CIMSA 2003 -
In
t e r n
Co
In
t e l l i g
L
m
u
p
g
u
t a t i o
a n
o
,
n
a l
Sw
i t z e r l a n
a t i o n
d
e n
,
a l
c e
29
Sy m
f o
- 31
r
p
o
s i u
Me a s u
J u
l y
m
o
n
r e m
e n
t
Sy s t e m
s
a n
d
Ap
Composite T
ec h
Cesare A
n
l i p
p
iq
1
i
u
,
es f or
G
i u
sep
p
3
i s
m
be co
an
u
ach
w
co
i n
m
p
i ts p
w
o
i n
au
l i cati o
an
si ti v e
m
g
i m
re
d
to
d
m
p
Early
i ti o
act o
n
th
al
ap
is
impact on q
b
u
h
co
al
f
e
m
re d
p
ro
IN
T
o
e
u
a
'A
l ity
n
c en
g
A
ap
e
n
i q
an
e r
q
ech
P
i u
u
g
al i ty
g
ri
e s h
co
m
p
sp
co
p
tati o
n
al
o
n
n
ce
o
e
a
so
n
i n
se d
co
niv
l i cati o
th
e cti o
m
as be e n
u
p
n
3
l y
sis in
at t eo
an
d
F
iat, O
ersity
rb
so
m
req
f t-
C
T
IO
in
economical
metal
manu
factu
issu
e artifact and th
e
e indu
i o
P
asq
u
el d
in
g
2
et t az
iaz z a L
.
da V
inci 3
2
, M
ilano, Italy
) , Italy
ramante 6
detected
tational
b
u
5
,
C
rema ( C
th
defects)
rden
in
R
) , Italy ,
e performance issu
b
u
t
also
order
to
th
e
e ( in
req
satisfy
u
ired
real- time
irements.
particu
to
F
)
ou
t
7
] .
[ 1
lar,
th
e
th
e
indu
laser
at
T
e g
th
h
e
w
strial
elding
lab
process
of
ox
au
oratories
e specific test b
earb
ring
for
rg
e addressed not solely
part in th
a
sis of th
R
i o
W
3
t t i
of
carried
N
defects
main
O
G
ser
tion w
u
( C
DU
c o
,
a
ia B
refers
are d
1
c c i
e L
ilan, V
In
r
tomotiv
ilano P
terms
e s.
O
S
compu
f o
p
i o
solu
n
u
at t eu
ab
of M
l ase r
f
M
A
assano ( T
s
ti o
e re d
co
M
a
ste e l
l u
o
si d
,
n
olitecnico di M
e F
ies, U
i n
e sts
i n
sy ste m
u
ap
al y si s
su
icerch
nolog
2
el o
z o
entro R
l ase r- base d
al i ty
si te
ce d
R
of
analy
u
u
p
p
ach
th
q
e
te ch
ecoming
ality
o
r
re al - ti m
n
i ti o
g
f o
T
A
detection
stries
ri n
e
ate d
s.
I.
indu
i to
stry .
m
n
n
n
i ssu
u
trad
trad
o
ai n
p
g
e
m
an
ap
ti n
a
ri n
g
g
u
o
i th
g
f actu
Q
i n
C
Department of Information T
e al - ti m
i n
i e v i n
e l d
R
m
s
Department of Electronics and Information, P
2
Abstract –
l i c a t i o n
e D
V
1
p
2003
u
nder
tomotiv
of
C
e
entro
R
icerch
ed is a steel g
for a passeng
er v
eh
monitoring
components
e
F
iat
ear, a critical
icle ( see F
ig
u
re 1
) .
A
its
B
Z
strial
process.
A
g
u
tomated
row
th
and
process th
speed
produ
w
ith
a
process.
M
u
ch
y
prov
env
du
th
e
isag
u
th
su
b
seq
q
sefu
e
can
eat
inpu
t
[ 1
] .
L
asers
ig
h
h
t
reliab
may
b
focu
u
ality
analy sis
of
th
Fig.
S
tomated
sed
w
l feedb
process
ack
to th
itself
to
v
sy
ery
process
activ
pert u
. ,
b
y
ity
th
ser on h
tu
ning
ternal inspection of th
dev
serv
ith
th
u
e
ality
A
w
e process [ 4
th
e
th
ent process tu
composite
sy
alg
stem,
compu
tech
stem
th
e
and
ning
ms
u
F
u
different
es
ollow
.
T
at
M
for
[ 5
] .
stu
h
ing
ality
eng
dy
it
a
sy nerg
Du
ality
ring
er,
ag
u
nal
defects
th
ineer
ing
analy
ic
e
to
dev
su
g
h
5
T
e
h
]
U
e
ge
a
r
c
o
n
s
id
e
4
on th
r e
d
in
o
u
r
w
o
r k
.
stru
res ex
e ex
u
ilt j oining
e
principal
O
2
laser.
factory
S
y
T
h
ropean U
e dev
g
tw
e
actu
ed
is
proj ect
aser
s ( i. e. , a lig
are
test- b
stems
L
o ring
ear) ,
M
S
L
elopment of intellig
b
part
of
P
( S
A
aterial
nion effort to adv
ally
P
S
h
u
t
ttth
e
elf-
rocessing
ance th
e applied
ent laser processing
] .
ctu
re
of
th
e
eneral b
paper
ack
g
ed th
rou
is
as
e ov
follow
r indu
S
erall composite alg
e tu
ection
2
and a
presents th
e
p and in section
orith
ning
S
elding
ection 3
strial set- u
e classifier selection, th
perimental resu
s.
nd on laser w
e ty pical defects.
tracted from ou
is describ
th
y
ear, b
th
ser- independent
a Eu
ces a g
also th
C
anu
description of th
II.
g
L
A
S
ER
W
EL
A
ested
th
e
for
a
m describ
meth
odolog
IA
S
y
ing
and
lts.
