Hello
everyone.
Thank
you
for
attending
this
experience
sharing
session
on
JMP.
My
name
is
Stephane
Georges,
I'm
R&D
Project
Manager
at
Clecim,
and
I'm
very
keen
on
data
science.
Today,
we'll
talk
to
you
about
the
design
of
experiment
methodology,
and
more
specifically,
about
the
case
we encountered
when
trying
to
find
optimal
parameters
for
laser welding
process.
During
this
presentation,
I
will
show
you
how we use
JMP
for
various
platform
and how JMP
adapted
to
the
reality
of
the
field
by
taking
into
account
a very irregular
study
area
and a
very
imperfect
study matter.
Without
further
delay,
I
will
start my presentation
by
telling
you
who
we
are
and
what
we
do.
Clecim
is
an
engineering
and
production company of equipment
for
the
steel
industry.
We
are
located
in
Montbrison
in
France
in
the
surrounding
of
Lyon.
The
history
of Clecim
is
not
new,
as
we
celebrated
five
years ago
our
100th
anniversary.
The
area
of
the
site
is
about
12 soccer
field
where
work
230
employees,
mainly
composed
of
managers
and
technicians.
A
s
we
like
statistics
when
working
with
JMP,
here
is
the
first
one.
Our
population
is
composed
mainly
of
men,
about
80 percent
and
20 percent
of
women.
Concerning
Clecim
activities:
Our
first
activities
is
studies and consulting activities
for
our
flat
steel
producer
customer.
We
supply
individual
machine,
or
we
supply
a
complete
production
line
such
as
pickling
line,
annealing
line,
galvanizing line, painting
line
and
so
on.
We
also
have
an
activity
of
services
for
the
furniture
or
spare
parts,
for export
missions,
maintenance,
and
so
on.
I
put
on
this
picture
a
typical
layout
of
a
galvanizing
line.
This
is
to
give
you
an
idea
of
such
processing
line.
This
one
is
dedicated
to
the
automotive market
and
the
length
of
such
an
equipment
is
about
half
a
kilometer,
so
a
very
huge
industrial
plant.
When
I
talk
previously
about
a
machine,
I
had
in
mind
rolling
equipment
such as rolling
mill,
plate
levellers,
automated
strip
surface
inspection system,
and
even
laser
welding
machine.
This
is
on
this
last
equipment
that
we
are
going
to
focus
on
right
now.
I
will
now
talk
to
you
about
autogenous
laser
welding
process.
Autogenous means without filling wire.
I
will
talk
also
about
the
parameter
and factors
that
govern
this
process.
But
first
of
all,
I
would
like
to
introduce
our
machine,
the
subject
of
our
study.
On
the
left
part
of
the
slide,
you
can
see
our
welding
machine.
In
fact,
not
only
our
welding
machine
but
it's
containment,
the
machine
is
inside
the
containment
for
safety
reasons
because we
are
using
laser.
The
dimension
here
of
the
door
gives
you
an
idea
of
this
welding
machine
of
scale
one.
This
is
a
huge
industry,
our welding
machine.
On
the
right
part
of
this
presentation,
you can
see
a
partial
inside
of
this
welding machine,
where
you
can
see
the
clamps
and
the
[inaudible 00:04:37]
of
the
machine.
Inside,
you
see
the
top
portion of the strip,
the
head
and
the
tail
of
the
strip,
that
will
be,
first
of
all,
cut
also
with laser and
finally
brought
together
in
order
to
be
welded.
I
will
now
talk
to
you
about
our
target
and
constraint.
Of
course,
our
objective,
our
target
is
to
have
a
good
weld.
To
do
that,
we
need
to
achieve
two
objectives.
The
first
one
is
to
have
a
weld seam
which
is
defect-
free.
Here
on
this
slide,
I
put
you
an
example
of
such
a
weld.
This
is
picture
number
one.
You
can
see
on
this
picture
that
this
whole
thing
is
quite
nice
without
any
defect.
When
I
talk
about
defect,
here
is
a
list
of
the
typical
defect
that
we
encounter
when trying
to
weld
with
a
laser.
Typically,
we
could
have
some
patterns.
This
is
picture
number
two.
This
is
a
top
view.
