• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.2
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• pp.68-74
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• 2013

In this paper, an average surface roughness, $R_a$, was measured by optical measurement and its mathematical model according to spindle speed and feedrate was obtained by least square method. Also, its result is compared and investigated with real measured average surface roughness. The optical measurement of surface roughness is performed by CLSM(confocal laser scanning microscope) and the captured HEI(height encoded image) data is used as an original data for the generation of average surface roughness and its mathematical plane or contour surface of surface roughness. Using this polynomial model with two independent variables, the behavior of an average surface roughness is investigated and analyzed with an experimental modeling of least square algorithm. And it can be used for the prediction of $R_a$ in different condition of machining.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.6
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• pp.159-167
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• 2008

Cooling lubricants are used in machining operations in order to reduce friction at the tool-chip and tool-workpiece interfaces, cool both chip and tool, and remove chip. Furthermore, they influence a strong effect on the shearing mechanisms and, consequently, on the machined surface quality and tool wear. However, several researchers state that the costs related to cutting fluids is frequently higher than those related to cutting tools. Moreover, the cooling lubricants cause an increase in both worker's health and social problems related to their use and correct disposal. Therefore, many researchers have focused on the environmentally conscious machining technologies. One of the technologies is known as MQL(Minimum Quantity Lubrication) machining. In this paper, an experimental model to obtain the optimal cutting conditions in MQL turning was suggested, and the effects of cutting conditions on surface roughness and cutting force were analyzed. For these purposes, FFD (Fractional Factorial Design) and RSM (Response Surface Methods) were used for the experiment. Cutting force and surface roughness with different cutting conditions were measured through the external cylindrical turning of Al 6061 based on the experiment plan. The measured data were analyzed by regression analysis and verification experiments with random conditions were conducted to confirm the suggested experimental model.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.12
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• pp.92-98
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• 2010

The design of press bonding tool in LCD module equipment is a very complex and difficult task because many design able variables are involved while their effects are not known. It takes longtime experiments and much expenses to verify the effects of these design variables. However the optimization of bonding tool using OLB(outer lead bonding) and PCB Bonding is a very important problem in LCD manufacturing process, so much design efforts have been made for improving the bonding tool performance. In this paper, a reasonable and fast process which gives optimized solution under the design requirements has been presented. Both analytical and statistical methods are employed in this process. A reliable analytic model using experiment-oriented FE analysis can be obtained, in which the regression equations that predict the tool efficiency from various DOE method are found. Improvement of tool efficiency could be estimated by the regression equations using meaningful factors converged by RSM(Response Surface Method). With this process a reasonable optimized solution that meets a variety of design requirements can be easily obtained.

• Geomechanics and Engineering
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• v.28 no.3
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• pp.313-324
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• 2022

Rock properties are important in the design of mines and civil engineering excavations to prevent the imminent failure of slopes and collapse of underground excavations. However, the time, cost, and expertise required to perform experiments to determine those properties are high. Therefore, empirical models have been developed for estimating the mechanical properties of rock that are difficult to determine experimentally from properties that are less difficult to measure. However, the inherent variability in rock properties makes the accurate performance of the empirical models unrealistic and therefore necessitate the use of soft computing models. In this study, Gaussian process regression (GPR), artificial neural network (ANN) and response surface method (RSM) have been proposed to predict the static and dynamic rock properties from the P-wave and rock density. The outcome of the study showed that GPR produced more accurate results than the ANN and RSM models. GPR gave the correlation coefficient of above 99% for all the three properties predicted and RMSE of less than 5. The detailed sensitivity analysis is also conducted using the RSM and the P-wave velocity is found to be the most influencing parameter in the rock mechanical properties predictions. The proposed models can give reasonable predictions of important mechanical properties of sedimentary rock.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.138-144
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• 2013