T
composition
and
of
of
softth
e
h
e
w
DIN
T
elding
C
ph
G
R
A
F
L
N
A
D IN
B
O
enomenon
R
DU
A
T
S
O
can
T
R
R
L
ET
- U
h
eat and/ or pressu
small areas;
permit th
e w
th
re.
L
aser lig
is remark
elding
ab
h
t may
b
e
defined
b
e focu
r.
as
th
e
ced b
sed to v
le concentration of pow
process to occu
P
IES
localiz ed coalescence of metals or non- metals produ
stem is a
elopment
6
a
M
and
nit [ 1
featu
directly
effects
processing
T
a self-
assess
sis sy
.
and
sing
[ 1
introdu
enomena
ilosoph
u
ning
elded
ith
ear
ent
nits)
u
approach
is ph
g
research
ow
tracted from
sis w
detects
e q
sig
th
analy
oreov
process
namely
orith
niq
q
sy
process
traditional
] .
elding
process.
les
A
tu
sis implemented directly
some critical parameters/ ph
elding
of
analy
set of information is ex
ing
e laser w
enab
[ 3
1
en ty pes of g
elded
U
at
some
e w
ev
ncro
Intellig
is
elded component.
ming
e ex
( e. g
e
X
eres, and
le and au
e
Y
h
re, most of times carried
re
ting
h
a h
lig
ersatile
ig
ase is a time- consu
classification
u
v
metals or non- metals at h
] .
ning
ality
increasing
and
ices
tu
u
l
procedu
e address th
q
e
e process.
th
an
erfu
ires a costly
associated w
ring
ing
ltrasonic
w
procedu
av
pow
m, controlled atmosph
ith
laser
e an on- line q
ring
u
In addition, ex
u
ith
low
acu
h
e
w
th
th
e sensors ob
du
er,
ides u
artifact req
w
th
offline inspection of each
parameters) .
t
is
to
] .
eneral,
improv
ou
ely
ers w
inspection ph
rarely
to
relativ
oreov
assessed b
S
e
s for j oining
amb
small areas [ 2
g
elding
du
elds in air, v
riz ed ch
In
w
sion
at allow
ce w
pressu
laser
diffu
y
ery
er can
Since
a
radiation,
laser
the
beam
ref lectiv
is
e
a
beam
composed
properties
of
partially ref lect the light and a big part of
pow
er can bou
nce back
.
T
light, w
L
iq
u
ch more pow
he
v
point
metal
v
apor.
apor opens a deep and narrow
( depth/ w
bu
idth)
threshold
pow
ork
eyhole laser w
process:
a
k
eyhole,
piece.
w
hen
O
w
ill
of
pow
cau
the
produ
se
K
is
low
v
ery
er
is
able
absorbed
near
the
changes
in
k
t becau
conf igu
hav
e
the
e hav
f ew
eyhole
−
not
se all the metal in the w
j oint
A
ny
E
v
in
the
en if
u
beam can pass throu
ithou
t w
elded mu
ej ected f rom the k
lens damage as w
ou
r
A
) .
R
eld
togenou
s:
laser
w
T
e
are
−
penetration depth,
−
misalignment of
−
porosity ( spontaneou
pow
−
T
aser P
er lack
ow
( t=
the R
T
he ex
of in Sinar D
0
pling in mou
s and cau
er lev
ms)
in
detecting
rce.
T
res ex
e are interested
er new
samples, bu
ing this resu
lt du
t
e to
ne the classif ier,
classif ied
f or
by
the
it the process) ,
the
dif f erent
error
ned
to
w
ecu
e
solv
consider
e
a
an
specif ic
ad-hoc
class
of
s to simplif y the system
tion of
iou
the classif iers.
s section w
f ramew
ork
e are look
ing
f or choosing of
the
ith
correctly
k
a
now
self -tu
ning
process
process
of
su
itable
ledge, and then au
classif ier u
sing self -tu
w
can
f ew
be
f eatu
ef f ectiv
res,
by
ely
classif ied
solv
ed
of
ex
mean
by
pert
tomatically selecting the optimal
ning techniq
u
es.
I I I .
C
el ( -1
0
in laser sou
%
)
of
laser sou
rce,
rce.
elding process are:
e been carried ou
laser sou
rce [ 1
8
t by u
u
er lack
onv
D
F
E
C
T
the
T
re redu
ction.
ersely,
u
the
F
E
detection
A
T
U
of
R
E
the
E
X
T
pow
R
A
er
C
f rom
the
I O
N
decrease
en f rom the grid cu
or
rrent on the
f f icient inf ormation to solv
signal
T
e that task
photodiode
is
u
sed
.
to
the dataset
in
strial env
the
ironment, high f req
signals
coming
f rom
u
ency noise
the
process;
to
e did a spectral
-pass f ilter.
A
been
done
to
f eatu
re
bseq
redu
low
tremely
e to the indu
analysis in order to select the correct low
ality
t by
u
u
ce
ent
the
dow
n-sampling
dataset
in
operation
order
to
has
speed
u
p
the
traction and classif ication.
igu
re 2
show
s the signals af ter being processed by the
-pass f iltering.
ex
tracted f eatu
du
ration ( T
order
ce a compression of
D
su
ex
he f irst is
I t is ex
N
s, the signal tak
present
F
e the f inal q
S A
separate the correct signal f rom the noise w
sing
es f rom signals.
eatu
important since it can produ
E
oncerning
pow
] .
traction phase and can be carried ou
he second phase is F
D
classif y penetration, misalignment and porosity f ormation.
and I nf rared radiation f rom the
applying traditional techniq
T
racy in
tational load.
ity and parameters,
both
ypical
nted samples,
he methodology presented to achiev
eatu
e
complex
sed by misalignment
ring the w
rrent)
5
w
n.
