Research on the quantitative analysis of subsurface defects for non

NDT&E International 45 (2012) 104–110
Contents lists available at SciVerse ScienceDirect
NDT&E International
journal homepage: www.elsevier.com/locate/ndteint
Research on the quantitative analysis of subsurface defects
for non-destructive testing by lock-in thermography
Liu Junyan n, Tang Qingju, Liu Xun, Wang Yang
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, PR China
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 12 April 2011
Received in revised form
1 September 2011
Accepted 2 September 2011
Available online 17 September 2011
This pa
p
paper
ape
per
p
er describes
descri
des
cri
ri b
ri
be
bes
e
ess the
th
he quantitative
he
qua
qua
anti
nti
nt
tittat
at
atiiv
ive
ve
ve analysis
ana
ana
aly
llys
ysis of
y
of the
the boundary,
th
boundary, location and
b
a
depth of subsurface defects
by
y llock-in
ock
oc
ock
c -in
n thermography.
th
tthe
h
he
errmo
rm
mo
m
ogra
aph
phy
h . The
Th
T e phase
ph
has
ase difference
ase
diif
dif
d
iffe
iffer
fe
fer
errenc
en
enc
e
ncce
nc
e between
betw
be
ween defective areas
a
and non-defective areas
iillustrates
ill
il
ll
llust
u rat
ate
at
ess the
th
he
e qualitative
qual
ua
alit
al
iita
tta
ativ
tive analysis
ti
an
a alysis
sis
i of
of the
th
he
e boundary
boun
unda
un
dar
dar
ary and
and the
th
the location
llocation of the
th subsurface defect. In order to
accurately
ac
acc
ura
atel
tte
elly estimate
e
essti
ttim
imate
t the size, location and
and depth
depth
th of
th
of the
the
e defects,
de
efe
f cts, the phase
p
is normalised, the heat
tra
ra
an
nsf
ns
sffe
err partial
par
art
a
r ial
rt
ia differential equation
equ
qu
uati
atti
a
ation
tion
o (PDE)
(PDE) model
mod
del
de
e iss used
ussed
se
ed
e
d to filter
filt
lt r the noise
lte
no
transfer
of normalised phase image
and
nd
d the
e differential
differenttial
all normalised
a
no
no
nor
orrmal
m ise
ed phase
ph
phase
assse
e profile
prr file iss employed
pro
p
empl
m loye
oye
oy
yed to determine
yed
det
d
de
etermine the boundary and location of
the defect.
de ect. The profile
de
def
prrofil
ofi
ofi
fille of
of the
tth
he differential
h
dif
d
ifffe
fer
e ent
er
en ia
al normalised
al
normalise
sed
d phase
phase
ph
a distribution
dis
istri
ttri
tr
ribut
ri
b ion has maximum, minimum and
zer
erro points
points that help
elp
lp
p to
to quantitatively
qu
ua
uan
a tit
titati
atti
a
ati
tivel
vel
ve
ely determine
d
determine the
e boundary
bound
nd
dary and
an location
an
loc
zero
of the subsurface defect.
An
n artificial
artificial neural network
net
etttw
work (ANN)
N)) is
is proposed
pr o
pr
op
opo
p sed to determine
dete
e rmi
miin
m
ne
e the
the
he depth
de
of the subsurface defect.
Exp
Ex
E
x erimental results for
fo
or a steel plate,
plate
te
e, a carbon
carb
carb
rb
bon
n fibre-reinforced
fibre-rein
in
nfforcce
n
ed
d polymer
poly
oly
lymer (CFRP) sheet–foam sandwich,
Experimental
a
an
nd honeycomb structure
structtu
ure
ur
rre
e composites
ccom
om
mpos
p ite
po
es with
wiitth
w
h artificial
artificial subsurface
subssurf
u ace
ce defects
ce
de
de
def
effe
ect
ec
c s show
s
and
good agreement with the
a
act
ual values.
actual
201
20
011 Elsevier
0
E
& 2011
Ltd. All rights reserved.