The automation technology for overlay welding is needed due to the occurrence of severe corrosion and abrasion on the surface of internal contact in different shape of fittings. In Korea, different shapes of fittings have been manufactured by using the imported equipment of overlay welding automation at some companies. Thus the research on the development of overlay welding automation system (in short, OWAS) for a large L-type tube is urgently needed. In this paper, the investigation is focused on the optimal design of a supporting base for the (currently developing) OWAS of large L-type tube. Specifically we assume that the base which supports the equipment during the process of overlay welding is loaded as self-weight in the direction of gravity through static analysis especially when it is rotated 180 degree on the OWAS. For optimal design of a supporting base for OWAS of large L-type tube, Solidworks(R) (for 3-dimensional modelling) and ANASYS Workbench(R) (for structural analysis) are incorporated so as to proceed an optimization routines based on Response Surface Method (RSM) and Design of Experiment (DOE). In more specific, DOE finds out major factors (or dimensions) of the supporting base by using MINITAB(R). Then the regression equations between design variables (the major factors of supporting base) and response variables (deformation, stress and safety factor for the supporting base), which will be resulted in by RSM, verify the major factors of DOE. In the next step, Central Composite Design (CCD) plans 20 simulations of ANASYS Workbench(R) and then figures out the optimal values of design variables which will be reflected on the manufacturing of supporting base. Finally welding experiment is conducted to figure out the influence of overlay welding quality in applying the optimized design values of supporting base to the actual OWAS.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• IE interfaces
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• v.9 no.1
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• pp.13-24
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• 1996

Quality of injection molded parts is dependent on both mold design and processing conditions. From the mold design point of view, an optimal shrinkage should be used to compensate the shrinkage of molded parts. From the processing point of view, it is important to analyze the priority of processing conditions because a number processing conditions affect the quality of molded parts. Processing analysis employing the design of experiment was performed, and the shrinkage of molded part was considered as a characteristic parameter to improve the quality. As the result of the analysis of variance on SN ratio of a characteristic value, injection speed and bolding pressure were selected as two effective process parameters. Regression analysis on shrinkage and processing conditions was carried out, and an optimal processing condition was obtained by the response surface analysis. Shrinkage at the optimal condition could be used to reduce the number of try-cut at the step of mold making. The ranges of indirect control parameter, such as maximum cavity pressure or weight, measured at the optimal processing condition were used for monitoring the quality of molded parts in process.

• Food Science of Animal Resources
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• v.38 no.2
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• pp.240-250
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• 2018

This study was undertaken to find the optimum soy-peptone, glucose, yeast extract, and magnesium sulfate amounts for the maximum growth of Lactobacillus plantarum JNU 2116 and to assess the effects of these medium factors through the use of response surface methodology. A central composite design was used as the experimental design for the allocation of treatment combinations. In the analysis of the experiment, due to a significant lack of fit of the second-order polynomial regression model that was used at first, cubic terms were added to the model, and then two-way interaction terms were deleted from the model since they were found to be all statistically insignificant. A relative comparison among the four factors showed that the growth of L. plantarum JNU 2116 was affected strongly by yeast extract, moderately by glucose and peptone, and slightly by magnesium sulfate. The estimated optimum amounts of the medium factors for the growth of L. plantarum JNU 2116 are as follows: soy-peptone 0.213%, glucose 1.232%, yeast extract 1.97%, and magnesium sulfate 0.08%. These results may contribute to the production of L. plantarum L67 as a starter culture that may have potential application in yogurt and fermented meat products.

• Natural Product Sciences
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• v.21 no.2
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• pp.141-145
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• 2015

Hesperidin, which is the most abundant flavonoid of Citrus unshiu (Rutaceae), has been reported to possess diverse activities and widely used as functional foods and cosmetics. For the development of functional products, extraction procedure is indispensable. Extraction conditions affect the composition of extract as well as its biological activity. Therefore, we tried to optimize extraction conditions such as extraction solvent, extraction time and extraction temperature for maximum yield of hesperidin using response surface methodology with threelevel-three-factor Box-Behnken design (BBD). Regression analysis showed a good fit of the experimental data and the optimal condition was obtained as ethanol concentration, 59.0%; temperature $71.5^{\circ}C$ and extraction time, 12.4 h. The hesperidin yield under the optimal condition was found to be $287.8{\mu}g$ per 5 mg extract, which was well matched with the predicted value of 290.5 μg. These results provides optimized extraction condition for hesperidin and might be useful for the development of hesperidin as functional products like health supplements, cosmetics and medicinal products.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.6
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• pp.461-468
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of the given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a surrogate model that has a regression model performed on a data sampling of the simulation. In general, metamodels(surrogate model) take the form y($\chi$)＝f($\chi$)＋$\varepsilon$, where y($\chi$) is the true output, f($\chi$) is the metamodel output, and is the error. In this paper, a second order polynomial equation is used as the RSM(response surface model) for high speed train that have twenty-nine design variables and forty-six responses. After the RSM is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called VMM(variable matric method) This paper shows that the RSM is a very efficient model to solve the complex optimization problem.