problems
tu
tomated
laser possesses su
ired du
3
ming
phase
optimal classif ier, in terms of
cing spatter, porosity, and
interested
periments hav
C
samples
pport a parallel ex
set
the
sensor
e a good accu
correctly
e stated also in the prev
a
er, the material being
of
consu
ith linear and non-
nded the compu
his choice allow
f or a general au
C
cou
0
s w
def ining
nces of f
time
classif ication
ailable to tu
now
these
T
mm gap
classif ier can be decomposed by three phases.
the F
A
is
1
u
er ( grid cu
T
and to su
the
) ,
he signals acq
process.
elding errors.
his
lts in the laser sou
decrease in laser pow
−
w
T
st bou
a
classif ier w
are
of
nk
specif ically
samples.
oreov
tion
ercome
af ter
. 2
ring the
ny non-metallic contaminants get
elding and f au
er lack
ov
the
eyhole, produ
w
o
classif ier
sed to
def ects can be classif ied as
or pow
T
he
ell.
process
distribu
sed
ith a 0
samples
t
lties in achiev
samples
typologies is u
elding.
ts
all
in
M
ndercu
alidation
ses:
ts are acceptable, a f ocu
tt j oint w
u
the
eeping bou
samples are av
the
to
no f iller metal is
become
the
ithou
operator ( there is an intrinsic error w
heat
egardless of
in
the beam j u
t the other side.
ires low
eld is being u
re 1
critical
j oint
gh a bu
st be clean.
def ects in w
is
ndercu
elding it at all;
alls and ou
I n
p
elds are au
gaps
eld.
f it-u
igu
u
elding.
in
ind of
e some dif f icu
−
property
ration f or laser w
s f or high speed and req
lmost all laser w
sed.
appreciated
on
w
racy and generaliz ation ov
dif f erent cau
elds, f or that reason it is common
tt j oint conf igu
ration,
f inished w
L
w
the
w
directly
domain
transf ormation;
in are accu
gh to
the
raining and V
I n this phase, the main characteristics w
: 1
little
by
ing
time
classif ication by k
elding is a
,
reation, T
linear components in order to achiev
nd the laser
0
ork
the
considered dif f erent k
this
he aspect ratio
eyhole w
changes
remark
machines
ration allow
in inpu
w
.
pow
hold the assembly together ( F
w
: 1
irradiant
the
er
ce thin and deep w
conf igu
w
of
w
in
spectral
−
asers
to select the bu
u
T
signals
ality.
L
A
eyhole.
re
elds can be as high as 1
nd 4
processing,
ddenly
nce the irradiant is high enou
most
Small
threshold
u
k
er is absorbed.
f orm
w
of
pressu
he third phase is the C
the classif iers.
I n order to satisf y the real-time constrains, du
e the boiling
channel arou
hat is called a k
t is more commonly arou
T
the classif ication
designing phases it is pref erable to select f ast and parallel
rf ace melts.
absorbed su
re abov
beam, f orming w
q
x
increases, raising the metal' s temperatu
generating
of
er f rom the incoming
radiation than solids do, so the heat f lu
and
T
nded by
isible light.
hen the su
classif iers maintaining
racy of
system.
the incoming
en better than v
ation drastically changes w
id metals absorb mu
satisf ied constrains on the accu
to
strial laser types emit inf rared
hich metals ref lect ev
his situ
and permit to obtain less complex
light
tend
his problem is compou
the f act that the maj or indu
T
by
metals
to
)
of
detect
greatest positiv
its
mean
F
rom the pow
er signal ( F
igu
re 2
. a)
w
e
res ref erring to the mean intensity ( A) , and
the u
sef u
lack
in
l part of
the
e and negativ
( respectiv
ely
F
1
pow
the signal.
er
sou
rce
e dev
iation of
and
F
2
in
M
w
oreov
e
er, in
detect
the
the signal f rom
the
f igu
re)
then
synthesized in the m
w
el ding
F
r o
a x
im
u
m
p
o
w
er
m
the
f il ter ing
,
w
o
e
r ig
ex
ina l
w
el ding
tr a c ted
f ea tu
sig
r es
a r ia nc e;
these index
p
th. A
l o
enetr a tio
n dep
r ef er enc e
o
r der
sig
to
isa l ig
dev
ia tio
m
u
ex
u
u
ig
p
o
r o
l itu
de ( A
the
u
, 3
p
int
e
T
he
c o
tr a c tio
c o
m
c h a
p
u
e
o
u
m
o
m
n
p
l ex
ha s
l ing
m
r ea l - tim
.b
n du
b
r ing
nm
the
f
v
iew
,
o
f
een
e
na l l y c o
p
m
the
k
ep
t
er f o
p
e b
o
f
u
p
- p
I V
o
r o
f
l o
na l
m
del
ig
u
r e 2
the
o
.b
etw
o
r ithm
ssib
l e to
s
f o
l o
a nc es,
no
so
r
.