Keywords:
Lock-in thermography
PDE
ANN
Differential normalised phase
Quantification
1. Introduction
te
n
ht l:
tp +8
:/ 6
/w 41
w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
Infrared thermography (IRT)
T) has been
be
b
een
n successfully
ssu
ucc
c es
essf
sful
u ly used
d ass a
non-destructive testing and evaluation
evaluatio
io
on (NDT&E)
(ND
NDT
T&
&E)) technique
tech
te
c nique in
many applications. Infrared thermography
thermograph
phy provides
ph
pro
rovi
ovi
vide
ide
d s the
th
he
e colour
c lour
co
mens in which
h local
llo
oca
al changes
chang
hang
ha
nges in
in the
the
th
temperature image of specimens
defect
cttss.. Since
S nc
Si
nce
e the
the
he beginbegi
be
begi
ginn
surface temperature indicate subsurface defects.
een successfully used
use
ed as
as a NDT&E
NDT
ND
T&
&
&E
E
ning of the 1960s, IRT has been
technique for materials [1–3].
cilitates better subsurface defect
Lock-in thermography facilitates
detection than ordinary infrared thermography because the
thermal wave is very sensitive to interfaces between materials
and it is less sensitive to non-uniform emission and surrounding
conditions. With the development of advanced inspection technology, the quantitative non-destructive evaluation (QNDE) of
defects plays an important role in NDT. In QNDE, defects are
generally evaluated by the characteristics of position, size, depth,
shape and type. Based on these parameters, the reliability and
service life of the structure can be estimated [4]. To accomplish
this, quantitative determination has been thoroughly studied
both in theory and in practical applications [5]. Although lock-in
thermography provides powerful amplitude and phase images,
QNDE gives a clear bridge relation between the true size of
de
effe
ectss and
a
am
mp
pliitu
tude
de and
an
a
nd phase
p
defects
their amplitude
images. Saintey and
Alm
mond have
h ve proposed
ha
prop
op
pos
osed
e a technique
techn
hnique for defect size determinaAlmond
n in
in which
wh
hic
i h the
he
e defect’s
de
d
efe
f ct
ct’ss size
size
e can be estimated as the full width
tion
ha
alff maximum
ma
maxi
ax
xiimu
mum (FWHM)
(FW
WH
HM
M) contrast
contrast [6]. However, this method
at half
tend
ds to
to estimate
est
stim
tiim
matte the
the
he defect’s
defe
de
fe
ect’s size as larger when the contrast is
tends
sm
ma
alll.
l. In
In some
som
me
e cases,
case
case
ca
ses, the
th
he contrast
contrast profile
profi is non-symmetric, and it
small.
diffi
di
fficcu
ffi
ult
lt to
to evaluate
ev
e
val
a ua
ate
te the quantitative
quantitativ defect size.
iss difficult
In this
tth
hiiss case,
ca
ase
e, we
we suggest a simple,
simple practical method for the
In
quan
nti
tita
tta
ati
tive determination
d termination of the boundary,
de
boun
quantitative
location and depth of
g
subsurface defects. To achieve this goal
of quantitatively determining the defect size, the original phase data from the lock-in
thermography are processed by means of a normalised method,
and the noise of normalised phase image is filtered based on the
heat transfer partial differential equation model. The normalised
phase distribution profile is directly used to calculate its differential value by the numerical difference method, or it is fitted by a
polynomial method and then used to calculate the differential
value. The differential profile of the normalised phase has maximum, minimum and zero points; these points correspond to the
boundary and location of subsurface defects. The artificial neural
network is proposed as a tool to determine the depth of the
defects based on the normalised phase for testing and training.
In our experiments, the reference specimens are made from a steel
plate, a carbon fibre-reinforced polymer (CFRP) sheet–foam sandwich and honeycomb structure composites with known sizes,
locations and depths of subsurface defects. The results obtained
by the proposed technique are compared with actual values.
Corresponding author. Tel.: þ86 451 8640 3380; fax: þ86 451 8640 2755.
E-mail address: Ljywlj@hit.edu.cn (L. Junyan).
0963-8695/$ - see front matter & 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ndteint.2011.09.002
L. Junyan et al. / NDT&E International 45 (2012) 104–110
2. Theory
2.1. Lock-in thermography
Lock-in thermography was first proposed by Carlomagno and
Berardi. The lock-in principle is the technique of choice if a signal
has to be extracted from statistical noise [7]. Basically, the heat
source induced from outside has to be modulated and calibrated
to make the surface temperature variation from the heat source
truly sinusoidal. The infrared camera captures a series of thermal
images and extracts a sinusoidal thermal wave pattern at each
pixel of the thermal image at a frequency modulated by different
thermal wave signal analysis methods [8]. The surface temperature T(x,y) (z ¼0) in one dimension is given by
Tðx,yÞ ¼ Aðx,yÞej½otjðx,yÞ
ð1Þ
where A(x,y) is the surface temperature amplitude and j(x,y) is
the surface temperature phase at each image pixel.
