R-DOE Design: Difference between revisions

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{{Reference Example|{{Banner DOE Reference Examples}}}}
{{Reference Example|{{Banner DOE Reference Examples}}}}
This example validates the calculation of the reliability DOE design in DOE++.
This example validates the calculation of the reliability DOE design in Weibull++.


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Data is from an example on pages 530 and 544 in the book ''Experiments, Planning, Analysis, and Parameter Design Optimization'' by C.F. Jeff Wu, Michael Hamad, John Wiley & Sons, Inc. 2000.
The data are from an example on pages 530 and 544 in the book ''Experiments, Planning, Analysis, and Parameter Design Optimization'' by C.F. Jeff Wu, Michael Hamad, John Wiley & Sons, Inc. 2000.


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From the book, the MLE and p values for the lognormal regression model:
 
From the book, the MLE and P values for the lognormal regression model are:


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R-DOE Design

This example validates the calculation of the reliability DOE design in Weibull++.

Reference Case

The data are from an example on pages 530 and 544 in the book Experiments, Planning, Analysis, and Parameter Design Optimization by C.F. Jeff Wu, Michael Hamad, John Wiley & Sons, Inc. 2000.

Data

Run Factor Failure Time
A B C D E
1 - - - - - (14, 16) (20, [math]\displaystyle{ \infty\,\! }[/math])
2 - - + + + (18,20) (20, [math]\displaystyle{ \infty\,\! }[/math])
3 - + - - + (8, 10) (10, 12)
4 - + + + - (18, 20) (20, [math]\displaystyle{ \infty\,\! }[/math])
5 + - - + - (20, [math]\displaystyle{ \infty\,\! }[/math]) (20, [math]\displaystyle{ \infty\,\! }[/math])
6 + - + - + (12, 14) (20, [math]\displaystyle{ \infty\,\! }[/math])
7 + + - + + (16, 18) (20, [math]\displaystyle{ \infty\,\! }[/math])
8 + + + - - (12, 14) (14, 16)


Result

From the book, the MLE and P values for the lognormal regression model are:

Effect MLE P value
Intercept 2.939 0.00
A 0.117 0.06
B -0.201 0.00
AB -0.049 0.43
C -0.051 0.41
D 0.273 0.00
E -0.153 0.02


Results in DOE++

The software results match the book results. The MLE information table is:

Reliability doe mle info.png
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