T
in
no
a na l ysis ha s b
sp
o
H
E
m
p
o
is p
su
y a
p
o
A
L
G
O
R
w
r es
to
o
f
r m
b
r
u
l o
I T
H
M
o
F
m
is
ht
o
du
ntr o
ed o
a s
b
l o
l
l o
g
p
l ex
m
u
F
T
T
2
c k
s
w
sa m
T
1
A
A
A
1
u
o
w
e
r
( a
)
F
w
a
e
n
l d
d
w
in
e
g w
l d
in
it h
g ( b
s
e
l e
)
c
s
ign
a
l s
t e
d
f e
a
Fmax
1
F
F
in
t u
a
r e
c
ex
l e
w
is g
a
el ding
so
e im
p
f
r
enetr a tio
ther , m
their
nted
p
e the c o
sed
ing
, f o
in
no
−
the l o
w
−
the L
a ser
( N
- p
m
r
c o
m
o
p
o
r o
u
c o
a
s
n dep
c essing
r ing
then
n
p
l ex
ity o
f ea tu
w
r es
ith
f
no
na l
o
ex
r esp
n–
p
o
w
r
d
e
c
r e
r s detec tio
w
e
a c tiv
l e p
s
f
c
tr a c tio
ec t
f t c o
m
p
c essing
n,
to
( a
)
a
n
d
1
0
m
u
ting
w
e
c a n
nu
m
b
u
the
p
r r e
the
the
N
o
c
o
w
e
r
b
r e
a ss f il ter ing
P
o
w
er
F
ha s c o
ea tu
r e E
m
x
p
l ex
ity O
tr a c tio
( N
) ,
n ha s c o
T
1
m
p
l ex
ity
2
2
1
t
2
A
Fig. 4
)
a
k
d
o
w
n
( b
)
f
na l :
H
p
f
a r t
r
se
er
1
s
a te
a nd f o
the
A
o
n
l e is
r esenc e o
Fmax
e
p
( a )
o
2
a
er y
H
T
e
y v
the sa m
ssib
so
r o
)
( b
p
ting
s
( a )
%
u
r ithm
th a r e c l a ssif ied
er r o
. I f
the p
( b
0
p
) ,
D
e
A
. 1
her e
o
ented b
D
Fig. 3
m
the a l g
l em
nting
r r ec tl y” ,
nito
ea c h sig
the
ta tio
l es in the sig
F
T
def ec ts.
r es a nd the
f t
a r t o
en in
ities w
tr a c t f ea tu
p
iv
hier a r c hic a l
a c tiv
r esent
tha t c a n b
n a nd p
l e f o
r ithm
2
( b
o
r ithm
n
2
( a )
. P
o
o
c essing
a ining
A
Fig. 2
ssib
r o
r ep
T
a x
o
p
l ied to
s
ea c h o
o
du
n a l g
a sed
ity) .
nl y a f ter
o
na l
p
c k
ic
b
n a s p
l es ( the r em
m
“ m
ent m
o
sig
es a r e a p
hl ig
n m
desc r ib
O
1
r ithm
sities.
l l o
ted in
o
c l a ssif ic a tio
the a l g
f o
u
a r e c o
er
the c l a ssif ic a tio
s r ef er s to
endentl y f r o
r o
ha se.
F
hig
l e c o
seq
T
o
r der
c k
tec hniq
er f o
b
c a n
es.
f ea tu
u
p
f
a l g
detec t a s so
l o
c l a ssif ied
ec tr a l
een c o
r a y b
r e o
he
a c h to
indep
) .
typ
the
w
T
ha r dw
P
f
a r e
r es
r
w
ns.
)
c tu
.
nded
l o
te, b
f ea tu
een the er r o
na l l y
.b
u
is sim
o
5
c l a ssif ic a tio
na l s r el a ted to
sel ec ted
n b
, 4
r o
a nd
ia tio
a nd H
r o
he g
del ;
r dina tes
( D
el y sig
)
i
r e
c l a ssic a l
a in
o
n ( T
u
p
T
in
he str u
ig
a p
a nd
o
these dev
o
F
c a l
sities
tw
o
T
ea n
c l a ssif y
sig
r a tio
c o
a ss
m
il d a
the
e du
m
it is p
a l g
r m
l ex
ic
intentio
sed to
een
ec tiv
tha t
ina tio
w
the r ef er enc e m
ent ( F
r esp
l o
the
r esent
e o
b
r e
to
to
etw
the c u
w
ef o
, w
n
p
m
a in f iv
in
r r ec t a r tif a c ts;
o
l a tio
a r e
isa l ig
sho
ity
b
o
tr a c t the tim
f
f f ic ient disc r im
ta tio
p
p
2
the m
b
es a r e u
r ef er r ing
r e
e ex
f
im
na l ,
intensity, f r o
a nd 4
a l ita tiv
su
u
f
o
a x
n- c o
it a
)
i
r r ec t a nd no
su
a tio
a ss f il ter ing
inter p
r es
ig
s o
- p
ic
f ea tu
F
w
dif f er enc e
a nd m
er m
b
sities w
er ,
r es 2
c u
I n
detec t c o
u
a c hiev
y
tr a c t
ent.
m
sed to
c o
q
v
inim
F
p
p
r eo
f ter
b
ns, in ter m
the a m
o
na l
ex
nm
in detec ting
M
c tu
r ef er r ing
intensity a nd v
m
f l u
( Fmax) .
m
is
a
. P
l ign
o
r o
m
)
s
e
it ie
n
t
s
e
( a
r r o
)
r s
a
n
( b
d
)
the
−
c o
O
O
w
n
a l i d
f a m
p
u
e to
l e ( N
a p
p
m
n
n
p
a tter n
a s s i g
n
b
ed
ei g
hb
o
el o
n
the
n
ei g
c a s e,
g
u
hb
n
k
o
s i ty
F
ea tu
r e
the r el ev
a l
E
x
m
p
E
l ex
x
i ty
n
tr a c ti o
a n
t f ea tu
tha t,
q
u
b
a l i ty
y
p
a n
tr a c ti o
O
3
( N
ha s
n
c o
ha s
i n
o
f o
c u
a n
c l a s s i f i er .