The thermal wave can be
e reconstructed using four equidistant
x,y)–
)
surface temperature (thermal
mal wave) average data points
poin
nttss S1((x,y)–
perio
od
d.. More
More
Mo
re thermal
th
herma
errm l
S4(x,y), with a phase step off a quarter of a period.
aged to obtain four
ur data
ur
data points,
da
po
p
oints
iin
nttss, which
wh
hiicch
h
wave data points were averaged
e noise effectt (Fig.
(F
Fiig. 1).
)
).
were helpful in reducing the
ase shift and
and
nd the
th
he amplitude
am
mp
plliitu
itu
ud
de
e of
of the
th
he
Calculations of the phase
thermal wave are given as follows:
S ðx,yÞS1 ðx,yÞ
x,yÞ
ð2Þ
ð2Þ
jðx,yÞ ¼ arctan 3
x,yÞ
S4 ðx,yÞS2 ðx,yÞ
Aðx,yÞ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffi
ffiffiffiffi ffiffiffiffiffiffi ffiffiffiffi
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
½S3 ðx,yÞS1 ðx,yÞ2 þ ½S4 ððx,yÞS
x,y
yÞS
ÞS2 ðx
Þ
ðx,yÞ
x, yÞ2
phase image. The heat transfer partial differential equation is
described as follows:
( @uðx,y,tÞ
¼ a Duðx,y,tÞ
@t
ð5Þ
uðx,y,0Þ ¼ u0 ðx,yÞ
where u(x,y,t) is the two-dimensional temperature field, D is the
Laplacian operator, a is the thermal diffusivity and u0(x,y) is the
initial temperature.
The temperature field can be obtained by the difference form
of Eq. (5), which is given by
uðx,y,tÞu0 ðx,yÞ
¼ aDuðx,y,0Þ
t
ð6Þ
uðx,y,tÞ ¼ u0 ðx,yÞ þ t aDuðx,y,0Þ
ð7Þ
The normalised phase can be filtered by Eq. (7), and the
filtered phase is given by
jFNormal ðx,yÞ ¼ jNormal ðx,yÞ þ bDjNormal ðx,yÞ
ð8Þ
(x,y)
, ) is the normalis
normalised phase filtered at the pixel
where
w
wh
he
errre jFNormal(x,y
i the filtered coefficien
coefficient b ¼ t a.
(x,y)
and
(x
x,,y
y) a
nd b is
nd
If b 4
If
40,
0, Eq.
Eq. ((8)
Eq
8 is the so-called regular
8)
r
heat transfer PDE filter
and
at low frequency band,
a
an
nd tthe
he
h
e normalised
no
n
orm
rmal
alis
ise
is
ed
d phase
p ase image is filtered
ph
fi
o0,
0,, E
0
Eq
Eq.
q
q.. (8)
(8) is
is the
the so-called iinverse heat transfer PDE filter
th
a
an
nd iiff b o
and
and tthe
he normalised
n rm
no
ma
alliisse
ed
d phase
phase
ph
s image is filtered at high frequency
ba
b
and
n . Eq. (8)
(8
8) is
is used
use
ed to
to filter
filt
lter the noise
n
band.
of the normalised phase
da
d
atta
a from
fro
fr
lock
ck
k-i
- n thermography
the
hermog
ography and to improve the contrast
og
data
lock-in
be
etw
twee
en the defective
defe
fe
ect
c ive area and
and
n the healthy area on the normalbetween
iis
ssed
ed
e
d ph
phase image.
ised
ð3Þ
e helpful
hel
elpfful
el
elpf
ul for
for estimating
fo
estimating the defect
defe
ectt size
ssiiz
ze
e
The phase image is more
om
mm
mo
on thermal
the
herm
rmal image because
becau
use
e the
in comparison with the common
han
nges att defect
defect spots, and itt is
is less
phase has more contrast changes
rm surface
sur
urrffa
a
ace
ce emission,
ce
emission, ambient
em
ambie
ie
en
ntt distursensitive to the non-uniform
bance and so on.
