My
name
is
David
Fenn.
I
work
for
PPG
Industries
at
their
Coatings
Innovation
Center
just
outside
of
Pittsburgh.
Today
I'm
going
to
be
talking
about
some
of
our
experiences
with
using
DOE
for
novel
coatings
development,
particularly
focusing
on
the
importance
of
using
auxiliary
responses.
The
agenda,
I'll
talk
a
little
bit
about
research
methodology,
and
in
case
you're
wondering
what
I
mean
by
auxiliary
responses,
I'll
define
that
in
this
section.
Then
we'll
go on
to
two
examples.
The
first
one,
a
new
resin
development
for
architectural
coatings,
and
the
second
one,
a
protective
coating,
then
we'll
finish
off
with
a
few
general
observations.
There
are
various
frameworks
that
can
be
used
to
describe
the
new
product
development
process.
One
that
I
particularly
like
is
shown
here,
DMADV.
Here
we
have
five
stages.
The
first
stage
is
to
define
the
goals
of
the
project.
What
are
we
trying
to
achieve?
Then
we
get
into
measurement.
What
are
the
critical
characteristics
we
need
to
measure,
and
do
we
have
suitable
processes
in
place
to
measure
them?
Then
we
think
about
analyze
.
What
factors
can
we
change
to
make
improvements?
Then
onto
the
design
stage,
where
we
deliberately
manipulate
those
factors
and
the
levels
of
those
factors
to
try
and
affect
an
improvement
and
lead
to
optimum
performance.
Then
once
we
have
an
advanced
prototype,
we
get
onto
verification,
thinking
about
will
our
solution
work
in
the
real
world?
What
are
the
important
things
we
need
to
think
about
when
we
apply
this
framework?
Well,
in
terms
of
the
defined
stage,
the
goal
needs
to
be
clear
so
that
the
whole
organization
has
the
same
understanding
of
the
goals,
and
it
needs
to
be
impactful.
If
we're
successful
and
we
deliver
a
solution,
will
it
fill
a
real
unmet
need
in
the
marketplace
and
be
a
successful
product?
If
we
skip
now
to
the
end,
any
solution
we
apply
needs
to
be
cost-
effective.
It
needs
to
be
robust.
Then
the
middle
of
this
process,
we
want
to
get
through
this
process
as
quickly
and
as
efficiently
as
we
can.
We
want
to
deliver
the
product
to
the
marketplace
as
soon
as
we
can,
and
we
want
to
expend
the
minimum
amount
of
cash
and
the
minimum
amount
of
resource
to
do
that.
Clearly,
DOEs
and
a
lot
of
the
tools
that
are
available
in
JMP
are
well
set
up
to
make
us
succeed
in
this
area.
One
of
the
tools
that
I
like
to
use
particularly
early
on
in
a
project
is
a
process
map.
This
is
a
very
particular
type
of
process
map.
It's
really
mapping
the
process
of
carrying
out
the
research
and
development.
I'm
showing
here
a
simplified
example
of
a
process
map
to
develop
an
automotive
base
coat.
We
have
all
the
steps
that
are
involved
in
our
experiment.
We
make
a
resin,
we
use
that
resin
to
make
a
base
coat
paint,
we
spray
apply
that
base
coat
onto
a
substrate,
we
apply
a
top
coat
onto
that
base
coat,
then
we
cure
those
coatings
together,
and
then
we
measure
the
properties
that
we
get
from
all
of
that.
All
of
these
steps,
we
list
all
of
the
factors
that
might
play
a
role
in
these
separate
steps.
This
is
useful
for
a
number
of
reasons.
First
of
all,
it
gives
everybody
in
the
team
a
unified
understanding
of
what
the
process
is
we're
dealing
with
and
how
are
we
going
to
affect
it.
It
also
allows
us
to
capture
all
of
the
variables
we
can
think
of
that
might
play
a
role
in
the
various
steps
so
we
don't
overlook
anything.
Then
it's
a
good
starting
point
for
thinking
about
which
of
these
are
we
going
to
try
and
manipulate,
which
of
these
are
we
going
to
focus
on
to
try
and
deliver
a
successful
project?
These
factors
are
further
subdivided
and
categorized.
First,
we
have
our
Xs.
These
are
the
variables
that
we
can
manipulate
to
try
and
affect
an
improvement
in
our
product
or
our
process.
