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DOE++ Features Summary
Easy-to-Use Platform for
Traditional and Reliability DOE
Intuitive
and Flexible Work Environment
The DOE++ interface is a powerful and flexible work center
that allows you to create multiple designs in a single project,
keeping all related analyses and information together in a single
file. Using the same “Project Explorer” approach that is employed
in ReliaSoft’s Weibull++,
ALTA and
BlockSim software,
DOE++ provides an intuitive, hierarchical (tree) structure
to allow you to view and manage one or many standard folios, plot
sheets, free-form regression analyses, spreadsheets and/or attached
documents per project.
[Click
to Enlarge...]
Support
for Traditional DOE Methodology and Reliability DOE
DOE++ facilitates the design types employed in traditional
DOE and also expands upon the traditional methods to support "Reliability
DOE," which provides the proper treatment for interval and right
censored data. Supported design types include:
- One Factor Design, also
known as One-way ANOVA, is used to determine if a particular factor has an effect on
a specified output or response. This approach allows you to
take a detailed look at the effect of the factor, using up to
255 levels, and helps you determine whether a change in output
is due to a change in the input (level) or due to random error.
- Factorial Designs help
you to determine which factors have a significant effect on
the output or response and allow you to identify interactions
between factors.
- Full Factorial Designs
test all possible combinations of factors at each level.
Such designs yield comprehensive data, but may not be possible
due to constraints on time, money and/or number of samples
available for testing. Available full factorial designs
include:
- Two Level Full
Factorial
-
General Full Factorial
- Fractional Factorial
Designs test a subset of the possible combinations of
factors at the levels in question. This allows screening
of larger numbers of factors and/or levels with less investment
of time, money and/or samples, but results in some level
of aliasing (or confounding), where the effect of a certain
factor or factorial interaction cannot be separated from
another effect. Available fractional factorial designs include:
- Two Level Fractional
Factorial
- Plackett-Burman
-
Taguchi Orthogonal Array
- Response Surface Method
(RSM) Designs allow you to study the quadratic effects of
the factors, making this design type well-suited to predictive
modeling and optimization. Available RSM designs include:
- Central Composite
- Box-Behnken
- Taguchi Robust Designs
aim to minimize the variability of the response in spite of
noise factors by combining an inner array of control factors
with an outer array of noise factors.
- Reliability DOE is
specifically intended to handle life data. Only one response
(typically times-to-failure) is measured, but the designs can
accommodate data sets that include suspensions (right censoring)
and/or uncertainty as to when the units failed (interval and/or
left censoring) in addition to complete data sets in which all
of the units under the test failed and the time-to-failure for
each unit is known. You can use the Weibull, lognormal or exponential
distribution to analyze data. Available reliability designs
include:
- One Factor Reliability
Designs
- Two Level Full Factorial
Reliability Designs
- Two Level Fractional
Factorial Reliability Designs
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