In
such
a
case,
this
is
the
molten
material,
which is ejected
from
the
top
of
the
weld.
We
could
have
also
chain of pearls.
This
is
picture
number
three.
This
time
this
is
a
bottom
view,
and
this
is
some
droplets
at
the
bottom
of
the
weld
seam.
We
could
have
also
other
defects,
such
as
humpings, underfillings, or even holes.
This
is
picture
number
four
here.
And
here,
typically,
this
is
the
case
when we have a
very
low
travel
speed
and
a
very
high
power
density.
Instead
of
welding,
we
are
drilling
at the
material
and
we
create
some
holes.
Of
course,
this is
we
absolutely
want
to
avoid.
Otherwise,
we
will
decrease
the
resistance
of
our
weld.
This
is
a
transition
for
the
second objective
because
we
want
not
only
the
weld seam
to
be
p
erfect,
but
we
want
it
also
to
be
resistant.
This
is
evaluated via
an
Erichsen
type
cupping
test,
so this I
will
describe
it
a
little
bit
later.
Our
target
is
to
have
a
trend
as
close
as
possible
to
the
one
of
the
base
material.
I
will
now
talk
to
you
about
the
last welding parameters,
the
factor
governing
the
process.
On
this
left
part
of
this
slide,
I
put
you
a
very
schematic
view
of
the
process.
In
gray
at
the
bottom,
you
can
see
the
two
pieces
of
material
that
we
want
to
weld together
that
can
be
of
the
same
nature
and
thickness
on it.
In
yellow,
this
is
laser
welding
head
that
is
connected
in
blue
to
its
laser
source.
In
order
to
imagine
the
kind
of
power
that
we
use
for
such
an
application,
imagine
that
when
you
use
a
laser
pointer,
typically
for
a
presentation,
such
kind
of
device
has
a
power
of
just
one
milliwatt.
Here
the
last
source
we
use
has
a
power of 12 million times.
It is 12
million
times
more
powerful
that's
such
a
small
device.
Just
to
explain
that
we
have
a
very
huge
power.
We
need
very
little
power
to
cut our material
and
to
weld
also
this
material.
On
the
right
part
of
this
presentation,
I
could use
the
process
parameter,
the typical process
parameter,
which can be, first
of
all,
the
laser power,
the
travel
speed
of
the
welding
carriage,
the
focusing
distance,
the
gap
between
the
plates,
the
thermal
treatment
that we can
apply
afterwards,
and
so
on.
But
in
fact,
for
simplicity
reason,
in
the
rest
of
this
presentation,
we will focus
only
on
the
two
main
one,
which is the
laser power
and
the
travel
speed.
We
will
also
consider
that
the
materials
are
identical
and
of
the
same
thickness.
You
will
see
that
just
with
these
two
parameters
we
will
have
enough
to
do.
Okay,
so
the
picture
is
set.
We
have
two
targets.
One
is
to
have
a
weld
seam
free
of
defect
and
we
want
also
to
have
its
resistance.
We
are
now
going
to
focus
on our case
study
towards
a
good
weld.
Our
first
target
is
to
have
a
weld
, which
is
defect-
free.
We
are
going
to
search
for
what
is
called
weldability
lobe.
To
do
that,
we
need
to
get
some
data.
To
get
some
data
we
will
use
the
so
called
Power JMP procedure.
In
that
case,
nothing
to
do
with
JMP,
even
if
JMP
is
a
powerful
software.
But
this
is
how
the
procedure
is
called.
The
picture
on
the
bottom
gives
you
an
example
of
such
a
procedure.
At
a
fixed
speed,
we
will
perform
11 successive
Power
JMP.
In
that
case,
we
will
switch
from
two
kilowatts
to
eight
kilowatts
to
three
kilowatts
and
so
on.
The
target
is
to
reduce
the
number
of
options
we
have
to
do,
and
in
just
one
weld, we will have 11 sample
and
we
will
have
11
observations
to
do.
Afterwards,
we
will
visually
examine
the
upper
part
of
our
bead,
the
lower
part
of
our
bead
for
each
slot
of
this
sample.
All
of
these
data
are
collected
into
JMP
and
we
will
use
the
Graph Builder
platform
in
order
to
display
this
map.
