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Design of Experiments (DOE)
What is it?
DOE is a systematic approach to investigation of a system or process. A series of
structured tests are designed in which planned changes are made to the input variables of
a process or system. The effects of these changes on a pre-defined output are then
assessed. Why is it
important?
DOE is important as a formal way of maximizing information gained while minimizing
resources required. It has more to offer than 'one change at a time' experimental methods,
because it allows a judgement on the significance to the output of input variables acting
alone, as well input variables acting in combination with one another.
'One change at a time' testing always carries the risk that the experimenter may find one
input variable to have a significant effect on the response (output) while failing to
discover that changing another variable may alter the effect of the first (i.e. some kind
of dependency or interaction). This is because the temptation is to stop the test when
this first significant effect has been found. In order to reveal an interaction or
dependency, 'one change at a time' testing relies on the experimenter carrying the tests
in the appropriate direction. However, DOE plans for all possible dependencies in the
first place, and then prescribes exactly what data are needed to assess them i.e. whether
input variables change the response on their own, when combined, or not at all. In terms
of resource the exact length and size of the experiment are set by the design (i.e. before
testing begins).
When to use it?
DOE can be used to find answers in situations such as "what is the main contributing
factor to a problem?", "how well does the system/process perform in the presence
of noise?", "what is the best configuration of factor values to minimize
variation in a response?" etc. In general, these questions are given labels as
particular types of study. In the examples given above, these are problem solving,
parameter design and robustness study. In each case, DOE is used to find the answer, the
only thing that marks them different is which factors would be used in the experiment.
How to use it?
The order of tasks to using this tool starts with identifying the input variables and the
response (output) that is to be measured. For each input variable, a number of levels are
defined that represent the range for which the effect of that variable is desired to be
known. An experimental plan is produced which tells the experimenter where to set each
test parameter for each run of the test. The response is then measured for each run. The
method of analysis is to look for differences between response (output) readings for
different groups of the input changes. These differences are then attributed to the input
variables acting alone (called a single effect) or in combination with another input
variable (called an interaction).
DOE is team oriented and a variety backgrounds (e.g. design, manufacturing, statistics
etc.) should be involved when identifying factors and levels and developing the matrix as
this is the most skilled part. Moreover, as this tool is used to answer specific
questions, the team should have a clear understanding of the difference between control
and noise factors.
In order to draw the maximum amount
of information a full matrix is needed which contains all possible combinations of factors
and levels. If this requires too many experimental runs to be practical, fractions of the
matrix can be taken dependent on which effects are of particular interest. The fewer the
runs in the experiment the less information is available.
Available on the web:
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