Transcript Design of Experiments - People Server at UNCW

Design of Experiments
CHM 585
Chapter 15
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• Experimentation is one of the most
important methods in the Quality
Movement -- the quest for continuous
improvement in our products and
processes.
• If you can measure aspects of quality in
your product, and if you have factors
under your control, then you can
perform an experiment to find out
which factor settings result in the best
quality product.
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• Experimentation
is usually
expensive, so you
want to get the
most information
from the least
number of runs in
the experiment.
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One approach when looking for
optimal factor settings is to pick
starting values and then adjust
one factor to see if it helps and
keep fiddling with it until the
response seems to get better. But
even then sometimes it gets worse
because of random or external
factors. It's hard to tell.
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• Then, adjusting another factor improves
the response more. But after adjusting
that factor, the first factor's supposedly
optimal setting is no longer optimal
because changing the second factor has
affected how the first factor works
(interaction). But there are dozens of
potential factors that might affect the
product. Each fiddle with a new control
sends you back to adjusting all the
other factors.
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• Hundreds of runs. Little progress.
No comprehensive understanding
of the whole process after much
effort.
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A more scientific approach to the problem is to
reason out all the factors that might affect
the response. After choosing the most
promising 12 of them for investigation, a
computer software program can help select a
screening design that needs only 16 runs.
Then perform the runs, and enter the
responses into the computer. This complete
analysis can quickly identify the three most
important factors. Next the software performs
a response-surface design for 20 runs. When
the analysis is done, you have a complete
understanding of the response surface, know
what the optimal settings are, and what the
variability in the process is.
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Experimental Designs are used to identify or screen important
factors affecting a process, and to develop empirical models of
processes. Design of Experiment techniques enable teams to
learn about process behavior by running a series of
experiments, where a maximum amount of information will be
learned, in a minimum number of runs. Tradeoffs as to amount
of information gained for number of runs, are known before
running the experiments.
A typical plant Designed Experiment has 3 factors, each set at
two levels - typically the maximum and minimum settings for
each of the factors. A Designed Experiment with 3 factors each
at 2 levels, is called a 23 factorial experiment (or Taguchi L8
experiment), and requires 8 runs, as follows:
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run number Factor A Factor B Factor C
1
2
3
4
lo
hi
lo
hi
lo
lo
hi
hi
lo
lo
lo
lo
5
6
7
lo
hi
lo
lo
lo
hi
hi
hi
hi
8
hi
hi
hi
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Bromination of acetone
O
+ Br2
O
Br
Br
CH 2
CH
Br
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Dithionite reduction
NH2
N
H2N
NH2
NO
+ Na2S2O4
N
NH2
N
NH2
H2N
N
NH2
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Evolutionary Operation (EVOP)
• It combines small variable perturbations and
numerous replications of every process adjustment
with statistical analysis.
• Every process contains random process
fluctuations (noise) typically caused by such
factors as raw-material variations, equipment
deterioration, and instrument corrections.
• Because process responses to variable changes and
the random noise may occasionally have
comparable values, replication allows the effects
of the noise to average out so the true effects of 13
the variable changes can be determined
The Biological Neuron
• The most basic element of the human brain is a
specific type of cell, which provides us with the
abilities to remember, think, and apply previous
experiences to our every action. These cells are
known as neurons, each of these neurons can
connect with up to 200000 other neurons. The
power of the brain comes from the numbers of
these basic components and the multiple
connections between them.
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• All natural neurons have four basic
components, which are dendrites, soma,
axon, and synapses. Basically, a biological
neuron receives inputs from other sources,
combines them in some way, performs a
generally nonlinear operation on the result,
and then output the final result.
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• The brain basically learns from experience. Neural
networks are sometimes called machine learning
algorithms, because changing of its connection
weights (training) causes the network to learn the
solution to a problem. The strength of connection
between the neurons is stored as a weight-value for
the specific connection. The system learns new
knowledge by adjusting these connection weights.
• The learning ability of a neural network is
determined by its architecture and by the
algorithmic method chosen for training.
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