This
is
what
I'm
going
to
show
you
right
now.
I
go
to
my
JMP
journal
here.
We
will
have
four
steps
to
follow.
This
is
our
first
step,
building
the
welding
format.
I
will
open
my
table.
I
collected
all
the
data
in
this
table.
I
have
all
my
parameters
here,
so the
laser power, the welding speed.
In
this
column,
I
inserted
my
visual
observation.
Is
there
any
penetration?
Yes/N
o.
Do
we
have
material
loss?
Yes/N
o.
Humping? Yes/No, and
so
on.
A
t
the
end
of
this
file,
I
also
put,
as you can see,
I
had
two
additional
columns
of
expression
vector
type
where
I
have
inserted
the
picture
of
all
my
observations.
As
you
can
see
here
also,
I
requested
to
have
this
information
displayed
in
the
study
area.
Now,
we
are
ready
to
open
our
Graph Builder.
Here
I
can
learn
it,
okay?
All
the
data
has
been
collected
in
this
map.
Here
you
can
see
that
on
the
X-
axis, I put my welding speed.
On
the
Y-
axis,
I
have
my
laser power.
I
have
associated
for
each
defect
color
or
shape.
A
lso,
we
can
have
an
association
of
the
color
and
shape
in
that
way,
which
is
a
convenient
way
because
we
can overlay
four
different
type
of
defects
at
the
same
time
for
each
point.
This
is
what
I'm
going
to
show
you.
For
instance,
if
I
take
this
blue
point
here,
according
to
the
legend,
we
have
top
spatters.
This
is
exactly
what
my
pictures
show
you.
Here,
this
is
the
picture
of
the
upper
part of my weld,
here
is
a
picture
of
the
lower
part
of
my
weld.
Here
we
can
see
that
we
have
effectively
top
spatters,
whereas the
bottom
part
is
defect-
free.
For
instance,
if
I
take
another
one,
if
I
take
this
purple
one
here.
So
purple,
this
is
the
association
of
the
blue
defect
and
the
red
defect.
We
have
top
spatters
and
bottom spatters,
which
is
effectively
what
we
can
see
here
on
that
picture.
This
is
a
convenient
way
to see
if
effectively.
I
have
no
mistake
in
order
to know
the
magnitude
of
the
defect.
This
feature
using
the
pictures
is
also
very
convenient
because,
for
instance,
if
I
take
this
point
here
and I lock the
pictures,
and
if
I
take
this
additional
point
here
and
I
lock
also
the
pictures,
we
can
see
that
for
constant laser power,
I
can
compare
the
pictures
and
see
what
are
the
effects
when
varying
the
welding speed.
In
that
case,
when
we
increase
our
welding speed,
we
can
see
that
the
width
of
our
weld seam
decrease
both
on
the
top
and both on
the
bottom.
This
is
a
convenient
way,
let's
say,
to
dip
into
the
understanding
of
our
process.
I
will
close
that.
I'll
come
back
to
my
study
case.
We
are
interested
in
the
good weld
area.
This
is
the
area
that I'm going
to
highlight
here.
This
is
the
black
area.
Okay, like that.
Okay,
this
is
our
area
of
interest.
Now,
what
we
want
to
do,
let's
say,
to
investigate
the
behavior
of
our
weld seam
from
the
resistance
point
of
view
on
that
typical
area.
Of
course,
we
want
to
do
it
with
a
minimum number of tests
and
we
will
perform
a
design
of
experiment
on
this
very
irregular
study
area.
Okay,
so
I
go
back
to
my
presentation.
But
before
answering
into
the
conception
of
the
design
of
experiment,
we
have
another
interesting
topic
to
do
with
JMP,
because
we
have,
first
of
all,
to study
the
strength
of
our
base
material.
We
have
to
evaluate
the
basic
strength
of
the
material
via
an Erichsen-
type
cupping
test.
Why
are
we
doing
that?
We
have
three
targets,
three
objectives.
The
first
one
this
is
to
establish
a
reference
from
the
strength
point
of
view.
In
that
way,
we
will
be
able
to
compare
the
resistance
of
our
base
material
with
the
resistance
of
our
weld seam.
This
is
the
first
point.