W
c l a s s i f i er s :
n
eu
r a l
c o
l e)
s
[ 9
] .
a
f
i ts
hb
ei g
hb
n
u
r
o
n
p
n
l y
w
n
d
ha s
o
es
f o
) ,
m
p
l ex
tha t c l a s s .
c o
ei g
n
o
p
n
o
the c l a s s i f i c a ti o
o
v
o
m
p
l ex
i ty
i s
to
a j o
the
g
h d
d
es i g
ei g
f ea tu
p
a ta
n
r o
c o
,
ti o
hb
the n
w
u
n
n
o
n
r es
r u
tw
l e
a n
i n
o
f
b
to
u
e
r ef u
a l s o
b
o
n
s i d
f
g
to
n
a n
g
u
n
k
o
o
- n
f
i ts
k
- n
ea r es t n
ei g
hb
i f
the
s ed
I n
k
- n
the m
f
n
a j o
u
ei g
ep
hb
o
a l u
p
l ex
n
o
r ed
ta ti o
u
n
c ti o
m
p
ei g
u
a l
W
n
l ex
hb
o
e o
l o
n
m
a n
o
m
u
d
t m
c a u
f
w
d
L
P
o
o
w
q
u
P
as
ly no
m
is
s
it io
F
,
n
etho
P
eat u
P
C
o
r o
las
o
r o
s
if ic
s
M
it y
x t r ac
t io
F
n
eu
r a l
i th
d
d
u
en
t a
las
o
u
s
l em
to
ti f i ed
L
F
as
F
it t ing
er
P
eat u
x
t
the m
E
x
t r ac
t io
n
L
n
o
w
w
L
L
as
er
las
P
enet r at io
s
if ic
at io
n
F
er
q
P
eat u
Signal
u
as
as
t r ac
t io
is
s
it io
F
er
P
r es
E
n
ilt er ing
o
w
er
x t r ac
t io
n
P
n
las
at io
n
o
s
w
if ic
er
at io
GOOD
GOOD
GOOD
NO GOOD
NO GOOD
GOOD
NO GOOD
GOOD
NO GOOD
Fig. 5. The Algorithm
g
o
n
n
f
b
er
i n
p
i th
s
i n
a n
d
c tu
eu
r o
o
f
u
a
the
the
T
he
r e;
f o
s ,
f o
n
s a m
ts
e
the
ea r ) .
p
i m
the p
r o
f ea tu
r es
i n
p
r
r
l es
a c ts
i n
r m
f
a ti o
k
f
U
s i n
e
i th
r i o
w
r i
the
s a ti s f a c to
w
a y
n
,
to
g
w
a n
a te the p
a n
d
er f o
K
e r ed
k
u
N
t f ea tu
u
i th
c e
u
c e the
- n
ea r es t
s i n
r m
r y
N
r e w
r ed
the
es
s t r el ev
w
c l a s s i c a l
a ti o
a l u
n
l o
ea c h f ea tu
thi s
e
b
a - p
eep
a
o
w
to
s e a n
to
g
s
c es s
the s el ec ted
f o
e u
a l
r m
v
o
n
n
b
r
w
n
s tr u
t a l l
W
I n
k
o
t o
.
f o
es ti m
nt ing
if ic
.
r t
enet r at io
r es
n
c e o
i n
s e.
c
f
s i x
ea n
a n
i r ed
]
o
t
ta i n
m
r k
i d
f
m
f
f o
o
i l i es .
c l a s s i f i er .
0
u
s ed
l i n
o
o
e o
u
u
a i n
b
er
c a n
r o
o
n
s
o
m
a ti o
n
eu
i n
b
er
g
l em
y
nt ing
r e E
M
C
n
r o
m
b
the
i f f er en
[ 1
C
it y
at io
o
eat u
e
b
u
o
b
m
p
A
C
s
r e E
r o
r m
u
thei r
te tha t n
e a m
t
n
P
-
F
o
s i g
the n
o
f o
etw
er
n
u
l ed
the r el ev
n
ilt er ing
ial
n
t
o
m
a b
p
the a c q
w
e i d
a l ,
n
u
w
the n
l a y
s
n
u
n
f
en
th f a m
o
etec t,
Welding Signal
c
n
o
n
t to
b
d
the
etho
e o
r
f
o
b
k
a c hi ev
to
o
he n
f
g
t
d
i n
i f i c a ti o
r a l
o
the n
r i ty
S
A
o
s i g
a d
etho
c y
i ty
ea c h er r o
T
i ty
to
e tr y
d
r
n
.
eu
n
u
en
n
the s a m
n
tp
d
c l a s s i f i er
n
ep
o
r a l
c ti o
u
o
m
ta n
o
d
s i c a l
r ta n
ta i n
n
l i c
s
k
eta i l ed
a r d
o
d
f
hy
p
eu
c l a s s i f i er ,
ea c h
i m
c o
r
e o
m
m
en
D
i ts
f u
the
the c l a s s i f i c a ti o
the c l a s s i f i er
d
i t d
the p
c a n
r w
n
er b
r
te.
d
he n
- f o
hy p
o
a n
T
a ti o
ec t the c l a s s i f i c a ti o
l ea v
ea r es t
the s ec o
i f
c o
n
s ,
I t i s
r a c y .
the r ed
r s
o
p
r k
f r o
.
] .
f o
u
l e
the c l a s s i f i er s
g
,
[ 9
n
q
r u
d
a
the c o
r i s ti c
m
f
i ti n
r es
r es p
n
n
n
l o
c l a s s i f i c a ti o
-
i s
o
o
p
r
f eed
c ti o
the v
ec t to
a c c u
ea r es t
the c l a s s i f i c a ti o
s ed
c o
a
i s
o
d
tr a c ted
heu
i s
ea r es t n
a n
x
a n
n
o
etw
i r ec tl y
r es p
n
w
er
i ty
n
- n
f ea tu
r
the
n
r a l
]
ec i f i c
o
( the a c ti v
f u
l ex
hb
n
n
c l a s s i f i ed
o
t the k
p
r ed
ex
the
er i n
u
m
s p
ei g
[ 8
l a y
a ti o
E
d
the c l a s s i f i er
e c l a s s .
