Ink Formulations Through Statistically Designed Mixture Experiments

  • J. Auslander
  • W. Hunt
  • S. Wenner
Chapter

Abstract

We demonstrate the usefulness of statistically designed mixture experiments in the development of special inks for use in postage meters. These inks must satisfy many difficult and conflicting requirements, so careful experimentation and analysis is essential. A statistical approach was used to design an efficient series of experimental formulations; the characteristics of these formulations were measured and the results then statistically analyzed and interpreted with the aid of contour plots. With the aid of these plots we were able to devise formulations with improved characteristics.

Keywords

Design Space Evaporation Rate Approximate Theory Component Fraction Mixture Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Patton T. C., (1978), Paint Flow and Pigment Dispersion, New York: John Wiley.Google Scholar
  2. 2.
    Barton. A. F., (1983), Handbook of Solubility Parameters and Other Cohesion Parameters, CRC Press, Inc. Boca Raton, Florida.Google Scholar
  3. 3.
    Walsham G. H., Granville D. E., “A Model of Evaporation from Solvent Blends”, Journal of Paint Technology, 43, No. 554, March 1971.Google Scholar
  4. 4.
    Bulletin PC-69-4, “Predicting the Evaporation Properties of Mixed Solvent Blends”, Shell Chemical Company Bulletin, Petrochemical Division, 1212 Avenue of the Americas, New York 10036.Google Scholar
  5. 5.
    Rocklin A. L and G. D. Edwards, “Predicting Viscosities of Organic and of Aqueous Solvent Blends,” Journal of Coating Technology, 4, No. 620, 68–74, September 1976.Google Scholar
  6. 6.
    Deming S. N. “Quality by Design”, CHEMTEC, February 1990, p. 118.Google Scholar
  7. 7.
    Tkaczuk P. “Formulating Solvent PSA’s Using Designed Experiments”, Adhesive Age, August 1988.Google Scholar
  8. 8.
    Nagarshet P. S. “Optimization of Processing Parameters for Injection Molding Using Flow Simulation Analysis and Statistical Techniques, ANTEC, 89, p. 1699.Google Scholar
  9. 9.
    Cornell, J. A. (1990), Experiments With Mixtures: Designs, Models and the Analysis of Mixture Data, New York: John Wiley.Google Scholar
  10. 10.
    Coenegracht, P. M. J., Smilde, A. K., Metting, H. J. and Doombos, D. A. (1989), “Comparison of Optimization Methods in Reversed-Phase High-Performance Liquid Chromatography Using Mixture Designs and Multi-Criteria Decision Making”, Journal of Chromatography, 485, pp. 195–217.CrossRefGoogle Scholar
  11. 11.
    Ford, I. and Silvey, S.D. (1980). “A sequentially constructed design for estimating a non-linear parametric function.” Biometrika, 67.Google Scholar
  12. 12.
    Box, G. E. P., Hunter, W. G., and Hunter, J. S. (1978), Statistics for Experimenters, New York: John Wiley.Google Scholar
  13. 13.
    Scheffe, H. (1958), “Experiments with Mixtures,” Journal of the Royal Statistical Society, Ser. B, 20, pp.344–360.Google Scholar
  14. 14.
    McLean, R. A., and Anderson, V. L. (1966), “Extreme Vertices Design of Mixture Experiments,” Technometrics, 8, pp. 447–456 (with discussion).CrossRefGoogle Scholar
  15. 15.
    Silvey, S. D. (1980), Optimal Design, New York: Chapman &anp; Hall.CrossRefGoogle Scholar
  16. 16.
    Kiefer, J., and Wolfowitz. (1959), “Optimum Designs in Regression Problems,” Annuals of Mathematical Statistics, 30, pp. 271–294.CrossRefGoogle Scholar
  17. 17.
    Kiefer, J., and Wolfowitz. (1960), “The Equivalence of Two Extremum Problems,” Canadian Journal of Mathematics, 12, pp. 363–366.CrossRefGoogle Scholar
  18. 18.
    Wynn, H. P. (1970), “The Sequential Generation of D-Optimum Experimental Designs,” Annals of Mathematical Statistics, 41, pp. 1655–1664.CrossRefGoogle Scholar
  19. 19.
    Wheeler, R. E. (1974), “Portable Power,” Technometrics, 16, pp. 193–201.CrossRefGoogle Scholar
  20. 20.
    Mitchell, T. J. (1974), “An Algorithm for the Construction of D-Optimal Experimental Designs,” Technometrics, 16, pp. 203–210.Google Scholar
  21. 21.
    Cox, D. R., (1971), “A Note on Polynomial Response Functions for Mixtures”. Biometrika, 58, 1, p. 155.CrossRefGoogle Scholar
  22. 22.
    ECHIP (1990). Hockessin, Delaware: ECHIP Inc. (SOFTWARE).Google Scholar
  23. 23.
    XSTAT (1984). New York: John Wiley (SOFTWARE).Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • J. Auslander
    • 1
  • W. Hunt
    • 1
  • S. Wenner
    • 1
  1. 1.Pitney Bowes Technical Systems and Advanced ProductsSheltonUSA

Personalised recommendations