Then
we
have
our
big
Ys.
These
probably
appear
in
the
specification
of
the
product.
These
are
what
we're
really
trying
to
achieve.
This
is
what
the
customer
really
cares
about,
what
the
customer
will
pay
for.
Next,
we
have
our
Ns,
noise
variables.
These
could
be
variables
that
we
may
be
not
controlling,
we're
not
deliberately
manipulating,
but
things
that
could
introduce
noise
into
the
process,
either
during
the
experiments,
during
the
new
product
development,
or
in
the
end
application,
in
the
manufacture
of
the
product
or
the
end
use
of
the
product.
Then
finally,
the
subject
of
today's
talk,
we
have
our
auxiliary
responses,
which
we
label
as
little
Ys.
These
might
not
appear
in
the
specification,
the
customer
might
not
even
be
aware
of
these,
but
they're
measurements
we
can
take
at
various
stages
of
the
process
that
might
tell
us
something
about
what's
going
on.
I
said
in
the
previous
slide
that
one
of
our
goals
is
to
get
through
this
whole
process
quickly,
as in
efficiently
as
we
possibly
can.
One
question
that
raises
is,
why
don't
we
just
measure
our
big
Ys?
We
have
the
ability
to
carry
out
DOE's.
We
could
optimize
for
our
big Ys,
we
could
build
predictive
models.
Isn't
that
all
we
need
to
do?
Why
should
we
spend
time?
Why
should
we
spend
money
measuring
some
of
these
little Ys
when
they're
not
the
real
goal
of
the
outcome?
Well,
I
hope
in
the
next
couple
of
examples
that
I
can
show
you,
some
cases
where
carefully
selecting
these
little
Ys
and
doing
some
good
analysis
can
be
really
critical
to
the
success
of
a
project.
Our
first
example
here,
the
development
of
a
new
resin
for
architectural
coatings.
The
goal
was
to
come
up
with
a
single
resin
that
could
meet
all
of
the
performance
requirements
across
several
product
lines
in
several
countries.
Our
starting
point
was,
we
had
no
single
resin
that
could
meet
all
those
requirements.
We
were
using
different
resins
in
different
products,
different
resins
in
different
countries,
and
we
needed
to
come
up
with
a
solution
that
allowed
us
to
reduce
the
complexity
there.
Our
early
prototype
struggled
in
a
number
of
areas,
but
one
particular
area
was
tint
strength.
The
way
these
white
base
paints
would
be
used
is
if
I
go
into
a
store
and
request
a
paint
of
a
particular
color
to
paint
the
walls
of
my
house,
the
store
will
take
that
white
base
paint
and
add
specified
amounts
of
concentrated
color
toners
to
that
paint
to
create
a
specific
color.
It's
really
critical
to
be
able
to
hit
a
target tint
strength,
which
is
the
measurement
of
how
quickly
that
color
will
change
as
we
add
a
certain
amount
of
a
particular
toner.
We
need
to
be
able
to
control
that
and
hit
it
reproducibly
to
achieve
the
wide
spectrum
of
colors
we
need
to
achieve.
We
also
had
a
few
issues
in
terms
of
poor
heat
age
stability
and
poor
resin
reproducibility.
Our
approach
was
to
carry
out
some
sequential
DOE's
to
learn
how
to
control
tint
strengths
and
some
of
the
other
factors.
I'm
showing
the
progress
on
this
plot
at
the
bottom
left-hand
side
of
this
screen.
Before
we
started
the
DOE's,
just
some
of
the
exploratory
experiments;
the
orange
bar
represents
the
range
of
tint
strengths
we
were
able
to
achieve.
We
can
see
that
is
far
below
the
target
range
of
tint
strengths
shown
by
this
green
bar
on
the
plot.
As
we
carried
out
the
DOE's,
we
learned
how
to
control
tint
strength.
We
were
able
to
increase
it
until
towards
the
end
of
the
project
when
we
were
doing
our
optimization
DOE's,
we
were
nicely
centered
around
this
target
tint
strength.
We
were
able
to
build
predictive
models
and
use
those
in
conjunction
with
predictive
models
for
some
of
the
other
key
properties
to
identify
white
space
where
we
met
all
of
the
target
properties
at
the
same
time.
But
rather
than
talk
about
the
whole
project,
I
want
to
now
focus
on
one
particular
DOE
that
we
carried
out.
