• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.6
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• pp.587-595
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• 2005

The heat and flow characteristics in a parallel-flow heat exchanger were examined numerically to obtain its optimal design variables. A desirability function approach was introduced to optimize its performance with respect to the design parameters over the design domain. By varying the importance of heat transfer and pressure drop which are out put variables, the optimal values of the design parameters are examined. As a result, the us-age of the desirability function is very effective for the optimization of the design variables in a heat exchanger since the changes of optimal values are physically appropriate by varying the importance of each output variable.

• Journal of Korean Society for Quality Management
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• v.43 no.3
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• pp.341-358
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• 2015

Purpose: This paper presents graphic methods and desirability function approach to determine best vibration reducing configuration for Surion helicopter. Many flight tests were executed and nine vibration levels in cockpit, cabin, and engine room were measured in each test and analyzed to find optimal configuration. Methods: Graphic analysis methods such as matrix, scatter, and box plots are used to identify better vibration-reducing flight test conditions. As an integrated measure of the performance of 9 vibration levels desirability function approach is adopted. Results: Three vibration reducing configurations are found to be proper and one configuration is recommended. Conclusion: It is expected to be helpful to adopt graphic and desirability function methods presented in this paper in developing new products or systems like helicopters. For efficient and effective flight testing of helicopters, it will be necessary to have consistently homogeneous environment for flight testing and applying design of experiments techniques and analyzing test data.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.97-106
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• 2002

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. and studied by others. In these studies, only single quality characteristic was considered. 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 the combined-array approach.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.46-57
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• 2006

The desirability function approach to multiple response optimization is a useful technique for the analysis of experiments in which several responses are optimized simultaneously. But the existing methods have some defects, and have to be modified to some extent. This paper proposes a new method to combine the individual desirabilities.

• Journal of Applied Reliability
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• v.1 no.2
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• pp.165-173
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• 2001

The desirability function approach by Derringer and Suich (1980) and the generalized distance approach by Khuri and Conlon (1981) are two major approaches to multiresponse optimization for improvement of quality of a product or process. So far, the desirability function method has been the only tool for multiresponse optimization in the situations where there are the goal regions for the respective responses. For such situations, we propose a multiresponse optimization method based on the generalized distance approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.95-104
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• 2005

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation which may amplify the variance of response. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameter setting, a new desirability function is proposed by considering POE as well as distance-to-target of response and response variance. The proposed method is illustrated using a rubber product case in Ribeiro et al. (2000).

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• 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.

• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.197-203
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• 2008

Many quality engineering practitioners continue to have a considerable interest in implementing the concept of response surface methodology to real situations. Recently, dual response surface approach is extensively studied and recognized as a powerful tool for robust design. However, existing methods do not consider the information provided by customers and design engineers. In this regard, this article proposes a flexible optimization model that incorporates that information via desirability function modeling. The optimization scheme and its modeling flexibility are demonstrated through an illustrative example by comparing the proposed model with existing ones.