2.2. Phase noise filtering
e variable
varria
ia
abl
ble
bl
le range,
ran
ange
g , the
he phase
ph
p
hase
ase is
as
is
In order to reduce the phase
normalised as follows:
jmax jðx,yÞ
jmax jmin
ðð4Þ
4Þ
2.3.
3.. Di
D
Diff
iff
ffeerreen
ntial calculation
calcul
ullatio
io
on of
of the
the
he normalised
no
Differential
phase
The normalised phase
pha
ha
ase
se distribution
dist
di
stri
ribu
ri
ib
bu
utio along the x direction of the
sp
pe
eccimen surface is analysed
ana
naly
na
l sed by means
me
specimen
of the differential calculati
ion
on method. The differential
differ
erential
er
tia
ti
all normalised
no
orma
tion
phase profile in Eq. (9) is
ca
alc
l ulated by subtraction
subtra
ra
acttio
on o
the adjacent
a
calculated
off the
normalised phase. In
a
ad
dd
diiti
tion, the normal
allis
a
ised
d phase
ph
p
has
ase distribution
dist
addition,
normalised
along the x direction
iiss fit
fi
tte
ted b
y a poly
ly
ynomi
mia
all in
i E
q. (1
fitted
by
polynomial
Eq.
(10), and then the differential
no
n
orma
alised phase
al
phas
asse is
is calculated
cal
cal
alccu
ulate
te
ed by Eq.
E (11):
normalised
jFFNor
x,y
yÞx ¼ jFNor
FNormal
Nor
No
orrmall ððx,yÞ
FNormal
No
Nor
N
orrm
o
mal
ma
a ðx þ Dx,yÞjFNormal ðx,yÞ
xÞ ¼
f jFN
FNormal
F
FNor
N mal
al ððxÞ
n
X
malised phase, jmaxx is
i the
th
he maximum
max
axim
imu
um
m
where jNormal is the normalised
e of
of the
the
e phase.
ph
hasse.
e
value of the phase and jminn is the minimum value
In order to enhance the contrast and reduce the background
back
kgro
kg
roun
und
sed phase data are processed based on
noise influence, the normalised
the heat transfer partial differential equation, which can be called
the filter of the heat transfer partial differential equation (PDE)
and it is always used to process image in signal processing fields.
In this paper, the heat transfer PDE is utilised to filter normalised
ð10Þ
iUai xi1 ¼ a1 þ2a2 x þ3a3 x2 þ þ nan xn1
ð11Þ
i¼0
f j0FFN
FNormal
FNor
FNo
N mal ðxÞ ¼
n
X
i¼1
where jFnormal (x,y)x is the differential normalising phase, Dx
is the step in the x direction, fjFnormal (x) is the fitting of
the normalised phase along the x direction, fj0 Fnormal (x) is the
differential normalised phase of polynomial fitting and ai is the
polynomial coefficient.
The least square method (LEM) is applied to fit the polynomial
normalised phase along the x direction of the spatial profile.
The highest polynomial exponent should be selected to reduce
the fitting deviation and avoid more oscillations. The allowance
deviation of polynomial fitting is given as
te
e0 ¼ c 9maxðjNormal ðx,yÞ9y ÞminðjNormal ðx,yÞ9y Þ9
Fig. 1. Correlation principle of lock-in thermography.
ð9Þ
ai xi ¼ a0 þ a1 x þ a2 x2 þ þ an xn
ht l:
tp +8
:/ 6
/w 41
w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
jNormal ðx,yÞ ¼
105
ð12Þ
where e0 is the allowance deviation of polynomial fitting, c is the
scale factor, usually, it should be selected that c o0.1, and
jNormal(x,y)9y is the normalised phase distribution along the x
direction of the spatial profile.
The deviation of the polynomial fitting is defined as the
maximum absolute difference value between the polynomial
fitting values and actual values of the normalised phase along
106
L. Junyan et al. / NDT&E International 45 (2012) 104–110
the x direction of the spatial profile:
2.4. Artificial neural network classifier for depth of defect
Dj ¼ maxð9f jNormal ðxÞjNormal ðx,yÞ9y 9Þ
ð13Þ
where Dj is the deviation of the polynomial fitting.