The
goal
of
this
DOE
was
to
confirm
and
quantify
something
we'd
observed
previously,
that
the
particle
size
of
the
resin
we
were
making
was
a
big
factor
in
controlling
tint
strength.
These
resins
are,
in
effect,
dispersions
of
little
particles
of
resin
in
water,
and
it
was
the
size
of
those
particles
that
seemed
to
be
important.
We
were
also
using
what
we
call
a
co-surfactant
to
help
disperse
those
particles,
and
we
had
a
few
choices
about
where
in
the
process
we
could
add
that
co-surfactant.
We
wanted
to
look
at
a
couple
of
candidates
for
the
addition
point
of
that
co-surfactant
to
see
if
it
affected
the
key
properties.
Then
finally,
up
until
this
point,
all
the
resins we've made,
we've
made
at
the
Coatings
Innovation
Center.
We
now
wanted
to
check,
could
we
make
these
resins
reproducibly
across
three
different
locations?
The
DOE
we
carried
out
is
shown
on
the
right-hand
side
here.
We
have
three
levels
for
our
target
particle
size.
We
have
two
levels
for
the
addition
point
of
the
co-surfactant.
That
gives
us
a
full
factorial
DOE
with
six
runs.
Then
we
replicated
that
DOE
across
three
different
laboratories.
I'll
go
straight
into
JMP
and
I'll
show
you
what
the
data
table
looks
like.
You
can
see
here
we
have
the
original
data
table,
the
DOE,
but
now
we
have
a
whole
collection
of
data
that
we
gathered
during
the
DOE.
The
first
thing
we'll
do
is,
we'll
look
at
what
we
learned
about
tint
strength.
I've
already
built
here
a
reduced
model
for
tint
strength.
If
we
have
a
look
at
the
effect
summary
to
start
with,
we
can
see
that
the
location
of
addition
of
the
co-surfactant
wasn't
a
factor
in
determining
tint
strength.
That
dropped
out
of
the
model.
But
we
do
see
that
the
target
particle
size
and
the
reactor
location
were
factors,
as
well
as
the
interaction
between
target
particle
size
and
reactor
location.
If
we
look
up
at
our
actual
by predicted
plot,
we
can
see
it
looks
like
a
pretty
nice
model.
We've
got
a
nice
R-square,
and
everything
looks
to
be
in
pretty
good
shape.
Then
probably
the
best
way
of
understanding
what's
happening
and
what
this
model
is
telling
us
is
to
look
at
the
prediction
profiler
here
at
the
bottom.
We
see
our
anticipated
effect
of
target
particle
size
on
tint
strength.
As
we
increase
target
particle
size,
we
get
higher
tint
strength.
Then
if
we
look
across
at
reactor
location,
what
we
see
is
that
Lab A
and
Lab C
are
giving
broadly
similar
results.
But
if
we
look
at
Lab B,
first
of
all,
we
see
that
the
tint
strength
that
we
get
from
Lab B
is
significantly
higher
than
we
were
getting
from
Lab A
or
Lab C.
We
also
see
that
the
dependence
on
particle
size
is
much
less
from
Lab B
than
we
saw
from
the
other
two
labs.
This
was
a
problem
for
us.
Whenever
we
see
that
different
labs
are
producing
different
results
with
the
same
resin
and the
same
process,
it
can
be
a
really
long
task
to
work
out
what's
going
on
here.
There's
so
many
potential
candidates
for
the
cause
of
this
poor
reproducibility.
At
this
stage,
we
were
very
concerned
that
it
was
going
to
take
us
a
long
time
to
resolve
this,
that it
was
going
to
derail
the
project,
and
we're
going
to
miss
our
target
launch
dates.
Before
we
went
into
any
specific
activity
to
try
and
address
this
problem,
the
obvious
first
step
was
to
look
at
the
data
that
we'd
already
gathered
in
this
data
table
and
see
if
there
were
any
clues
that
could
maybe
give
us
a
hint
as
to
why
Lab B
was
giving
different
properties.
Whenever
I
see
a
wide
data
table
like
we've
got
here,
one
of
the
first
tools
that
I
always
go
to
is
the
column
switcher.
The
way
in
this
case
that
I
will
implement
this
is
the
first
step
is
to
build
a
variability
chart
that
best
shows
the
problem
that
we're
having.
