• Proceedings of the Korean Society for Quality Management Conference
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• 2009.10a
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• pp.15-22
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• 2009

Quality variations in mixture products such as medicine, food, engineering chemicals, and alloy materials can be caused by their own sub-components. For instance, discharging characteristics of a lithium-ion rechargeable battery depend upon the mixture ratio of ethylene, dimethyle, and ethyle-methyle, all of which consists an electrolyte solution in the battery. Thus it is important to determine tolerances of mixture components in maintaining the product quality at a desired level. This paper proposes a simple but efficient approach to a mixture tolerancing method with multi-response variables. We use a response surface method for empirical modelling between mixture components. An illustrative example of the proposed method is given.

• Journal of Korean Society for Quality Management
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• v.38 no.4
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• pp.580-586
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• 2010

Performance variations in mixture products such as medicine, food, and chemicals can be caused by their own subcomponents. For instance, a discharge capacity of a lithium-ion battery depends upon the mixture ratio of ethylene, dimethyle, and ethyle-methyle, all of which are subcomponents of an electrolyte solution in the battery. Thus it is crucial to determine tolerances of the mixture ratio in order to maintain the product quality at a desired level. This paper is concerned with the tolerance design of the mixture ratio. In particular, minimizing variance around the mixture ratio is adopted as a decision criterion in this paper. An illustrative example with multiple quality characteristics is given as well.

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.413-424
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• 2010

Mixture experiments involve combining ingredients or components of a mixture and the response is a function of the proportions of ingredients which is independent of the total amount of a mixture. The purpose of the mixture experiments is to find the optimum blending at which responses such as the flavor and acceptability are maximized. We assume the quadratic or special cubic canonical polynomial model over the experimental region for a mixture since the current mixture is assumed to be located in the neighborhood of the optimal mixture. The cost of the mixture is proportional to the cost of the ingredients of the mixture and is the linear function of the proportions of the ingredients. In this paper, we propose mixture response surface methods to develop a mixture such that the cost is down more than ten percent as well as mean responses are as good as those from the current mixture. The proposed methods are illustrated with the well known the flare experimental data described by McLean and Anderson(1966).

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.11
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• pp.1605-1614
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• 2002

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality. In order to implement the purpose of this study, Jeans market had been segmented by the mixture regression model, and price response function was calibrated for each segment. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables. Data were co]looted from the sample of the 23o undergraduate and graduate students in Seoul. For the data analysis, mixture regression model, conjoint analysis, and t-test were used. As a result, jeans market was divided into 5 segments. Segment 1,2,3 were allocated into the group not using the level of price as the quality indicator while segment 4,5 were done into the other group. Significant differences existed between two groups in product attributes, not in demographic variables. Mixture model and conjoint analysis were proved to be an effective set of tools in market segmentation.

• Journal of Korean Society for Quality Management
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• v.13 no.2
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• pp.66-72
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• 1985

The objective of mixture experiments with process variables is to find experimental blends and conditions that produce the product of highest quality. In this paper, designs for mixture experiments with process variables are presented, where the emphasis is on using only a fraction of the total number of possible design points and the fitting of reduced models for measuring the effects of the mixture components and process variables.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.334-344
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• 2003

A mixture experiment is a special type of response surface experiment in which factors are the ingredients or components of a mixture, and the response is a function of the proportions of each ingredient. Evolutionary operation is useful to improve on-line full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents an evolutionary operation procedure with mixture variables for large-scale mixture production process which can be beneficial to practitioners who should improve on-line mixture quality while maintaining the production amount of the mixture product.

• Food Science of Animal Resources
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• v.39 no.2
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• pp.345-353
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• 2019

Various commercial collagen mixtures aimed at improving the quality of meat products are available, but the optimal composition is unclear. This study aimed to compare the functional properties, including physicochemical characteristics and lipid oxidative stability, of loin ham marinated with three commercial collagen mixtures sold as food additives. The addition of collagen mixtures led to significant increases in the moisture content, water holding capacity (WHC), cooking yield, and instrumental tenderness, regardless of the type of collagen mixture. In particular, meat samples containing collagen mixture C showed the highest (p<0.05) WHC and tenderness among all groups. Furthermore, collagen mixture B induced increases (p<0.05) in pH values in both raw and cooked samples. The $a^*$ values of samples with collagen mixtures were lower (p<0.05) than those of samples without collagen mixtures. All collagen mixtures effectively improved oxidative stability during 7 days of storage at $4^{\circ}C$. The samples containing collagen mixture B had the lowest lipid oxidation (p<0.05) among groups. These results indicated that collagen mixture C could be used in injection brine to enhance the quality characteristics of meat products, particularly the WHC and tenderness. Collagen mixture A could be used for meat products with high fat contents based on its ability to improve lipid oxidative stability during long-term storage.

• Journal of Korean Society for Quality Management
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• v.23 no.2
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• pp.68-79
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• 1995

Good estimation of the slopes of the mixture response function may be important as well as estimation of mean mixture response. It is possible to evaluate and compare several mixture designs with respect to the slope. A graphical method is proposed that allows us to evaluate a given design's support for the fitted model in terms of slope variance. We can plot variances of slopes along Cox direction according to existence of restriction of simplex region when comparing several different mixture designs.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.83-87
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• 2002

Many industrial products such as paints, ink and adhesives are composed of the ingredients of a mixture. In mixture experiments, the characteristics of quality(response) depends only on the proportions of the ingredients and does not depend on the total amount of the mixture. This article discusses the constrained mixture experimental design, the data analysis, and the optimum formulation of ingredients based on the two quality characteristics - taste and flavor. It IS shown that efficient designs can be constructed from D-optimal criterion. Special cubic models were selected as the final mixture response surfaces for both reponses. The desirability function was used for the optimization of the two responses.

• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.209-219
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• 2014

Purpose: The purpose of this study is to develop the statistical test for process capability index $C_p$ based on mixture normal process. Methods: This study uses Bootstrap method to calculate the approximate P-value for various simulation conditions under mixture normal process. Results: This study indicates that our proposed method is effective way to test for process capability index $C_p$ based on mixture normal process. Conclusion: This study finds out that statistical test for process capability index $C_p$ based on mixture normal process is useful for real application.