Example 4 - Plackett-Burman Design
[Download DOE++ Example File (*.rdoe)]
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.
The data set as entered in the DOE++ Folio.
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.
