• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.57-64
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• 2001

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 Vining and Myers (1990) and others. In these studies, only single respouse variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.1-6
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• 2005

In this paper we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• 통합자연과학논문집
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• 제14권3호
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• pp.73-77
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• 2021

Robust design is an approach to reduce the performance variation of mutiple responses in products and processes. In fact, in many experimental designs require the simultaneous optimization of multiple responses. In this paper, we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.685-696
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• 2001

Robust design is an approach to reducing performance variation of response values in products and processes. In the Taguchl 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 response variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses, and when we use 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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• 제16권2호
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• pp.255-261
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• 2005

Many designed experiments require the simultaneous optimization of multiple responses. In this paper, we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• 대한산업공학회지
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• 제43권2호
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• pp.127-134
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• 2017

For product or process design and development, it is common to optimize multiple responses (characteristics) based on experimental data. To determine optimal conditions, we need to design the experiment, estimate a proper model for each response, and optimize the multiple responses simultaneously. There are several techniques and many research results on optimizing multiple responses simultaneously, when the experimental data are available. However, the experimental design issue for optimizing multiple responses has not been discussed yet. This paper proposes some idea on how to plan screening design when requirements for multiple performance characteristics are to be met in developing new products. A screening design procedure is developed for securing the requirements of multiple responses. Initial design factors are classified into three categories; specific, non-conflicting common, and conflicting common. After screening experiments, follow-up design region search method is suggested with respect to the most unsatisfied or important response, or overall desirability. A case study on a synthesis of melamine formaldehyde resin is presented to illustrate the procedure and to show the validity of the approach.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.103-117
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• 1996

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 Vining and Myers(1990), Box and Jones (1992) and others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses, and when we use the combined-array approach to assign control and noise factors.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.325-335
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• 2003

Robust design is to identify appropriate settings of control factors that make the system's performance robust to to changes in the noise factors that represent the source of variation. 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 response variable was considered. We propose how to simultaneously optimize multiple responses when we use the combined array approach.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 춘계학술대회
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• pp.65-75
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• 2003

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 response variable was considered. We propose how to simultaneously optimize multiple responses when we use the combined array approach.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2001년도 추계학술대회
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• pp.44-55
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• 2001

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 response variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses, and when we use the combined-array approach to assign control and noise factors.