This polynomial fitting process is repeated so that the criteria
condition is satisfied that Dj o e0 (in Fig. 2). The polynomial
fitting process is finished using the Matlab programme.
In order to enhance the polynomial fitting accuracy and reduce
oscillations at higher order, the normalised phase profile can be
divided into many small segments, and then the above polynomial
fitting is allowed to be applied on each segment of the normalised
phase profile. This method is available for showing good fitting
results. The differential normalised phase profile distribution provides maximum, minimum and zero values, the distance between
characteristic points (maximum value and minimum value) is used
for defect size determination and the zero value point is used for
local defect centre determination.
An artificial neural network is a massively parallel distributed
processor that has a natural propensity for storing experiential
knowledge and making it available for use. Artificial neural
networks as classifiers for defect detection in non-destructive
testing when using infrared thermography have been proposed in
the past few years, and multilayer perceptrons were employed to
detect and characterise defects using the normalised phase data
from lock-in thermography.
In this work, a multilayer perceptron classifier is characterised
by the presence of an input layer of source nodes, a hidden layer
and an output layer (in Fig. 3). The multilayer perceptron network
is called a feedback network that can be trained using the back
propagation algorithm, which has been widely the most used
learning algorithm in recent years.
The input vector is Pi that includes the modulated frequency
fLockin and the normalised phase at different pixel (x,y) is given as
follows:
Pi ¼ ffff Lock
Lockin
ck
kin
in , jN
Normal
Norm
No
Nor
orrmal ðx,yÞg
or
This
Th
T
h
his
is feedback
is
fe
eed
edba
dba
b ck
k network
netw
ne
twork architecture can be used to identify the
de
defe
fect
ctt and
an
nd inspect
in
nssp
pe
ectt the
the defect
th
defect depth,
dept
defect
so the output layer of
feed
dbacck
k network
n tw
ne
twor
ork has
has
a two
ttw
wo defect
defect detectors’
dete
feedback
neuron cells and the
ou
utp
t ut layerr value
val
a ue
e are
arre given
a
giv
ve
en
n by
output
ϕ
ϕ
=
ϕ
−ϕ
ð15Þ
liinearððW O2 lo
llogsigðW
gssiig
gðW
W H P i þ bH ÞÞ þ bO2 Þ
De
epde
Dep
def
eff ¼ linearððW
e
ð16Þ
te
ht l:
tp +8
:/ 6
/w 41
w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
Δϕ > ε
Fig. 2. Diagram off the polynomial fitting.
logsigðW
ogs
gssig
igððW H Pi þ bH ÞÞ þ bO
logsigððW
A ¼ llo
og
gssig
igðð
ð W O1 lo
O1 Þ
one
detector
network outputs for
where
e A is
is on
o
ne of tthe
he defect
de
d
efe
fectt d
ete
iidentifying
id
denti
tifyi
ti
fyin
fy
ing defect;
ing
defect; its desired
de
esirred
ed output
outp
ou
outp
tput
p value is 0 for healthy pixel
a
an
nd 1 for defective pixe
xel. De
D
epde
and
pixel.
Dep
def
ef is one of the defect detector
netw
work outputs for measuring
mea
ea
asu
suring
riing defect
def
de
efect depth.
network
Th
he hidden
hidden layer enab
ble
es tthe
he
e neural
ne
eura network to extract higherThe
enables
or
rde
er sstatistics,
tatistics, especial
allly
ly when
when
wh
en the
th
he size of input layer is large
order
especially
en
nou
ugh
h. T
he supervised
d lle
earni
rniing
g me
etho is concerned with applying
enough.
The
learning
method
ma
any
y training
trra ning examples
trai
exampl
plless to modify
m diiffy
mo
y the connecting weights of the
many
ne
eu
urron
n cells.
ce
ells. Each example
ex
xa
am
mpl
ple
e includes
inc
nclu
l d
de
es a se
neuron
series of input vectors and the
corr
respo
p nd
ndin
i g des
essiirrred
ed
e
d re
esp
spon
onse
se
e. The artificial neural network is
corresponding
desired
response.
pres
sen
ente
ted with
with
h plenty
plle
ent
nty of
of examples,
exam
mples, and the connecting weights are
mp
presented
adju
usstte
ed
d to m
mi
ini
ini
nimi
m sse
e the
the difference
th
difffe
ference between
bet
adjusted
minimise
the desired output and
th
he a
ctua
al rresponse
espons
es
po
on
nsse of
of the
the
e network.
network. The network training is repeated
the
actual
u
un
ntil
ttiil a steady
sttea
eady
dy sst
ta
atte iiss rreached.
eached. At that time,
ea
t
until
state
the connecting weights
a
ar
re no
no longer
lon
ong
ge
er v
arrie
ied. In this work, this network was trained using
are
varied.