I've
pre-built
a
variability
chart
here
where
I've
got
target
particle
size
and
reactor
location
as
my
X-axis
and
I've
got
the
initial
tint
strength
as
my
Y-axis.
The
first
task
is
to
get
this
into
a
format
that
the
best
represents
the
problem
we're
dealing
with.
The
first
thing
I'll
do
is
swap
over
my
target
particle
size
and
react
or
location.
I'll
also
add
and
connect
the
cell
means
to
add
some
lines
here.
Now
I'm
pretty
happy
with
this.
I
think
this
nicely
reflects
the
problem
that
we're
dealing
with.
We
can
see
Lab A
and
Lab C
very
similar
results,
but
Lab B,
higher
tint
strength
and
less
dependence
on
tint
strength
and
particle
size.
Now
I
can
use
my
column
switcher,
and
what
this
will
allow
me
to
do
is
keep
this
plot
in
exactly
this
format,
but
quickly
switch
out
this
Y-axis,
the
initial
tint
strength,
for
any
other
variable
that
I've
got
in
my
data
table.
I'll
go
into
the
redo
platform
and
select
the
column
switcher.
Now
I
can
select
any
of
the
other
factors
in
my
data
table.
I'm
just
going
to
select
everything
that
I've
got
in
my
data
table.
Then
when
I
hit
OK,
I
now
have
this
column
switcher
to
the
left
of
my
plot.
I
can
click
on
any
of
these
factors
and
it
will
change
this
axis
but
keep
the
plot
in
exactly
the
same
format.
If
I
select
particle
size,
I
can
see
now
I'm
plotting
my
actual
measured
particle
size
against
target
particle
size
and
reactor
location,
exactly
the
same
format.
It
looks
like
in
this
case,
all
three
labs
are
giving
pretty
similar
results.
I'm
not
seeing
anything
that
gives
me
a
clue
as
to
what's
going
on,
but
I
can
quickly
just
scroll
through
this
whole
data
set.
I'm
seeing
mostly
noise
right
now.
I'm
not
seeing
any
patterns
that
seem
to
be
particularly
helpful,
but
I'll
keep
going.
When
I
get
to
this
plot
here,
so
now
I'm
plotting
conductivity,
I
see
interestingly
that
Lab B
is
making
resins
with
much
higher
conductivity
than
Lab A
and
Lab C.
That's
one
useful
observation.
I'll
keep
going.
This
next
one,
this
is
actually
another
measurement
of
conductivity
after
the
resin
has
been
in
a
hot
room
for
a
week,
showing
the
same
thing,
still
confirming
that
Lab B
is
giving
higher
conductivity.
I'll
keep
going.
Mostly
noise,
maybe
a
little
bit
of
an
indication
that
the
molecular
weight
from
Lab B
is
slightly
lower.
I'll
keep
going.
Again,
still
not
seeing
anything
that
interesting,
mostly
noise.
But
then
I
get
to
this
plot
here,
and
again,
now
we're
plotting
the
pH
of
the
resins
one
hour
into
the
process,
so
early
into
the
process,
the
acidity
or
pH
of
the
resin.
Lab B,
again,
is
different
from
Lab A
and
Lab C.
It's
giving
me
much
higher
pH.
Keep
going
just
to
check
if
there's
anything
else.
This
was
the
initial
plot
we
started
with
of
initial
tint
strength,
and
then
the
last
one
is
the
paint
viscosity,
where
everything
looks
pretty
similar.
Really
quickly
using
column
switcher,
I
found
out
that
not
only
is
Lab B
making
resins
with
higher
tint
strength,
it's
making
resins
with
higher
conductivity
and
higher
acidity,
higher
pH.
What
could
that
be
telling
us?
What
might
be
causing
higher
pH
and
higher
conductivity?
Well,
these
resins,
I
said,
were
a
dispersion
of
a
polymer
particle
in
water.
Anything
that's
changing
the
conductivity,
the
pH
is
going
to
be
in
the
water
phase.
It's
not
going
to
be
in
the
resin
phase.
What
we
did
was
we
precipitated
by
centrifuge,
we
precipitated
out
the
resin
and
just
analyzed
the
water
phase.
We
carried
out
a
lot
of
analysis,
but
one
of
the
things
we
worked
on,
I'm
showing
on
this
plot
on
the
right-hand
side
of
PPMs,
of
parts
per
million
of
phosphorus
and
sulfur
in
that
water
phase.