e r ef u
i n
a c c o
a l l
k
r d
n
l a y er
en
eu
d
d
c l a s s i f i c a ti o
c o
t to
i ts
the s a m
f
to
c l a s s
ei ther
r i ty
c a n
o
e i n
p
f
d
s to
o
hb
en
the k
hi c h the c l a s s i f i er
s tea d
ta k
o
ei g
t r ea c h a
the i n
a c ti v
l em
n
d
d
c o
e a n
o
r
b
o
ea r es t
n
hi d
r r ec tl y
tu
ea r es t n
a c c o
the f i r s t c a s e,
c a n
t s tep
a i n
a tten
n
to
the
i f
g
g
a r i ety
n
I n
a te
c l a s s
n
u
to
the
a
a y
l a r
n
s
ex
he m
o
er
ea r es t
r .
e m
i n
el o
r d
hy
a tter n
a tter n
t b
T
c l a s s i f i ed
p
I n
c es s i n
s i s .
l i c i ty ,
p
hb
n
the n
i r e en
i n
s o
a t l ea s t the m
w
p
s ed
es ti m
a r ti c u
n
n
c l a s s i f i c a ti o
i ty
s .
i s
o
n
to
d
u
,
- n
s i m
hi l o
to
o
o
o
r
o
k
r k
a tter n
r s
k
a
een
ea r es t n
o
n
o
a l
he p
p
n
tu
b
T
the
etw
c ep
ha s
tha t
ei g
n
n
e
u
r es
r o
a l y
a c q
m
r s
n
r es
tr a c ti o
to
hu
to
r s
x
er ts
A
n
E
p
n
n
r e
c o
i s
o
.
ea tu
ha s
n
i s
ea r es t
F
g
ex
y
r u
r i ty
i tti n
b
i ts
l i c a ti o
a j o
ea tu
the
c l a s s i f i er
a r d
ea r es t n
F
g
ti f i ed
f
N
r el i es
F
c l a s s i f i c a ti o
o
r w
ti n
r o
the
- f o
n
i a l
t the f i n
a ta
r u
o
the
i l i es
D
u
m
n
) ,
hi d
en
en
a te
f eed
o
etr a ti o
( N
) .
c l a s s i f i ed
v
o
P
( N
l em
i th d
n
en
O
) ,
c e i d
el o
p
P
i ty
l y
M
( 1
O
ev
o
the
−
d
l ex
the
−
a s er
p
the P
−
i m
L
m
a n
g
the
c e o
r es
f o
f
r
The
topology
applying
the
of
the
c asc ade
neural
network
c orrelation
different error type c lassifiers.
B
is
designed
algorithm
[ 1
1
]
interv
by
to
im
oth feed- forward neural
network c lassifiers and k- nearest neighbor c lassifiers hav
Figure
6
we
plot
alidation set ( m
trials) .
the
results
with a v
the
that
m
with
iation ov
er 1
0
0
the
parable
in
ac c urate
c lassic al
perform
one
and
anc e,
in
v
the
neural
onc e
1
alidation.
the
to 1
The
the
m
c lassifier
has
notic eably
error
a
lower
m
ax
c ase.
M
oreov
al
c om
plex
im
with
respec t
to
er
the
ity
the
of
best
c om
4
ity
inv
estigate
the
reproduc ed the sam
whole
set
of
1
0
c orrelation
c om
topologies.
notic eable
c onfirm
of
feature
e c lassific ation ex
for
m
eac h
ethod
The
differenc e
to
inv
the
leav
estigate
of
this
ing
rev
eal
test
error
ant features m
the training phase we uses part of the av
the
topology
L
ev
network
and
selec tion.
enberg- M
arq
the
The
uardt
preproc essed by norm
rest
training
ue to sm
possible
phase
interv
use
and
als)
perform
estim
to
all am
ate
D
ep
the
m
the
t h
3
aj ority
a
for the estim
B
y
2
]
the
data
are
and
of
sam
statistic al
ple
for
bound
probability
the
of
B
and
it
the c om
position of the
the best result in ac c urac y
the m
it
ent
when
ax
im
with
a
different
c lassifier
um
is
likelihood
ε ,
the
initial
giv
en,
estim
s
if ic
a
t io
n
e
r r o
r
a
n
d
d
e
v
ia
t io
n
o
is the num
τ
distribution,
c
l a
s
if ie
v
is
possible
the
to
c om
real
eric ally
and
sam
ertic al ax
m
easurem
M
it h
the
N
ples.
G
iv
en
the
and
c onfidenc e
the
estim
interv
ated
als
error
for
the
lev
el
of
c onfidenc e
) .
is there is the true error ε,
is is the estim
ent ( ex
atlab on a P
c om
plex
and the
ity
ated error
inary ev
perim
entium
and
m
ents hav
I I I
em
ε .
I n this paper we
aluation of the system
m
for q
e been perform
onoproc essor system
ory
oc c upation
for
neural networks is lower with respec t of K
uality
ed by using
) .
S
inc e the
feed- forward
N
N
c lassifiers
we c hoose for all the error type detec tion the best neural
c lassifier
ev
obtained.
W
ith
suc h
a
system
the
aluation of a standard welding took about 3
M
w
ple of
applying
isc lassified sam
pute
error
tabulated
present the prelim
we
r s
ust
the
)
training
s
m
for
τ
=
ber of m
I n the horiz ontal ax
c onfidenc e
f
er
weights
we
ator
by drawing a random
data
. 95 in Figure 7
γ = 0
Power Error
s
ost
alidation has been selec ted ov
f e
a
t u
r e
r e
d
u
c
ou
t io
n
n
t
( h
Error
o
r iz
o
n
Poros i t y
t a
l
a
x is
:
n
u
m
b
e
r
o
f
s
e
l e
c
t e
d
f e
a
Error
t u
q
uality
sec onds.
