• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.395-396
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• 2006

This paper presents the optimal design of Robot-Arm part use Design of Experiment(DOE). The DOE(Design of Experiment)was conducted to find out main effect factors for design of Robot-Arm part. In this design of Robot-Arm, 5 control factors include numbers of 4 level are selected and we make out L16 orthogonal array. Using this orthogonal array, find out optimal value and main effect factors of object function for design of Robot-Arm part by 16 times of test. We evidence this optimal value by using CATIA V5 Analysis.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.8
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• pp.208-215
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• 1999

Using the orthogonal arrays and the occupant analysis software based on the multi-body dynamics , two interactive design algorithms are proposed to improve the initial design of the occupant protection systems. Algorithm 1 sequentially moves the narrow design space within the upper and the lower design limit. Algorithm 2 sequentially reduces the relatively wide design space. Each design algorithm is composed of two levels . The first level is to improve the characteristics of the crash performance considering the noise factors. In order to obtain the robust design, the second level reduces the variations the noise factors. In order to obtain the robust design, the second level reduces the variations due to the tolerance of the design variable. To utilize the algorithm 1, HIC(Head Injury Criterion) , 3 msec criterion value of the chest acceleration and the femur load decreased by 27.4%, 10.4% and 55.8%, respectively. To utilizer the algorithm 2 , the results decreased by 38.0%, 10.5% and 3.0% , respectively.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.142-145
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• 1997

This paper presents an modified orthogonal neural network(MONN) based on orthogonal functions and applies the network to nonlinear system control. The accuracy of orthogonal neural network is essentially dependent on the choice of basic orthogonal functions. Modified orthogonal neural network is modified model of orthogonal neural network with input transformation to adapt its basic orthogonal functions. The results show that the modified orthogonal neural network has the excellent performance of approximating and controlling nonlinear systems and the input transformation make the ability of modified orthogoneural neural network better than one of orthogonal neural network.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.708-713
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• 2000

The design of experiment(DOE) is getting more attention in the engineering community since it is easy to understand and apply. Recently, engineering designers are adopting DOE with orthogonal arrays when they want to design products in a discrete design space. In this research, a design flow with orthogonal arrays is defined fur structural design according to the general DOE. The design problem is defined as a general structural optimization problem. Sensitivity information is evaluated by the analysis of variance(ANOVA), and an optimum design is determined from analysis of means(ANOM). Interactions between design variables are investigated to achieve additivity which should be valid in DOE. When strong interactions exit, a method is proposed. Some methods to consider the problem are suggested.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.187-196
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• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.112-118
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• 2013

Formula race car is generally recognized as a vehicle which is optimally designed for on-road race track with the regulations of race host bodies. Especially, the uprights of suspension system decisively have effects on the performance of cornering and stability of race car's driving performance, which are very important factors in the design of race car. This paper is a study of optimal upright design of F1800 grade formula race car which are normally used in professional race circuit in Korea. To design optimally the front upright of F1800 formula race car, Taguchi's orthogonal array, which is known for more useful method than full factorial design experimental method in cost and time, is used with CAE method such as FEM analysis. And the result of this paper shows that Taguchi's orthogonal array employed for this optimal design is very useful for designing the front upright of race car by minimizing its weight as well as keeping its safety factor as enough as designer wants in the view of quality, cost and delivery at the early design step.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.91-100
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• 1994

The lapped orthogonal transform(LOT) has been recently proposed to alleviate the blocking effects in transform coding. The LOT is known to provide an improved coding gain than the conventional transform. In this paper, we propose a prefilter approach to design the LOT bases with the view of maximizing the transform coding gain. Since the nonlinear phase basis is inappropriate to the image coding only the linear phase basis is considered in this paper. Our approach is mainly based on decomposing the transform matrix into the orthogonal matrix and the prefilter matrix. And by assuming that the input is the 1st order Markov source we design the prefilter matrix and the orthogonal matirx maximizing the transform coding gain. The computer simulation results show that the proposed LOT provides about 0.6~0.8 dB PSNR gain over the DCT and about 0.2~0.3 dB PSNR gain over the conventional LOT [7]. Also, the subjective test reveals that the proposed LOT shows less blocking effect than the DCT.

• Transactions of Materials Processing
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• v.7 no.2
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• pp.127-138
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• 1998

Process design of multi-step wire drawing process, conducted by means of finite element analysis and ANN(Artificial Neural Network) has been considered. The investigated problem involves the ade-quate selection of the drawing die angle and the correspondent reduction rate in the condition of desired initial and final diameter. Combinations of the process parameters which are used in finite ele-ment simulation are selected by using the orthogonal array. Also the orthogonal array. Also the orthogonal array and the results of finite element simulation which are related to the process energy are used as train data of ANN. In this study it is shown that the application of new technique using ANN and Othogonal array table to the process design of metal forming process is useful method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.862-865
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• 2000

The object of this paper is to evaluate the surface roughness using the experimental equation of surface roughness, which is developed in turning by an orthogonal array method. $L_9{3^4}$ orthogonal array method, one of fractional factorial design has been used to study effects of main cutting parameters such as cutting speed, feed rate and depth of cut, on the surface roughness. And the analysis of variance (ANOVA)-test has been used to check the significance of cutting parameters. Using the result of ANOVA-test, the experimental equation of surface roughness, which consists of only significant cutting parameter - feed rate, has been developed. The coefficient of determination of this equation is 0.962.