If
I
look
at
the
orange
bars
to
start
with,
the
sulfur,
I
can
see
all
of
the
resin
s
from
all
three
labs
are
very
similar,
but
the
blue
bars,
the
level
of
phosphorus,
Lab
B
is
making
resins
with
about
four
times
as
much
phosphorus
as
they
were
making
from
Lab A
and
Lab C.
When
we
looked
at
the
recipe
for
making
these
resins,
there's
only
one
raw
material
that
brings
in
phosphorus.
On
a
bit
of
further
investigation,
what
we
found
out
was
the
supplier
that
was
delivering
this
material
to
Lab B
was
mistakenly
supplying
something
that
was
four
times
as
concentrated
as
it
should
have
been,
and
four
times
as
concentrated
as
they
were
providing
to
Lab A
and
Lab C.
The
auxiliary
data
that
we
looked
at
in
this
DOE
using
the
column
switcher,
we
were
able
to
really
quickly
pinpoint
the
cause
of
that
problem.
We
didn't
have
to
expend
time
to
get
there.
The
project
stayed
on
track,
and
there
was
even
a
bonus.
We
learned
that
increasing
the
level
of
this
material
with
the
phosphorus
was
another
tool
we
had
to
increase
the
tint
strength.
We
would
have
probably
never
been
aware
of
that
if
we
hadn't
carried
out
this
analysis
and
had
this
happ
y
accident.
That's
the
first
example.
We
go
on
to
the
second
example
now.
In
this
case,
we're
dealing
with
a
protective
coating,
a
coating
that's
designed
to
go
over
metal
substrates
like
iron
and
steel
and
protect
from
corrosion
.
We
have
five
experimental
resins
that
we
want
to
look
at,
a
resin
that's
designed
to
give
good
corrosion,
and
then
four
resins
that
are
designed
to
improve
the
flexibility
of
the
coating.
The
first
three
of
these
resins
are
added
in
the
first
stage
of
the
coating
preparation,
and
then
the
last
two
get
added
in
a
separate
later
step.
We
have
two
questions
here
we're
trying
to
answer.
How
do
the
resins
affect
corrosion
and
affect
flexibility,
and what
is
the
best
combination
of
the
levels
of
these
resins
to
give
us
the
best
combination
of
corrosion
and
flexibility?
Again,
we
use
DOE,
we
were
able
to
build
predictive
models,
and
here
we
were
using
the
mixture
profiler
to
identify
some
white
space
that
we
can
work
in.
This
DOE
is
a
little
bit
more
complicated
than
the
first
one,
so
I'm
trying
to
represent
pictorially
what
we
were
dealing
with
here.
If
we
look
at
our
first
stage
of
our
coating
manufacture,
in
addition
to
our
three
experimental
resins,
we
have
a
main
resin
at
a
fixed
level.
In
effect,
our
three
experimental
resins
are
three
mixture
variables
here
because
they
form
the
rest
of
this
100
%.
They
add
up
to
a
constant
sum
of
57.77
%.
Three
mixture
variables
dealing
with
there.
Then
in
stage
two,
we
can
deal
with
our
other
two
resins
as
independent
variables
because
they're
not
part
of
that
mixture.
We
have
three
mixture
variables,
two
independent
variables.
We
also
have
some
levels
that
the
formulators
were
able
to
decide
they
wanted
to
work
in
based
on
prior
experience
for
all
of
these
resins.
Then
we
have
some
constraints
on
the
combinations
we're
dealing
with.
For
example,
at
the
start
here,
we
want
the
sum
of
Flex2
and
Flex3
to
be
more
than
10%
but
less
than
30%.
There
are
some
other
constraints
as
well.
A
fairly
complicated
DOE,
but
using
custom
design,
it's
relatively
straightforward
to
build
this
DOE.
Definitely
some
tips
and
tricks
in
terms
of
how
to
build
the
DOE,
what
model
to
use,
and
how
to
analyze
that
data.
I
don't
have
time
to
go
through
that
today,
but
I'd
be
perfectly
happy
to
talk
about
that
offline
if
anybody's
interested.
But
let's
go
straight
into
JMP
and
we'll
look
at
this
example.
Here
we
have
the
DOE
that
we
carried
out.
It
was
a
16-run
DOE.