The
second
point
is
to
be
able
to compare
the
two
pieces
of
material
that
our
customer
sends
us.
We
want
to
be
sure
that
these
two
pieces
have
the
same
behavior.
For
that,
we
have
to
ensure
that
they
can
be
comparable.
The
last
point
is
that
we
want
also
to
check
that
the
plates
are
homogeneous
from
the
resistance
point
of
view
and
that
they
do
not
present
any resistance
profile
in
their
widths
or
in
their
lengths.
To
do
our
Eric
hsen-type cupping
test,
we
will
do
that
on
the
base
material.
This
is
what
is
mentioned,
what
is
highlighted
here
in
the
three
first
pictures.
We
do
not
have
any weld.
We're
just
performing
this
Erichsen-
type
cupping
test.
For
simplicity
reason
I
will
call
this
procedure
ball
test
in the
remaining
part
of
the
presentation.
These
three
ball
tests,
we
do
them
on three
different
positions
of
the
material,
one
located
at
the
center
of
our sample,
one
located
on
what
we
call
the
drive
side
on
the
machine,
and one located
on
the
operator
side
of
the
machine.
For
one
sample,
we
do
free
tests.
What
is
a
ball
test?
In
fact,
this
is
explained
in
the
pictures
located
at
the
bottom
of
this
slide.
We
simply
take
a
bowl
made
in
titanium,
and
we
will
press
it from the bottom
and
we
will
register
the
deformation
of
the
material,
and
we
will
register
the
breakage
force.
We
do
that
for
our
two plates,
and
we
register
all
of
that
in
JMP
and
analyze
the
results
in
JMP.
We
will
use
the
distribution
platform
and
the
Fit
X
by
Y
platform.
This
is
what
I'm
going
to
show
you
right
now.
I
go
back
to
my
JMP
journal.
This
is
our
second
step,
analyzes
the
base
material.
I
will
open
my
file.
Here,
I
put
all
my
data.
My
plate
ID,
plate
number
1
and
2.
For
each
plate
I
do
that
twice.
For
each
sample,
we
perform
the
measurements
at
three different locations,
and
here
are
the
recorded
value,
the
recorded
strength.
We
will
analyze
all
of
that
and I store
everything
into
a
dashboard.
First
of
all,
this
is
interesting
to
see
visually
our
result.
I
will
focus,
first
of
all,
on
this
custom
map
shape.
In
blue,
you
have
the
data
for
the
first plate,
where
I
have
my
first
sample
and
second
sample.
For
each
sample,
I
have
my
free
ball
test,
one
located
on
the
operator
side,
one
located
in
the
center,
and one located
here
on
the
drive
side.
We
have
the
same
for the second piece
of
material.
What
we
can
see
here
is
that
the
resistance
goes
through
the
following
rates,
so from
nine
to
10.4
tons.
Nothing
really
particular
to
see,
except
maybe
that
here
in
the
operator
side,
we
can
see
that
we
have
on
the
same
side
the
external
value.
Here
is
the
lowest
value
and
here
is
the
maximum
value,
so
maybe
that
will
be
something
to
look
at
but we will
come
to
this
a
little
bit
later.
Our
target
is
to
perform
an
ANOVA
in
order
to see
if
our
two
plates
can
be
considered
as
comparable.
But
before
doing
an
ANOVA,
we need
to
ensure
that our data
follow
a
normal
distribution
and
that
our
variances
can
be
considered
as
equal,
so
this
is
what
we
are
going to do right now.
Here
are
the
distribution
for
plate
number
1,
for
plate
number
2.
Okay,
I
know
that
I
do
not
have
a
lot
of
data,
but
we
will
consider
that
we
have
enough
to
perform
the
test.
We
will
look
at
the
two
Anderson-Darling
coefficients
here.
What
the
p-value
tells
us
is
that
we cannot reject
the
hypothesis
that
the
two
plates
can
be
comparable
so
that's
good.
Then
concerning
the
variances,
so here we use another
platform,
but
we
first
go
at
the
end,
we
perform
the
variance
analysis
here,
and
we
will
look
at
the
F Test,
and
the
F Test
tell
us
that
we
can
consider as our variance
are
equal.