Error
Fig. 6. Cl a
and the
)
c onfidenc e
approac h
N
als of c onfidenc e.
perim
m
from
ε ( num
it is also
the
( by
ayesian
error
N
ε
ated ac c urac y of the best c lassifiers
applying
ex
the
between
] .
ailable,
the
)
and
the
ples av
e
where
thus
using
and
ilies ( k- nn and neural c lassifiers)
)
aliz ation and resc aling in order to
ount of sam
obtain
anc e.
[ 1
alidation subsets,
ε
no
ed
set
ple.
c lassifier to it:
ore
perform
training
we
alidation
)
ade by
alidation
are
The table shows for eac h error type
probability of error
ailable data to
c ross- v
is
algorithm
speed- up the learning phase [ 1
D
for
.
the
m
the
ount and porosity)
Theoretic ally,
the off- line test.
train
m
different
ac hiev
we
also
c lassific ation
ing the a- priori c hoic e of relev
power,
ents using the
type
bounds
initializ ation
the
reduc tion
perim
error
results
in
0
siz e
alidity
features
c asc ade
plex
v
al
I n the c ase of feed- forward neural networks,
c orresponding c lassic al c lassifier.
To
interv
sing the plot in Figure 7
from
ac c urate network in c ross- v
of
neural
of
U
ely are an i. i. d sam
c orresponding interv
neurons,
plex
c onfidenc e
.
e the hypothesis that the c ross- v
for the two fam
)
I n partic ular the best neural network is
ounting
el
independent
training and v
ore ac c urate than the k- nearest neighbor c lassifier in the
in
is
( depth,
are
subset
- lev
presented in table 1
plot
c lassifier
c orrec t
0
%
The final results of the training phase for our tests are
the
different
ber of hidden neurons ( from
ost
feature is selec ted.
m
ean and dev
ariable num
shows
c om
obtained
s of c lassific ation error on
The neural c lassifier has been c reated and tuned
selec ting
5
patterns effec tiv
reduc ed set of features in term
the v
The
plic itly assum
subset
e
been trained.
I n
als.
plotted as func tion of N
the
r e
s
) .
T
C
l a s s i f i e r
T
r a i n
i n
g
C
a
r o
b
l e
s s
1
V
.
E
x p
a l i d
e
a t i o
r i m
e
n
e p
t h
K
4
N
0
N
s a m
( 1
- N
F
F
N
2
8
0
o
w
e r
K
N
N
6
9
F
4
8
N
o
u
n
t
K
N
N
5
0
F
N
3
9
P
o
r o
s i t y
K
N
F
N
- N
F
N
2
1
0
1
9
4
l e s
%
( 1
0
9
s a m
0
%
l e s
2
%
0
1
s a m
l e s
8
p
l e s
p
l e s
)
p
l e s
p
l e s
%
0
c c u
r a c y
I n
t e r v
0
%
0
%
~
– 1
0
0
–
~
0
1
0
%
~
0
–
0
%
~
0
–
1
. 8
%
0
0
~
0
%
8
p
%
l e s
0
. 3
5
1
0
l e s
%
N
0
0
%
J .
]
E
H
l i o
.
p
]
[5
. C
]
D
]
L
]
G
F
s o
Y
W
8
.
–
4
%
~
0
–
4
%
F
H
.
.
t e
r v
a
l
f o
r
γ =
0
. 9
[10
5
]
S
. C
O
N
C
L
U
S
I O
N
S
M
[12
A
a p
o
r o
f
n
s y
w
n
p
e u
a u
o
l e
m
f o
r a l
s t e m
h
t o
a c h
n
t o
c o
m
a t i c
r
p
q
u
l a s e r
e t w
o
a c h
i e v
u
r k
t a t i o
a l i t y
w
s
e l d
a n
n
e
g
a l
o
l o
d
o
a n
a l y
g
i s
i n
t r a d
d
a d
p
s e l f - t u
r e s e n
i t i o
a c c u
.
s i s
n
a l
r a c y
n
t e d
a l g
k
.
o
e e p
i n
T
g
e a s u
e
r i t h
i n
m
h
g
m
c o
s
b
m
r e m
b
o
a l l o
i n
u
n
w
d
s
e d
e n
a t i o
.
e
[13
0
0
)
s =
v
0
)
0
)
0
)
4
e r
=
1
0
1
e u
r o
n
o
s =
v
2
e r
1
0
1
=
e u
g
n
9
p
N
C
E
g
l e n
s =
v
4
e r
1
0
S
l i c a t i o n
s i n
8
” .
P
e r g
s e s ” .
a m
I n
d
m
u
o
n
p
s t r i a l
r e s s ,
19
l a s e r
6
s o
7
l u
t i o
n
,
.
u
l t r a s o
i n
U
I n
F
a h
l m
p
n
s t e e l
S
a n
p
.
g
.
e n
“ F
e c t r o
,
S
o
.
i c
l o
s t r u
c t u
“ R
n
g
- r a n
g
r e s ” .
e l a t i o
e t r a t i o
u
z z y
n
l e y .
i v
n
a n
n
” .
d
u
s h
W
N
r e v
a n
a n
g
9
P
e
P
i m
a g
r o
c e e d
f
o p
i n
g
i n
s c h
g
s
o
e m
f
O
e
f o
M
A
r
E
,
i p
e l d
e u
o
i n
r a l
g
a p
J o
p
t i c a l
u
a n
d
a c o
a l ,
19
9
a c h
i n
e n
g i n
f a c t u
r i n
r o
r n
8
u
s t i c
.
e e r i n
g
” .
L
o
l
w
m
o
e r n
.
i n
v
R
E
E
m
9
T
a n
r l d
,
“ C
u
19
9
8
g
o
f
t h
p
r o
c e s s e s ” .