If
we
go
right
across
to
the
left-hand
side,
we
have
our
three
mixed
variables
and
our
two
process
variables.
We've
measured
our
flexibility
and
corrosion
and
then
we
have
a
lot
of
other
auxiliary
responses
we've
measured.
I
was
able
to
build
good
predictive
models
for
flexibility
and
corrosion.
What
I'm
going
to
do
is
just
show
you
those
models
in
the
profiler
just
to
help
us
understand
what
we're
learning
and
what's
going
on.
I'll
add
those
two
predictive
models
that
I
built
to
my
profiler,
and
then
I
get
my
profiler
here.
I
can
see,
first
of
all,
I'm
plotting
flexibility
and
corrosion
here.
Lower
numbers
are
better
for
both
of
these
responses.
Lower
numbers
for
flexibility,
lower
numbers
for
corrosion
are
what
we're
targeting.
I
can
see
as
I
add
my
corrosion
resin,
if
I
increase
the
level,
I
get
better
corrosion
performance,
but
unfortunately,
I
get
worse
flexibility.
The
opposite
is
true
for
most
of
these
flexibleised
resins.
As
I
add
more
of
these,
I'm
getting
better
flexibility,
but
worse
corrosion.
This
is
something
that's
very
common
in
coatings
development
and
lots
of
other
areas.
Seems
like
there's
always
a
pair
of
properties
where
if
we
improve
one
of
them,
we
always
make
the
other
one
worse.
But
if
I
come
across
to
my
Flexabiliser
4
resin,
something
really
interesting
here,
as
I
add
more
of
this
resin,
I
get
better
flexibility,
but
I
don't
suffer
at
all
in
terms
of
corrosion.
This
is
going
to
be
a
really
useful
tool
for
us
to
optimize
the
combination
of
flexibility
and
corrosion.
But
I'd
like
to
understand
a
bit
more
about
the
science
behind
this.
What's
happening?
What's
unusual
about
Flex4
that
allows
us
to
improve
our
flexibility
without
degrading
corrosion?
Again,
I
want
to
use
all
of
this
auxiliary
data
that
I've
gathered
in
my
data
table
to
help
me
understand
that.
What
I
did
is,
I
want
to
look
through
this
table,
and
I'm
going
to
use
a
different
tool
this
time.
I'm
going
to
use
multivariate.
If
I
select
that,
this
allows
me
to
basically
look
at
the
correlation
between
all
the
combination
of
factors
that
are
in
my
data
table.
I'll
select
everything
that
I
measured
and
I'll
add
it
in
the
Y
columns
and
just
hit
OK.
This
generates
my
multivariate.
The
first
thing
I
see
is
this
table
here
where
I've
got
all
the
correlations
for
all
the
pairs
of
combinations
of
the
factors
that
are
put
in
my
table.
I
can
see
there
are
some
pretty
nice
correlations
here.
I'm
seeing
some
fairly
strong
correlations,
but
it's
a
little
bit
difficult
to
go
through
all
this,
a
bit
overwhelming
to
go
through
all
this
and
pick
out
any
interesting
patterns.
I've
also
got
my
scatter
plot
here,
and
if
I
add
a
fit
line
to
these
scatter
plots,
again,
I'm
seeing
some
fairly
strong
correlations,
but
still
I
think
this
is
a
bit
overwhelming
to
dive
straight
into.
The
tool
that
I
like
to
use
to
start
with
here
is
pairwise
correlations.
If
I
select
that,
this
generates
a
new
table
where
I've
got
all
the
possible
pairs
of
variables
and
it's
giving
me
the
correlation.
I
can
sort
this
table
based
on
any
column.
I'm
going
to
sort
by
the
significant
probability
and
I'll
make
it
a
scending
because
I
want
my
low
significant
probabilities
to
be
at
the
top
of
my
table.
Then
if
I
hit
OK,
I
can
see
the
first
and
strongest
correlation
I
get,
in
fact,
involves
this
Flexibiliser
Resin
4
that
was
giving
us
this
interesting
behavior.
I
can
see
a
strong
correlation
with
the
secondary
or
TG2.
This
is
a
glass
transition
temperature.
The
glass
transition
temperature
is
a
temperature
at
which
a
coating
changes
from
being
a
glassy
hard
material
to
a
soft
rubbery
flexible
material.
