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Lookup NU author(s): Erik Monness, Matt Linsley
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Full factorial designs of a significant size are very rarely performed in industry due to the number of trials involved and unavailable time and resources. The data in this paper were obtained from a six-factor full factorial (26) designed experiment that was conducted to determine the optimum operating conditions for a steel milling operation. Fractional-factorial designs 2III6-3 (one-eighth) and 2IV6-2 (one-fourth, using a fold-over from the one-eighth) are compared with the full 26 design. Four of the 2III6-3 are de-aliased by adding four more runs. In addition, two 12-run Plackett-Burman experiments and their combination into a fold-over 24-run experiment are considered. Many of the one-eighth fractional-factorial designs reveal some significant effects, but the size of the estimates varies much due to aliasing. Adding four more runs improves the estimation considerably. The one-quarter fraction designs yield satisfactory results, compared to the full factorial, if the 'correct' parameterization is assumed. The Plackett-Burman experiments, estimating all main effects, always perform worse than the equivalent regular designs (which have fewer runs). When considering a reduced model many of the different designs are more or less identical. The paper provides empirical evidence for managers and engineers that the choice of an experimental design is very important and highlights how designs of a minimal size may not always result in productive findings. Copyright © 2006 John Wiley & Sons, Ltd.
Author(s): Monness E, Linsley MJ, Garzon IE
Publication type: Article
Publication status: Published
Journal: Applied Stochastic Models in Business and Industry
Year: 2007
Volume: 23
Issue: 2
Pages: 117-128
ISSN (print): 1524-1904
ISSN (electronic): 1526-4025
Publisher: John Wiley & Sons Ltd.
URL: http://dx.doi.org/10.1002/asmb.641
DOI: 10.1002/asmb.641
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