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http://dx.doi.org/10.7469/JKSQM.2020.48.2.269

Efficient Designs to Develop a Design Space in Mixture Response Surface Analysis  

Chung, Jong Hee (Department of Statistics, Ewha Womans University)
Lim, Yong B. (Department of Statistics, Ewha Womans University)
Publication Information
Abstract
Purpose: The practical design for experiments with mixtures of q components is consisted in the four types of design points, vertex, center of edge, axial, and center points in a (q-1)-dimensional simplex space. We propose a sequential method for the successful construction of the design space in Quality by Design (QbD) by allowing the different number of replicates at the four types of design points in the practical design when the quadratic canonical polynomial model is assumed. Methods: To compare the mixture designs efficiency, fraction of design space (FDS) plot is used. We search for the practical mixture designs whose the minimal half-width of the tolerance interval per a standard deviation, which is denoted as d2, is less than 4.5 at 0.8 fraction of the design space. They are found by adding the different number of replicates at the four types of the design points in the practical design. Results: The practical efficient mixture designs for the number of components between three and five are listed. The sequential method to establish a design space is illustrated with the two examples based on the simulated data. Conclusion: The designs with the center of edge points replications are more efficient than those with the vertex points replication. We propose the sample size of at least 23 for three components, 28 for four components, and 33 for the five components based on the list of efficient mixture designs.
Keywords
Quality by Design(QbD); Design Space; Tolerance Interval; Practical Efficient Mixture Designs;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Chung, J. H., Kim, J., Lim, Y. B. 2019. Efficient Designs to Develop a Design Space in Quality by Design. Journal of the Korean Society for Quality Management 47(3):523-535.   DOI
2 Juran JM. 1992. Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services. New York: Simon & Schuster.
3 Koons, G. F., Wilt ,M. H. 1985, Design and Analysis of an ABS Pipe Compound Experiment. Experiments in Industry: Design, Analysis, and Interpretation of Results, p 111-117.
4 Stat-Ease, Inc. Design-Expert (R) Software Version 12. 2019. Tutorials. https://www.statease.com/docs/v12/tutorials/
5 US Food and Drug Administration. 2009. Guidance for Industry: Q8 (R2) Pharmaceutical Development. Center for Drug Evaluation and Research.
6 Whitcomb, P.J. 2008. FDS-A Power Tool for Designers of Optimization Experiments. Stateaser Newsletter from Stat-Ease,. Inc.
7 Zahran, A., Anderson-Cook, C. M., Myers, R. H. 2003. Fraction of Design Space to Assess Prediction Capability of Response Surface Designs. Journal of Quality Technology 35(4):377-386.   DOI