plane development of guide vanes. The effect of hub tip ratio analyzed the meridional plane of guide vanes. The importance of the geometric design variables was analyzed using $2^k$ factorial designs. The objective functions for guide vane geometric variables were defined as the total efficiency and the total head at the design flow rate. From the $2^k$ factorial design results, the important design variables were found and the performance was increased in comparison with the base design model.

• Journal of Mechanical Science and Technology
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• v.20 no.3
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• pp.366-375
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• 2006

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps: the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.312-322
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• 2001

This paper investigates the combined effects of parameters such as die surface finish, die directionality of stoning contact pressure and draw speed on the resulting coefficient of friction. Also, this study show the correlation between the NN(Neural Network) and DOE(Design of Experiments) to reduce the number of experiments without the loss of the effects of parameter upon coefficient of friction. The experiments were run in random order with at least three replicates. It was found that the directional stoning orthogonal to the pulling direction is lower friction than directional stoning parallel to the pulling directi on using FCD55 die and SPCEN.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.814-820
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• 2000

This papers investigates the combined effects of parameters such as die surface finish, die directionality of stoning, contact pressure and draw speed on the resulting friction factor. Also, this study show the correlation between the NN(Neural Network) and DOE (Design of Experiments) to reduce the number of experiments without the loss of the effects of parameter upon friction factor. The experiments were run in random order with at least three replicates. It was found that the directional stoning orthogonal to the pulling direction is lower friction than directional stoning parallel to the pulling direction using FCD55 die and SPCEN

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.605-607
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• 2011

A Study on the flow-structure characteristics of a 3/2 way pneumatic valve is essential for optimizing the performance of ship engines. It is important for the valve to have desirable safety factor am reduced weight from the safety and economic point of view. In this study, we capture flow-structure characteristics of 3/2 way pneumatic valve. This is optimized based on the proper design criteria. The air at a pressure of 30 bar is the working fluid which is made to fill in the tack in short time. This time is defined as the filling time. The flow and structure analysis is performed for three cases under maximum stress and safety factor. In optimum design, considering the flow-structure characteristics, we model twenty seven cases by using DOE(design of experiments) method Here, analysis for each cases is performed and then metamodels are created We obtain optimized parameters and then analysis is repeated to compare with the initial model. Finally, the feasibility of the optimum design is verified.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.4
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• pp.291-297
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• 2014

A Design of Experiments(DOE) approach to an experimental study of fuselage drag and stability characteristics of a helicopter configuration was applied to achieve an accuracy improvement in the wind tunnel testing. The impact of blocking the test was assessed by comparing the ANOVA table for the blocked and unblocked cases. For a second-order response model, the role of blocking resulted in a substantial increase in the accuracy of test results. These accuracy improvement could be achieved through randomization, blocking, and replication of the data points i.e. a re-ordering of the test sequence where the data were acquired.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.10
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• pp.831-842
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• 2015

An optimization process was developed to improve mixed-flow pump performance. The optimization process was combined with CFX (a computational fluid dynamics (CFD) code) and HEEDS (an optimization code). CFX is a widely used CFD software for turbo machinery, whereas HEEDS, which uses the SHERPA algorithm, is a newly introduced optimization code. HEEDS can use a large number of optimization variables; thus, it is possible to effectively consider interaction effects. In this paper, an impeller model, which is already optimized with design of experiments (DOE), is used as the base model. The optimization process developed in this paper shows an improved design within an acceptable timeframe.