As
our
data
are
normally
distributed
on
our
variant
article,
we
can
apply
safely
our ANOVA.
This
is
what
is
mentioned
here.
On
the
top
part,
you
have
the
drawing.
Here
are
the
associated
data.
I
will
not
focus
on
the
data.
We
will
just
have
a
look
at
the
pictures.
Here,
what
we
can
see,
this
is
the
extremities
of
the
two
diamonds
overlap.
We
cannot
reject
the
hypothesis
that
our
two
plates
are
different,
so
this
is
good.
We
will
now
reach
the
conclusion
that
our
two
plates
are
equivalent.
We
can
now
aggregate
all
the
data.
This
is
what
is
done
here
in
the
distribution.
We
put
all
our
data
together,
and
finally,
we
have
a
global
resistance
of
our
plate
of
9.76 tons
plus
or
minus
0.17
at
two
standard
deviations.
This
is
the
first
point
and
we
will use
this
information
a
little
bit
later.
Another
interesting
thing,
so
this
is
what
is
mentioned
here.
We
can
also
perform
the ANOVA
taking
into
account
the
position.
And this as
we
have
previously
observed,
we
can
see
that
the
run
variation
on
the
operator
side
is
a
little
bit higher
compared
to
the
drive
side
and
the
center
of
our
plate.
We
want
to
understand
a
little
bit
why
such
things
happen.
To
do
that,
I
will
come
back
to
my
presentation,
we
will
have
a
look
at
the
plate.
We
are
studying
at
the
moment.
Here
is
the
appearance
of
our
sheet
metal.
What
we
can
see
is that
on
the
drive
side
here,
our
plate
is
nearly
flat,
I
would
say.
But
on
the
contrary,
on
the
operator
side,
we
clearly
see
that
the
plates
have
some
waves.
I
do
not
know
exactly
what
is the history
of
this
material,
but we can clearly
imagine
that
there
was
a
trouble
at
the
rolling
m
ill
or the
plate
leveller
and that
the
higher
force
has
been
applied
on
the
operator
side,
leading
to
this
kind
of
periodic
modification of
the
resistance.
This
is
a
new
constraint
because we
have
to
take
into
account
this
new
information
in our design
of
experiment.
I
will
sum
up
all
the
information
we
have
before
building
our
design
of
experiment.
First
of
all,
I
remind
you
that we have
a
very
irregular
study
area.
This
is
a
black
area
that
is
mentioned
here
in
the
drawing.
The
traditional
way
to
deal
with
such
things
in
JMP
would be
to
fill
up
a
linear
constraint.
But
here
due
to
the
shape
of
this
area, it's a little bit difficult.
Instead,
we
prefer
to
use
the
Candidate Points technique,
which
is
called
also
Covariates
engine.
I
remind
you
also
that
we
have
an inhomogeneous plate
and
to
deal
with
this
phenomenon,
we
will
have
to
introduce
a
few
parameters
into
our
design
of
experiment.
First
of
all,
we
have
to
take
into
account
the
strength
variation
in
the
width.
To
do
that,
we
will
introduce
a
categor
ial
parameter,
a
3 levels-
categorial
parameters,
and
the
three
levels
are
drive
side,
center,
and
operator
sides.
In
order
to
deal
with
the
periodic
variation
of
the
resistance
along
the
length
of
the
plate,
well, this is a little bit difficult,
because,
in
fact,
we
do
not
control
this
parameter.
We
have
to
be on
this variation.
For
that,
we
will
introduce
the
weld
position
from
the
head
of
the
plate,
or in
millimeter,
and
we
will
introduce
it
as an
uncontrolled
parameter.
Finally,
this
is
not
finished.
This
is
what
you
can
realize,
what
you can see
in
the
last
picture
at
the
bottom.
This
is
typically
here
a
picture of a weld,
where
are
located
above
three
ball
tests.
Well,
these
ball
tests
are
not
independent.
They
belong
to
the
same
treatment.
They
belong
to
the
same
weld.
They
are
at
the
same
weld
position.
We
are
in
the
presence
of
split/
plot
design,
where
we
have
hard and
easy-
to-
change
parameter.
This
is
a
lot
of
constraints
we
have
to
take
into
account.
Now,
I
will
show
you
how
to
do
that
with
JMP.
