• Journal of Integrative Natural Science
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• v.12 no.3
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• pp.69-73
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• 2019

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Taguchi has dealt with having constraints on both the mean and variability of a characteristic (the dual response problem) by combining information on both mean and variability into an SN. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for parameter design to solve the dual response problems without resorting to SN.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.11-18
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• 2001

Taguchi parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used signal-to-noise(SN) ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many statisticians criticize the Taguchi techniques of analysis, particularly those based on SN ratio. In this paper we propose a substantially simpler optimization procedure for parameter design without resorting to SN ratio.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.829-835
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• 2009

Parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used the signal-to-noise ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. However, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and control factors (design factors), and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Structural Engineering and Mechanics
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• v.53 no.6
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• pp.1127-1141
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• 2015

The optimum design of liquid column dampers in seismic vibration control considering system parameter uncertainty is usually performed by minimizing the unconditional response of a structure without any consideration to the variation of damper performance due to uncertainty. However, the system so designed may be sensitive to the variations of input system parameters due to uncertainty. The present study is concerned with robust design optimization (RDO) of liquid column vibration absorber (LCVA) considering random system parameters characterizing the primary structure and ground motion model. The RDO is obtained by minimizing the weighted sum of the mean value of the root mean square displacement of the primary structure as well as its standard deviation. A numerical study elucidates the importance of the RDO procedure for design of LCVA system by comparing the RDO results with the results obtained by the conventional stochastic structural optimization procedure and the unconditional response based optimization.

• Structural Engineering and Mechanics
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• v.64 no.5
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• pp.567-577
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• 2017

The traditional design procedure of a prestressed concrete (PC) cable-stayed bridge is complex and time-consuming. The designers have to repeatedly modify the configuration of the large number of design parameters to obtain a feasible design scheme which maybe not an economical design. In order to efficiently achieve an optimum design for PC cable-stayed bridges, a multi-parameter optimization technique is proposed. In this optimization technique, the number of prestressing tendons in girder is firstly set as one of design variables, as well as cable forces, cable areas and cross-section sizes of the girders and the towers. The stress and displacement constraints are simultaneously utilized to ensure the safety and serviceability of the structure. The target is to obtain the minimum cost design for a PC cable-stayed bridge. Finally, this optimization technique is carried out by a developed PC cable-stayed bridge optimization program involving the interaction of the parameterized automatically modeling program, the finite element structural analysis program and the optimization algorithm. A low-pylon PC cable-stayed bridge is selected as the example to test the proposed optimization technique. The optimum result verifies the capability and efficiency of the optimization technique, and the significance to optimize the number of prestressing tendons in the girder. The optimum design scheme obtained by the application can achieve a 24.03% reduction in cost, compared with the initial design.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.96-107
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• 1993

In parameter design Taguchi analyzed a statistic which he called signal-to-noise(SN) ratio by using the experimental design technique. However he gave no justification for using SN ratios in the optimization procedure of parameter design. In this paper we discuss the validity of such SN ratios as proper statistics to be analyzed in parameter design. Moreover, using the real empirical data we examine the appropriateness of SN ratios, and we explain how transformation technique can be applied in parameter design as an alternative method of analysis.

• IE interfaces
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• v.7 no.1
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• pp.75-80
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• 1994

In a recent article, Leon et al. lucidly explained the ideas of the Taguchi two-stage procedure for parameter design optimization, and proposed alternative performance measures called PerMIA to the signal-to-noise ratios. On the other hand, Box proposed an empirical approach to the problem based upon monotone transformations of the performance characteristic(y). This paper develops procedures for parameter design optimization under the assumptions that the expected loss(not necessarily a mean squared error loss) is increasing with respect to the variance of the error in y, and that the mean of y satisfies certain conditions of adjustability. It turns out that the variance of the error in y can play the role of PerMIA, and it is further shown that the derived PerMIA can be adapted to the Box empirical procedure for the minimization of the expected loss in the original metric.

• Journal of Integrative Natural Science
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• v.11 no.2
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• pp.76-81
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• 2018

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for robust design using desirability function without resorting to SN.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.211-218
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• 2010

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. Moreover, there are difficulties in practical application, such as complexity and nonlinear relationships among quality characteristics and design (control) factors, and interactions occurred among control factors. Neural networks have a learning capability and model free characteristics. There characteristics support neural networks as a competitive tool in processing multivariable input-output implementation. In this paper we propose a substantially simpler optimization procedure for parameter design using neural network without resorting to SN. An example is illustrated to compare the difference between the Taguchi method and neural network method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.243-254
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• 1996

In this study, a model and an analysis method for parameter design is presented a linear relation between the input signal and the ideal value of a performance characteristic. Furthermore, There presented a new performance measure, expected quality loss after adjustment, which is proved to be equivalent to Taguchi's SN ratio approximately. On the basis of this, a two-step optimization procedure is proposed for parameter design considering the signal factor and the quality characteristics with continuous variable. Proposed procedure and Taguchi two-stage procedure are compared.