Projection Property of Classical Fractional Factorial DOE and Taguchi DOE

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Projection Property of Classical Fractional Factorial DOE and Taguchi DOE
Swaminathan Balachandran
Professor of Industrial Engineering
University of Wisconsin – Platteville
Ph.: (608)-342-1718
FAX: Ph.: (608)-342-1566
1 University Plaza, Platteville, WI 53818
balachas@uwplatt.edu
Presenter: Swaminathan Balachandran
Keywords: Taguchi Method, Resolution, Projection Property, Comparison of Classical and Taguchi DOE
Purpose: To enlighten, exemplify, and highlight the significant differences between the classical fractional
design of experiments and the Taguchi Methods of DOE.
Abstract
Many practitioners are attracted to the Taguchi method of designing experiments because these
may be used to investigate N factors using N+1 trials in screening experiments. However, this advantage is
nullified by the fact that the fractional factorial designs in the Taguchi method are not symmetrical fractional
factorial designs. When Taguchi method is used to study N factors in N+1 trials and subsequently some of the
original factors are discarded, a new set of trials will be required. But the classical fractional factorial design
of experiments possesses the projection property and a subset of the original variables may be analyzed using
the original experimental data. This paper uses two examples to illustrate the above difference between the
methods.
The paper will first compare the classical fractional design of experiments (DOE) and the Taguchi
Methods of DOE in detail. Then two example problems or case studies will be presented. Finally, the two
approaches to DOE will be compared in detail using the projection property to show that Taguchi method
must be used if and only if the user has adequate knowledge of the system being analyzed.
This paper provides a new way of looking at the Taguchi method of designing experiments and
brings into focus some limitations of the method. From this paper users and practitioners will be able to realize
how the classical DOE may save quite a bit of effort in the long run, especially when the analyst may change
the focus of the study after the initial trials.
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