Use DOE planning to set up and plan experiments. Designed experiments
(DOEs) provide a cost-effective methodology for simultaneously testing multiple
factors. A DOE consists of a number of experimental runs, or trials. In each
trial, the factors to be tested are set at predetermined levels. The type and
size of the experiment are determined by the goals of the experimenter and the
nature of the factors. Typical goals might include eliminating unimportant
factors, developing a full predictive model (Y = f(X)), describing how the
process inputs jointly affect the process output, and determining the optimal
settings of the inputs.
Use the DOE planning worksheet to set up and plan experiments using two
basic types of DOEs.
- General full-factorial
- Factors can have any
number of levels
- Always a full-factorial
- Two-level (also called 2K)
- Every factor has exactly
- Either a full-factorial or
Answers the questions:
- What factors will be used in
the DOE and at what levels?
- Which type of DOE will be used
to test the process inputs (factors)?
- What are the goals of the
- What is the timeline for the
- Did you have any special
considerations when conducting the experiment?
|When to Use
||Use the DOE planning worksheet before setting up and running any
DOE. The planning worksheet is a record of the goals for the experiment and the
conditions under which the experiment is to be run. This document is an
effective communication tool for explaining the rationale for the experiment to
- The first question that must
be answered is which type of DOE to use:
- When setting the levels
of the factors, you should limit the number of levels for each factor to two.
DOEs with two levels for each factor are far more efficient (fewer experimental
runs) than those in which some factors have more than two levels.
- 2K factorial DOEs
provide a very easy-to-use prediction equation, while General Full Factorial
(GFF) DOEs do not. If obtaining a prediction equation is one of the important
goals of the experiment, the 2K Factorial DOEs are highly recommended.
- If any factor must be
evaluated at more than two levels, use the GFF.
- If you are using a 2K
factorial DOE, consider using high-resolution (resolution V or higher)
fractional DOEs instead of full-factorial designs. If you use a fractional DOE
you can either reduce the size of the experiment or replicate the design with
the same number of runs you would have had in the full-factorial design. If
3-way interactions are deemed unlikely and unimportant, the 2K fractional
factorial design with a minimum resolution V is the preferred design because it
will reduce the sample size while still providing the needed level of analysis.
- For three factors, all
at two levels, the 2K full factorial in eight runs is generally recommended.
- For four factors, all at
two levels, the 2K full factorial in 16 runs is generally recommended, unless
the runs are extremely expensive
and it is not important to estimate all 2-factor interactions.
- For five or more
factors, all at two levels, 2K fractional factorial designs with resolution V
or higher are generally recommended.
- Use the design-selection
tables in Minitab to help you select an appropriate DOE (for example, the
number of runs, given the number of factors you want to include).
- Whenever possible, the runs
in the experiment should be done in random order to prevent confusing a factor
effect with the effect of an untested factor (sometimes called a lurking
- Replicating the DOE
increases your ability to detect smaller factor effects. If you are not sure
how many replicates you need, use the power and sample size calculations in
Minitab to determine the number. To use this command, you will need to know (at
least approximately) the standard deviation of the output, the size of the
effect you want to detect, how much risk you are willing to assume at both
missing an effect of interest and wrongly determining an effect is
- The 2K factorial DOE (both
full-factorial and fractional-factorial) relies on the assumption that the
effects of the factors on the response are reasonably linear (can be modeled
adequately with a straight line) in the inference space. You should include
center points in your 2K factorial DOE whenever you doubt the linearity of the
effects. The center points produce a test for curvature, that is, they test the
assumption of linearity. If the curvature is statistically significant, you
must still decide whether the amount of curvature present is of concern from a
- When adding center points to
the DOE, the following procedures are often recommended:
- Use the current process
settings of the factors as the center point, which gives the operators running
the experiment a comfort level with familiar settings of the factors.
- Do not fully randomize
the center points in the DOE. Instead, put one or two center points at the
start of the experiment, one or two in the middle, and one or two at the end,
which provides a check for trends during the running of the experiment.
- State the outputs that will
be measured during the experiment.
- State your factors and their
levels of interest.
- Verify the measurement
systems for the Y data and the inputs (factors) are adequate.
- Develop a data collection
strategy. For example, determine who should collect the data, where and when
the data should be collected, how many data values are needed, the preciseness
of the data, how to record the data, and so on.
- Select the type of DOE to be
used and the number of times the experiment will be replicated.
- Answer the checklist
questions at the end of the worksheet.
- Obtain approval to proceed
with the experiment.
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