.
l a s s i f i c a t i o n
g
6
2
e u
M
n
e
2
9
o
t h
f
L
c o
n
a s e r
w
f e r e n
c e
e l d
s
o
n
u
s t e r n
i t i o n
a n
” .
S
N
e u
d
p
r i n
g
r a l
e r - V
N
e r l a g
e t w
o
r k
,
s ” .
.
c h
2
a n
g
M
s a c t i o n
,
“ T
N
n
g
r a n
e :
i n
o
e c o
19
i e r e .
c e s
K
J i a n
l u
e b
a n
.
a t i o n
I E
V
t r o
c e e d
. ;
” ,
n
i c s
0
. W
c e ,
v
T
n
r o
r e s s ,
P
.
d
.
P
a t t e r n
,
i e w
C
A
K
19
“ P
e n
,
,
r e ” .
l ,
c o
e l e c t r o
l a s s i f i k
i n
t e l l i g
e t e r s
t o
e r s i t y
D
r e ” .
F
t r o
i p
m
p
a t u
“ K
a
O
n
.
n
:
r i g
i g
C
n
n
h
.
S
d
.
e l d
r l d
K
. ;
e
H
a g
u
g
t
y
e n
u
n
o
.
p
e r i m
a n
a r d
c e e d
e t w
,
,
e t w
i n
r k
g
i n
g
e n
.
o
.
r k
19
9
o
J a n
h
e
.
C
r a l
I n
2
a o
s
0
o
0
0
p
a s c a d
f o
r m
“ S
n
a t t e r n
a g
e ( s ) :
e - C
a t i o
t a t i s t i c a l
P
o
n
A
n
4
- 3
r r e l a t i o
P
r o
p
a t t e r n
a l y s i s
a n
d
7
n
L
c e s s i n
g
e a r n
S
i n
y s t e m
g
s ,
h
t t p
: / / w
w
w
. i m
]
h
t t p
: / / w
w
w
. s l a p
[16
]
h
t t p
: / / w
w
[17
]
t t p
: / / w
s . o
w
“ T
E
o
g
t e r n
i n
r a n
“ I m
s i n
I n
r a i n
T
,
o
e
.
. c r f . i t /
w
. r o
“ T
S
r g
s . o
w
f i n
c h
t h
e l l
]
I E
p
.
r o
i n
g
O
n
v
i n
f o
N
r a l
e u
g
i t i a l
a t i o n
f e e d
v
a l
t h
e
a l u
J o
a r d
N
o
t
f
o
o
r k
g
e
n
e t w
o
i n
t h
C
n
e t w
l e a r n
e s
i n
r w
s ,
s p
a d
f e r e n
r k
s
19
e e d
a p
t i v
c e
w
9
4
o
f
e
w
o
i t h
t h
e
.
2
- l a y
e i g
h
t s ” .
N
e u
r a l
n
e r
.
a n
B
a j .
r o
y
f
0
h
” .
i d
b
l e y m
t s ” .
e n
m
W
s
[15
w
M
r i t h
B
o
s
s ,
H
M
a l g
[14
h
E
a p
c u
e w
Y
w
“ S
,
e
i n
r o
. H
d
o
19
n
R
n
o
.
.
N
W
,
n
l l ,
o
R
i t i o
P
e x
E
r o
e s t
7
w
.
. K
r a l
]
F
“ F
r l d
“ A
. U
9
.
a n
A
e u
N
e
.
n
a n
r i d
n
t
t h
t h
D
o
A
P
s
i t e c t u
0
.
s t i c
n
n
a r q
]
n
g
. T
i o
. D
b
9
H
w
l a s e r
19
c u
u
. B
r c h
19
A
a r s o
,
. E
A
,
i e m
a c h
[11]
V
F
3
J a i n
M
i n
n
o
e s t
N
a n
e r r i t .
e r .
t o
i x
o
N
8
n
c o
a m
]
k
a l a s ,
F
A
r e c o
e
r o
K
E
s y s t e m
H
i c s
a k
s
k
e c i s i o
]
C
[9
c
1
1
K
N
0
.
n
i l e y ,
D
y
19
n
2
e r
=
e u
%
a r a c t e r i z a t i o
n
u
. J .
D
[8
B
a r s o
. T
D
G
.
a s e r
]
a s e r
c h
i s s i o
b
e
s =
v
8
.
L
[7
. M
9
“ L
y ,
e l e c t r o
e f e c t
N
[6
.
e e d
t o
M
n
R
e m
f i d
n
o
e s t
( B
[4
n
r o
%
)
19
o
e u
e s t
K
~
%
d
C
1
)
p
[3
.
N
%
–
~
O
7
t e s
=
%
8
–
0
%
[1]
.
o
K
( B
[2
i g
N
%
R
F
a l
)
0
( 3
A
)
s a m
1
f
( B
%
s a m
o
)
0
6
r
c l a s s i f i e r
)
0
( ~
l t s
( B
%
0
4
r r o
e s t
l e s
%
6
1
)
p
0
( ~
1
u
)
0
( 3
p
p
s a m
)
s a m
0
s a m
5
)
p
0
( 7
1
1
s
)
%
( ~
r e
l e s
%
s a m
2
l e s
0
0
( 3
%
s a m
5
8
)
p
p
0
s a m
6
l e s
s a m
( 7
2
)
p
%
0
1
1
l e s
%
0
( 1
- N
F
s a m
( 3
p
s a m
5
9
( ~
)
s a m
( 7
M
l e s
%
0
3
)
p
0
( 1
- N
F
l e s
%
s a m
( 7
P
p
0
l
E
n
b
D
t a
h
e
d
y s t e m
e s i g
T
/
e c h
r g
- s i n
/
a r . c o
m
/
n
a n
.
J o
d
a n
u
r n
a l y s i s
a l ,
19
6
o
2
f
.
p
a t t e r n
r e c o
g
n
i t i o n