ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

Comparing different transformation strategies on a six-factor full factorial (2^6) industrial experiment.

22 September 2008, 16:00 – 16:20


Submitted by
Erik Monness
Erik Mønness
Hedmark University College
Different transformation strategies are compared on a data set from an experiment to determine the optimum operating conditions for a milling machine with respect to surface finish. The data was obtained from a six-factor full factorial (2^6) Designed Experiment. The six factors Tool speed, Work piece speed, Depth of cut, Coolant, Direction of cut and Number of cuts were varied each with two levels. Each treatment was repeated 8 times so the mean and standard deviation of each treatment were available. (A talk on comparing fractions from this experiment was given at ENBIS-2005, and published in Applied Stochastic Models in Business and Industry, 2007. 23: p. 117-128: Comparing different fractions of a factorial design: A metal cutting case study. Mønness, Linsley, and Garzon,.).

An empirical power transformation is determined graphically from the σ≈μ^(λ-1)relation. Another strategy is derived from the Box-Cox transformation.

Issues to be explored:
• What λ is best based on the saturated model?
• Is the estimate stable considering different fractions of the data?
Models involving the experimental factors:
• Applying different models, e.g. involving only main effects, 2-interactions, 3-interactions, how stable is the λ estimate?
• How does the transformation affect the set of significant factors?
Since one motivation for doing a transformation is to simplify any model, this issue is of interest when having many experimental factors.
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