Background
Plackett-Burman design is one
of the so-called "screening designs." Such designs are traditionally
used for identifying important factors from among many potential
factors. In the analysis of these designs, usually only main effects
are estimated.
Consider a life testing of
weld-repaired castings.* The objective
of the test is to identify the important factors that affect the
life and to improve the product life. There are seven factors
that may affect the life. A two level full factorial design will
require 27 = 128
runs. It will be time-consuming and costly. Therefore, an eight
run Plackett-Burman experiment will be conducted.
For this example, the seven
factors are:
The response is the failure
time of each sample. The logarithmic transformation of the
failure time is used in the analysis.
Experiment
Design
The experimenters use DOE++ to design a
Plackett-Burman design. The design-specific settings, the
factor properties and the
response properties used are shown next.
The design matrix and the response data are given
in the "Cast Fatigue Experiment" Folio. Analysis
Part I
Step 1: After performing
the experiment according to the design and recording the
results, the experimenters enter the data set into the Standard Folio, as shown next.
[Click
to Enlarge]
Step 2: The data set is
analyzed with the default risk (significance) level of 0.1,
using individual terms.
Step 3: An Effect
Probability plot is
created, as shown next.
The Effect Probability plot shows
that effect F is significant.
Conclusions
The effects of the factors are given below:
As shown in the Regression
Information table on the Analysis tab, assuming that there is no
interaction, a higher product life can be achieved by setting A,
B, C, D and E at their respective low levels and F and G at
their respective high levels. Otherwise, further experiments can
be conducted to study the interaction effects of those factors.
Factor F was found to be the
most important factor.
* Wu,
Jeff and Hamada, Michael, Experiments:
Planning, Analysis, And Parameter Design Optimization,
John Wiley & Sons, New York, 2000. | 
