pg_0010
5.
Process Improvement
5.1.
Introduction
5.1.1.
What is experimental design.
Experimental
Design (or
DOE)
economically
maximizes
information
In an experiment, we deliberately change one or more process variables (or
factors) in order to observe the effect the changes have on one or more response
variables. The (statistical) design of experiments (DOE) is an efficient procedure
for planning experiments so that the data obtained can be analyzed to yield valid
and objective conclusions.
DOE begins with determining the
objectives
of an experiment and selecting the
process factors
for the study. An Experimental Design is the laying out of a
detailed experimental plan in advance of doing the experiment. Well chosen
experimental designs maximize the amount of "information" that can be obtained
for a given amount of experimental effort.
The statistical theory underlying DOE generally begins with the concept of
process models.
Process Models for DOE
Black box
process
model
It is common to begin with a process
model
of the `black box' type, with several
discrete or continuous input
factors
that can be controlled--that is, varied at will
by the experimenter--and one or more measured output
responses
. The output
responses are assumed continuous. Experimental data are used to derive an
empirical (approximation) model linking the outputs and inputs. These empirical
models generally contain
first and second-order terms
.
Often the experiment has to account for a number of uncontrolled factors that
may be discrete, such as different machines or operators, and/or continuous such
as ambient temperature or humidity. Figure 1.1 illustrates this situation.
5.1.1. What is experimental design.
http://www.itl.nist.gov/div898/handbook/pri/section1/pri11.htm (1 of 3) [5/7/2002 4:01:42 PM]
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