My
Flex4
level
is
correlating
with
here
a
secondary
glass
transition
temperature
that
I'm
measuring.
And
I
can
see
also
if
I
go
a
little
bit
further
down,
my
primary
glass
transition
temperature,
the
main
glass
transition
correlates
strongly
with
the
corrosion.
S
cientifically,
I
think
they're
interesting
observations.
What
I
did
based
on
that
is
I
also
built
predictive
models
for
my
primary
TG
and
for
my
secondary
TG.
Now
I
can
look
at
my
profiler,
but
I
can
include
all
of
my
four
predictive
models.
Now
I'll
include
the
two
I
did
before,
flexibility
and
corrosion,
but
also
my
primary
TG
and
secondary
TG.
Now
what
I
can
see
is
that
the
first
two
rows
are
exactly
what
we
were
looking
at
before.
If
I
look
at
my
primary
TG,
I
can
see
whatever
I
do
in
terms
of
adding
new
resin.
For
example,
if
I
add
more
of
my
corrosion
resin,
I'm
increasing
my
primary
TG,
and
that's
correlating
with
an
improvement
in
corrosion.
The
flexibilising
resins,
if
I
add
more
of
those,
I'm
decreasing
my
primary
TG
and
making
my
corrosion
worse.
That
primary
TG
does
seem
to
correlate,
as
the
multivariate
is
showing,
correlate
very
well
with
corrosion.
If
I
look
at
my
Flex4
resin,
it
was
having
no
effect
on
corrosion
and
it's
having
no
effect
on
my
primary
TG,
so
it's
different
from
my
other
flexivising
resins,
but
I
can
see
for
my
secondary
TG,
as
I
add
more
of
my
Flex4,
it's
rapidly
decreasing
the
secondary
TG.
The
other
resins
really
don't
have
much
effect
on
secondary
TG.
What
does
that
mean?
What
can
I
learn
from
that?
Well,
any
material
that
has
multiple
TGs,
glass
transition
temperatures,
it's
usually
a
sign
that
it's
a
multi-phase
raw
material.
It's
not
a
homogeneous
material.
That
was
the
case
here
when
we
did
some
microscopy.
What
we
saw
was
our
coating
had
a
continuous
phase
shown
by
this
gray
material
here,
but
it
had
dispersed
in
that
a
secondary
phase.
The
primary
glass
transition
temperature
was
correlating
with
that
primary
continuous
phase
and
the
secondary
lower
glass
transition
temperature
was
correlating
to
this
secondary
phase
that
we
have
here.
We
had
a
hard
glassy
primary
phase
and
then
a
soft
rubbery
secondary
phase.
Why
that's
important
is
usually
high
glass
transition
temperature
does
lead
to
better
corrosion
because
it
inhibits
the
diffusion
of
anything
through
this
layer
and
stops
material
getting
to
the
substrate,
the
metal
substrate,
and
causing
corrosion.
Usually,
if
I
want
to
make
flexibility
better,
I
have
to
make
this
continuous
layer
softer
and
that
degrades
corrosion.
But
with
this
type
of
morphology,
I
was
able
to
keep
my
hard
continuous
phase
and
gain
flexibility
through
a
separate
dispersed
rubbery
phase.
This
meant
that
anything
that
wanted
to
diffuse
through
the
coating
and
cause
corrosion
was
always
having
to
diffuse
through
this
high
TG
area.
It's
given
me
the
combination
of
good
corrosion
and
good
flexibility
together.
The
auxiliary
data
that
I
gathered
was
really
responsible...
The
analysis
to
that
was
responsible
for
the
learning
of
what
was
going
on
in
this
system.
In
conclusion,
it's
definitely
possible
to
carry
out
successful
DOEs
where
we
only
measure
the
critical
responses,
the
big Ys.
But
I
hope
I've
shown
that
including
carefully
selected
auxiliary
responses,
little Ys
can
often
be
really
valuable,
can
bring
clarity
to
unexpected
results,
and
it
can
help
us
to
build
scientific
knowledge.
I
hope
I've
also
shown
that
JMP
provides
some
tools
that
really
help
us
with
this.
I've
shown
a
couple,
but
there
are
many
more
that
are
available.
I'd
finally
like
to
finish
off
by
thanking
the
many
associates
at
PPG's
Coatings
and
Innovation
Center
who
contributed
to
this
work.