731 iinput–output
73
731
n ut
np
u –o
ou
uttput pair points extract from
fro the metal samples, which
ϕ
Δϕ =
ð14Þ
Fig. 3. An artificial neural network with one hidden layer and an output.
L. Junyan et al. / NDT&E International 45 (2012) 104–110
contained air (bottom-flat hole) delamination defects in different
depths and configurations, 603 input vectors were the modulated
frequency and normalised phase pair points over healthy area and
32 were over at each defect depth area (4 different defect depth
areas). The network was trained using 1240 input–output pair
points extract from the CFRP sheet sandwich samples, which
contained Teflon insert defects in different configurations, 1000
input vectors were the modulated frequency and normalised phase
pair point over healthy area and 120 were over at defective area
(2 defective area). After different simulation and experimental tests,
the ANN is settled for a 30 2 defect detector network for the metal
sample and settled for a 60 2 defect detector network for the CFRP
sheet sandwich sample, so the reported experiments were executed
with 60 nodes for the metal material and 120 nodes for the CFRP
sheet sandwich material. The artificial neural network is applied to
recognise the depth of the subsurface defects.
107
12.8 10 6 m2/s, and the thermal diffusivity of the CFRP material
is 0.42 10 6 m2/s.
The experimental system consists of the infrared camera, the
power amplifier controller, the function generator, the system
controller and the heat source (2 halogen lamps, the power of
each lamp being 1 KW), as shown in Fig. 5. The heat source was
driven by a power amplifier controller, and the heat source and
the infrared camera were synchronised using the sinusoidal
function of the function generator and the lock-in modulus. The
phase angle data were calculated by the lock-in modulus at the
optimum modulated frequency that produces the maximum
phase difference between the defective area and the healthy area.
4. Results and discussion
4.1. Specimen 1: flat bottom holes of ANSI 1045 steel
mental procedure
3. Specimens and experimental
ht l:
tp +8
:/ 6
/w 41
w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
ns, with known defects
deffec
ects
ts of
of different
diff
di
ffffer
ere
en
nt
The reference specimens,
re made as shown
show
wn in
in Fig.
Fig.
Fi
g 4.
4. Specimen
Sp
pe
eci
cm
me
en 1
en
depths, shapes and sizes, were
5 steel with four
fo
our
ur rows
wss of
w
of flat-bottom
fla
fl
atatt--bo
bottom
was made from ANSI 1045
pecimen 2 was
was made
wa
ma
ad
de from
frro
om CFRP
CF
FR
RP sheet
she
h et
holes at different depths. Specimen
wit
ith two
two
w rectangular
rre
ectta
an
ngular Teflon
face and foam sandwich material with
hs with
h the
tth
he same
same
ame sizes.
am
siz
izes. Specimen
Spe
ecciime
im
me
en 3
inserts at the same depths
eet face
ce and aluminium
ce
alu
l m
miinium honeycomb
ho
on
ne
eyc
yco
om
mb
was made from CFRP sheet
rts at
at the
th same
s me
sa
e depths
depths with the
de
th
he same
sa
am
me
me
with four round Teflon inserts
ty off the
the steel
steel material
st
material (ANSI 1045)
10
045
45) is
sizes. The thermal diffusivity
Fig. 6 shows the defects of specimen
sp
1 detected using the
no
n
orm
rmal
rmal
alis
ised
e phase image at the optimum
opti
normalised
modulated frequency of
0.
