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설계기반 품질고도화에서 디자인 스페이스 구축을 위한 효율적인 실험계획

Efficient Designs to Develop a Design Space in Quality by Design

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초록

Purpose: We research on the efficient response surface methodology(RSM) design to develop a design space in Quality by Design(QbD). We propose practical designs for the successful construction of the design space in QbD by allowing different number of replicates at the box points, star points, and the center point in the rotatable central composite design(CCD). Methods: The fraction of design space(FDS) plot is used to compare designs efficiency. The FDS plot shows the fraction of the design space over which the relative standard error of predicted mean response lies below a given value. We search for practical designs whose minimal half-width of the tolerance interval per a standard deviation is less than 4.5 at 0.8 fraction of the design space. Results: The practical designs for the number of factors between two and five are listed. One of the designs in the list could be chosen depending on the experimental budget restriction. Conclusion: The designs with box points replications are more efficient than those with the star points replication. The sequential method to establish a design space is illustrated with the simulated data based on the two examples in RSM.

Abstract

Purpose: We research on the efficient response surface methodology(RSM) design to develop a design space in Quality by Design(QbD). We propose practical designs for the successful construction of the design space in QbD by allowing different number of replicates at the box points, star points, and the center point in the rotatable central composite design(CCD). Methods: The fraction of design space(FDS) plot is used to compare designs efficiency. The FDS plot shows the fraction of the design space over which the relative standard error of predicted mean response lies below a given value. We search for practical designs whose minimal half-width of the tolerance interval per a standard deviation is less than 4.5 at 0.8 fraction of the design space. Results: The practical designs for the number of factors between two and five are listed. One of the designs in the list could be chosen depending on the experimental budget restriction. Conclusion: The designs with box points replications are more efficient than those with the star points replication. The sequential method to establish a design space is illustrated with the simulated data based on the two examples in RSM.

참고문헌 (10)

  1. DeGryze, "Using the Correct Intervals for Prediction: a Tutorial on Tolerance Intervals of Ordinary Least-squares Regression", Chemometrics and Intelligent Laboratory Systems, 87(2), pp.147~154 (2007)
  2. Elibol, Murat, "Response Surface Methodological Approach for Inclusion of Perfluorocarbon in Actinorhodin Fermentation Medium", Process Biochemistry, 38(5), pp.667~673 (2002)
  3. Giovannitti-Jensen, A., "Graphical Assessment of the Prediction Capability of Response Surface Designs", Technometrics, 31(2), pp.159~171 (1989)
  4. Juran JM, "Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services" (1992), Simon & Schuster
  5. Myers, R. H., "Response Surface Methodology: Process and Product Optimization Using Designed Experiments" (2016), John Wiley & Sons
  6. "VdgRsm: Variance Dispersion and Fraction of Design Space Plots. R package version 1.5" (2015)[URL]
  7. Guidance for industry: Q8 (R2) pharmaceutical development, "US Food and Drug Administration", Center for Drug Evaluation and Research, ~ (2009)
  8. "AlgDesign : Algorithmic Experimental Design. R package version 1.1-7.3" (2014)[URL]
  9. Whitcomb, P. J., "FDS-A Power Tool for Designers of Optimization Experiments" (2008), Stateaser Newsletter from Stat-Ease,. Inc
  10. Zahran, A., "Fraction of Design Space to Assess Prediction Capability of Response Surface Designs", Journal of Quality Technology, 35(4), pp.377~386 (2003)

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