• Journal of Korean Society for Quality Management
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• v.26 no.2
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• pp.17-26
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• 1998

Taguchi's parameter design is a method for quality improvement by making the performance fo a system robust to noise. Parameter design with dynamic characteristics has been recently the subject of much interest. This paper is concerned with a review and a generalization of the Signal-to-Noise (SN) ratio, a quality measure for parameter design with dynamic characteristics, proposed by Taguchi. We present a method for determination and analysis of the generalized SN ratio and illustrate its implementation by example.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.41-46
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• 2019

This paper is analysis on usefulness of Microstrip patch antenna design using Taguchi's Orthogonal array. For comparison, the shape of U-slot and Feed line elements are decided as a parameter in Microstrip patch antenna. And all the possible result of 19,683 times simulation using parameter sweep and the result of Taguchi's orthogonal arrays are compared. The simulation of Parameter sweep and Orthogonal arrays has 3.7% error on 10dB Bandwidth. Finally, It is identified that 19,683 times parameter sweep simulation can be reduced to 27 times with Taguchi's orthogonal arrays but still it represents antenna parameter characters well.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.47-63
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• 1991

Parameter design is the core of the Taguchi method, and its purpose is to design product or processes such that the performance of product becomes robust to noise. Taguchi recommends to use the so called orthogonal arrays for parameter design. The purpose of this paper is to clarify the principles involved in constructing the Taguchi orthogonal arrays. Understanding such principles may be useful for finding better ways of utilizing the Taguchi orthogonal arrays as well as for developing better experimental designs.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.109-118
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• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

• 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.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.126-142
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• 1999

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. Taguchi parameter design has a great deal of advantages but it also has some disadvantages. The various research efforts aimed at developing alternative methods. In the Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et. al. ( 1990) and studied by others. In these studies, only single quality characteristic was considered. In this paper we propose how to simultaneously optimize multiple quality characteristics using desirability function when we used the combined-array approach to assign control and noise factors. An example is illustrated to show the difference between the Taguchi's product-array approach and the combined-array approach.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1263-1272
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• 2016

Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. However, a disadvantage of this method is that it requires an excessively large number of experiments. So combined array approach have been proposed to reduce the number of experiments. Taguchi has used the signal-to-noise ratio to find the optimum conditions in the Taguchi parameter design. In analyzing the data from the parameter design various problems tends occur by using an SN. In this paper, we propose an alternative solution for reducing the number of experiments without depending on the signal-to-noise ratio to overcome the shortcomings of the parameter design. Two examples illustrate this procedure in the two different experimental design (product array, combined array) approaches.

• Proceedings of the KIEE Conference
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• 2008.10a
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• pp.107-108
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• 2008

This article describes optimization for PGS(Patterned Ground Shield) of rectangular spiral inductor using Taguchi's Design of Experiment. PGS is decrease method of parasite component by silicon substrate among dielectric loss reduction method. Using taguchi's design of experiment, each parameter is fixed upon that PGS high poison(A), slot spacing(B), strip width(C) and overlap turn number(D) of PGS design parameter. Then we verified that percentage contribution and design sensitivity analysis of each parameter and level by signal to noise ratio of larger-the-better type. We consider percentage contribution and design sensitivity of each parameter and level, and then verify that model of optimization for PGS is lower inductance decreasing ratio and higher Q-factor increasing ratio by EM simulation.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.251-262
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• 2004

There is boner process of stone manufacturing to become quality down of stone to consolidated micro crack appearance of stone surface and biotite by fire that is to be route process in stone surface by flame of LPG. And then, it is develop that stone surface process equipment by automation for the work method of boner process can be substitute work method by shotball blasting. To developement of equipment, There is to be down noise and dust. Acording to remove calamity growth hazardous substance in the work environment, there is to solve workplace avoidance factor. We have taken Taguchi's parameter design approch, specifically orthogonal array, and determined the optimal levels of stone surface through analysis of the experimental results using SIN ratio.

• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.167-176
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• 2000

A study to analyze and solve problems of plastic injection molding experiment has presented in this paper. We have taken Taguchi's parameter design approach, specifically orthogonal array, and determined the optimal levels of the selected variables through analysis of the experimental results using S/N ratio.