I
can
go
back
to
my
JMP
journal,
so
this
is
our
first
step.
But
I
will
show
you
from
the
beginning
and
I
will
go
back
here
to
this
step.
I
come
back
to
the
file
I
had
previously.
I
will
select
here
all
the
rows
with
a
Good Weld.
I
will
also
select
another
power
column,
my
welding speed
column.
In
the
table
here,
I
will
extract
a
subset
of
this
table.
I
will
extract
selected
line.
I
will
extract
the
selected
column
here.
Okay, I
will
build
my
subset
of
Candidate Points.
But
here
is
the
tricky
thing because, in fact,
as
we
have
a
design
also,
I need to
tell
them
that
you
will
have
the
possibility
to
select
three
times
each
point.
I
will
multiply
by
three
this
number
of
points.
There
is
probably
a
lot
of
way
to
do
that.
In
my
case,
I
will
just
create
three
columns,
one
call
it
drive
side,
one
call
it center,
one call it operator side,
and
I
will
just stack all these
using
that
three
columns.
Here
I
have
created
my
set
of
Candidate Points.
With
the
Graph Builder,
I will check
that
everything
is
okay.
In
the
Y-
axis,
I
will
put
the
laser power.
On
the
X-
axis,
I
will
put
the
laser
speed.
Here,
we
can
recognize
the
shape
of
our
irregular
study
area.
I
will
also
put
here
the
label
into
the
color
sections.
Each
of
my
points
has been
multiplied
by
three.
This
is
exactly
what
I
wanted.
I
can
use
this
as
a
starting
point.
This
is
a
set
of
my
Candidate Points.
I
could
have
additional
points.
I could have
done
a
little
bit
all
of
that.
I
could
have
added
some
points
here,
for
instance,
and
so
on.
But
to
be
honest,
the
discretization steps
are
fine
for
me.
I
will
keep
all
the
points
like
that.
I
will
open
now
my
design
of
experiment
menu,
go
to
my
Custom
Design
platform.
In
the
response,
I
want
to
measure
the strength
of
the
material.
In
the
Factors,
I
will
add
my first parameter,
Laser power and welding speed as Covariate.
I select Covariate, I select laser power and Welding parameter.
Automatically, JMP
fills
up
the
lowest
and highest value
for
these
two
parameter.
I
will
add
also
my
3-level
categorial
parameter.
I
call
it
side,
and
my
level
drive
side,
center,
and
operator
side.
I
do
not
forget
also
to
add
my
uncontrolled
parameter.
This
is
the
position
of
the
weld.
This
is
an
uncontrolled
parameter.
I
do
not
know
the
limits,
so
I
put
nothing
in
these
boxes.
I
also
do
not
forget
to
change
here
in
order
to
take
it
into
account
the
speed/plot
feature.
I
mentioned
here
that
my
laser power
and
welding speed are hard-to-change
compared
to
the
side.
Everything
is
correct
here,
so
we
can
run.
Concerning
the
constraints,
I
do
not
need
this
area,
because
I
already
took
into
account
my
constraints
when
selected
my
covariate,
so I
do
not
need
here.
Concerning
the
model,
I
immediately
choose
RSM.
But
in
that
RSM,
I
will
suppress
the
interaction
of
the
laser power
with
the
position
and
the
interaction
of
the
position
with
the
welding speed
because
there
is
clearly
no
interaction
at
all.
I
will
immediately
click
on
the
Make Design
button
because
it
will
take
some
moment.
Here
we
can
see
that
JMP
proposed
me
to
perform
eight
tests
with
24
measurements.
This
is
fine
for
me.
I
keep
this
default
parameter.
I
make
my
table
here.
Okay.
This is the defect concerning to laser power, welding speed, side.
I
have
the
column
,
I
will
record
the
position
of
my
welding speed,
where
I
will
record
the
strength,
so
everything
is
okay.
I
will
never
visualize
the
point
into
the
Graph Builder.
I
put,
once
again, the laser power
in the Y-
axis,
the welding speed
will
in
the
X
-axis.
Perfect, and we read curve.
I
will
also
put
the
side
here
into
the
Color
area.
I
will
add
some
details.
Okay,
here
we
can
see
that
this
is the point
that
has
been
selected
by
JMP
within
the
framework
of
our
Candidate
set.
For
each
point,
I
have
to
perform
three measurements,
on
drive
side,
center,
and
operator
side,
and
JMP
wants
me
to
perform
this
test
twice.
Okay
, so
this
is
what
we
have
done.
I
will
now
show
you
the
result.
I
go
back
to
my
JMP
journal
here.
This
is
our
last
step,
the
result
analysis.
I
will
open
the
associated
table.
Here,
this
is
the
same
table
as
previously.
I
have
recorded
the
positions.
I
have
recorded
the
strength
in
absolute
value
in
terms.
I
have
inserted
two
columns
here.
This
is
our
reference
strength,
the
strength
of
our
base
material
that
we
have
previously
determined.
This
is 9.76
tons,
because,
in
fact,
I
will
use
it
in
order
to
create
this
extra
column,
this
is
the
strength,
but
in
percent
compared
to
the
base
material.
This
is
on
this
last
column.
This
is
the
first
column
that
we
will
take
into
account
in
our
optimizations.
I
store
my
analysis
into each column
here.
Here
are
the
results.
As
a
reminder,
I
put
on
the
right
part.
the
irregular
area
as
a
reminder.
Here
are
the
experimental points
that
we
have
performed.
I
have
colored
the
point
using
the
strengths in
percent.
I
use
the
Fit
Model
platform
in
order
to
create
my
model.
Step
by
step,
I
have
suppressed
the non-significant
interaction
of
our
parameter
using
the
p-values
here.
Finally,
I
have
a
model
with
an
explicative
power
of
two
of
96 percent,
which
is
quite
good
because,
in
fact,
it means
that
only
four percent
of
the
wall
variation
escape
to
our
prediction
power.
Concerning
the
collinearities
, if I look to our VIF,
our
variance
inflation
factor,
all
of
them
are
below
three.
We
can
be
now
confident
in
our
model
and
we
can
use
it
in
prediction.
We
can
go
to
the
prediction
profiler
here.
First
of
all,
I
will
focus
on
this
part,
on
the
interaction
of
the
position
with
the
side.
What
you
can
see
here,
if
I
move
the
position
of
the
weld,
it
seems
that
the
resistance
is
not
sensitive
to
the
positions
on the drive
side
and
center.
But
on
the
contrary,
on
the
operator
side,
this
one
is
clearly
influenced
by
the
positions.
This
is
exactly
what
we
have
seen
in
our
plate.
This
is
a
modeling
of
our
waves.
Here
we
do
not
see
this
kind
of
shape
because
this
can
be
easily
explained.
Our
weld
are
not
so
huge.
It's
only
a
sample
of
six
centimeters.
We
do
not
consume
a
lot
of
material.
We
do
not
go
along the wall
with
that
shape.
We
go
from
the
bottom
to
the
top
only.
We
are
quite
happy
because
we
were
able
to
analyze
and
to
model correctly
this
behavior.
This
is
interesting
because
we
can
now
have
access
to
the
pure
effect
of
the
laser power
and
welding speed.
This
is
what
is
mentioned
here.
For
this
particular
material,
we
can
conclude
that
we
can
increase
the
resistance
of
this
material
by
decreasing
the
laser power
or by
increasing
the
welding speed.
Now
what
we
have
to
do
this is to
determine
an
optimal
point
using
all
the
information
we
have.
To
do
that,
we
prefer
to
do it
using
the
control
profiler.
This
is
what
I
have
mentioned
here.
In
this
control
profiler,
once
again,
I put
in
the
X-
axis,
the
welding speed,
in
the
Y-
axis,
I
have
the
laser power.
I
have
reproduced
my
area,
my
irregular
area
where
I
am
defect-
free.
To
do
that,
I
have
simply
implement
a
script.
Here is a list
of
points,
and I just
asked
JMP
to use this point
and
to
draw
a
polygon.
I
have
my
black
area
where
I
am
defect-
free.
On
that
drawing,
I
have
also
inserted
the
ISO
resistance
curve.
Here
in
red,
you
can
see
the
values
of
this
ISO resistance.
We
can
see
that,
here,
we
go
from
50 percent
to
nearly
100 percent.
Before
doing
the
optimization,
I
will
add
another
constraint.
It's
not
enough.
From
the
productivity
point
of
view,
we want,
of
course,
to
go
as
fast
as possible
and
we
want
to
have
the
highest
welding speed.
Using
all
that
information,
we
have
selected
the
point
at
six
kilowatts
and
11
meters
per
minute.
This
is
the
point
that
has
been
located
here.
Why?
Because
this
point
is
located
in the black area,
where
the
w
eld seam
is
free
of
defect.
We
can
see
using
our
model
that we
expect
this
point
to
have
at
least a resistance of 90 percent
of
the
resistance
of
the
best
material.
What
is
interesting
also
is
at
this
point
we
have
enough
safety
merging
around
this
point.
Of
course,
we
have
tested
this point
and
this
is
the
result.
But
I'm
going
to
show
you
right
now.
I
go
back
to
my
presentation,
which
is
located
here.
This
is
the
result
of
the optimal
point
we
are
choosing.
First
of
all,
on
this
slide,
you
can
see
on
the
left
part,
the
upper
w
eld bead pictures,
on
the
right
side,
the
lower
weld bead pictures.
You
can
see
that
the
weld seam
are
free
of
defect.
We
have
no
patterns,
we
have
no
droplet,
no
chain
of
pearl,
no
holes,
et
cetera.
This
is
what
we
wanted.
From
a
resistance
point
of
view,
first
of
all,
if
we
focus
on
the
pictures,
we
can
see
that
this
is
the
material
that breaks
and
this
is
not
the
weld seam that
opens,
so
this
is
the
first
good
point.
Concerning
the
resistance,
we
can see
that
each
of
them
are higher than 90 percent.
This
is
exactly
what
we
wanted,
let's
say
that
we
have
achieved
our
target.
This
is
the
end
of
this
presentation.
This
is
now
time
to
conclude.
First
of
all,
I
go
back
to
my
JMP
journal,
I
would
like
to
mention
that
with
this
presentation,
you
have
the
possibility
to
download
an
article
that
will
be
located
on
the
website.
Here
you
have
a
full
article
explaining
all
the
case
studies.
I
have
added
additional
material.
If
you
are
interested
in
knowing
more,
please
feel
free
to
download
it.
A
s
a
conclusion,
I
would
like to mention
that
if
some
of
you
are
interested
in
knowing
more
about
covariates,
I
would
like
to
mention
two
available
sources.
The
first
one
is
an
article
that
you
can
find
on
the JMP user
community
entitled
"What
is
a covariate in
design
of
experiments?"
Also
from
the
same
offer,
you
have
a
webinar
entitled
"H
andling Covariates E
ffectively
when
Designing
Experiments."
To
conclude,
I
put
here a quote from Mark Twain
that
humorously
tells
us
that
"Facts
are
stubborn
things,
but
statistics
are
pliable"
Inspired
by
Mark
Twain,
I
would
like
to
say
that
facts
are
certainly
stubborn
things,
meaning
complex, but don't panic
because,
in
fact,
JMP
can easily adapt
to
the
reality
of
the
field.
In
my
case,
he
was
able
to
adapt
to a very irregular study
area
and
also
to
a
very
imperfect
study
material.
This
is
the
end
of
my
presentation.
I
will
now
answer
your
question,
so
please
feel
free
to
ask.
If
we
run
out
of
time,
I
mentioned here
my
contact
information,
so
please
feel
free to contact me,
and
so
I'm
waiting
now
for
your
question.
Thanks
a
lot.
Bye bye.
![Figure 3 – Base material strength analysis The dashboard is composed of various JMP platforms: Graph Builder, Distributions and ANOVA. The custom map shape[1] of the Graph Builder displays the two samples corresponding to each of the two plates and the position of the various cupping tests colored by strength. In the ANOVA, the overlap of the two diamond tips demonstrates that the plate can be considered as identical. The chart on the right shows that the strength variance is higher on operator side (OS). Once aggregated, data from the bottom distribution presents an average strength is 9.76±0.18 tons (at 2σ).](https://us.v-cdn.net/6038925/uploads/images/43960i481DEB136F7CBAA4/image.png "image.png")