.12
2 Hz.
Hz
0.12
All
that form the subsurface
A
ll of
of tthe
he defects
defe
de
fect
c s at
at different depths
de
are
e cclearly
le
ear
arlly
y observed
obsse
obs
ob
e
erv
ved
ed in
in the normalised
normalis phase image with different
T
normalised
normali
phase image is obviously
fil
lte
tere
red coefficients
re
coe
effi
ffici
cie
en
nts
t b. The
filtered
¼0.4 (Fig. 6(c)), and the contrast
ssmoothed
sm
oothed
d at
at filtered
fillte
terre
ed coefficient
coe
effi
fficient b ¼
main
inly improved
im
mprrov
o ed
d at
at higher
h gh
hi
her values of the filtered coefficient. The
iss ma
mainly
detta
de
ails
ls of
of the normalised
no
orm
rmal
alised phase
phas
ph
a e image
im
details
are clearly recognised for
0.4
0.4 (Fig.
(F
6(d)). Fig. 6(e) shows the
tth
he filtered
fillttered coefficient
coeffficcie
i nt b ¼ the
no
orrm
m
mal
a issed phase difference
al
dif
iffere
re
enc
nce induced
in
nd
duce by 0.12 Hz at the central line
normalised
te
Fig. 4. Specimen structure and geometry.
Fig. 5. Experimental system.
108
L. Junyan et al. / NDT&E International 45 (2012) 104–110
Fig. 6. Normalised phase filtered images,
ages, normalised phase
pha
ha
h
ase
e distribution
dist
istrib
rib
rib
ri
ibu
uti
t io
ti
on
n and
and
nd differential
diiff
ifffffere
renti
re
ntti
tiall normalised
tial
no
orm
or
rrm
mal
ali
a
lliised
ised
sed
se
ed phase
pha
ph
p
h se
e filtered
filt
ltte
lte
ered
r
images of specimen
speci
1: (a) original phase image;
tered coefficient b ¼ 0.4
0
0.4;
.4; (d
(d)
d) filtered
filltte
fi
ter
ered
e
d coefficient
coefficient b ¼ 0.4;
0.4
.4
4; (e)
(e
(e) n
no
normalised
orm
rrma
ma
m
alli
alis
lis
iissed
s d phase
pha
has
ase distribution in Line I and (f) b ¼ =0 Differential
(b) filtered coefficient b ¼0.3; (c) filtered
normalised phase filtered image.
ht l:
tp +8
:/ 6
/w 41
w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
Fig. 7. Defect location and boundary
y identifications
identificcati
a ons
nss of
of specimen
spec
eccim
ime
me
m
en 1: (a) defect
defect
def
ect location and
nd
d boundary
boun
ndar
da y identification
id
de
en
ent
nttiific
fica
ati
tion
ti
on in
in Line
Liine II
II and (b) defect location and boundary
identification in Line III.
Table
T
Tab
Ta
a
ab
blle
e1
Comparison
Co
C
Com
om
mpar
ariso
ison
iso
n of measurement
mea
me
m
ea
e
asurement results and actual
actua values.
Rows
Measurement Results (mm)
Ac
Actual
size (mm)
Errors (%)
MCL
MD
ACL
AD
CLE
DE
1
23.53
50.84
77.95
8.82
10.08
9.24
24.30
50.50
76.80
9.20
10.30
9.85
3.17
0.67
1.50
4.13
0.48
6.20
2
23.53
50.42
77.74
8.40
9.24
7.56
24.50
50.00
75.80
8.20
8.60
8.10
3.96
0.84
2.56
2.44
7.44
6.67
3
25.11
50.54
75.32
7.15
6.85
6.72
24.60
50.20
75.60
6.80
6.50
6.50
2.07
0.68
0.37
5.15
5.38
3.38
MCL—measurement centre location, MD—measurement diameter, ACL—actual
centre location, AD—actual diameter, CLE—centre location error and DE—diameter error.
te
of the third column (Line I) for defects that are
are
re 1.0
1 0 mm
1.
mm deep.
de
ee
ep
p..
Although the defect locationss can be estimated from
m the
th maxmax
ximum normalised phase difference
ence value, it is difficult to evaluate
evalu
luat
lu
ate
ze. The subsurface defects can be
the defect’s boundary and size.
identified in the differential normalised phase image shown in
Fig. 6(f). Fig. 7 shows the differential normalised phase profile for
the central line of the first row (Line II) and the third column
(Line III) defects.
The defect locations and sizes are quantitatively determined
from the differential normalised phase image in Fig. 6(f) and the
differential normalised phase diagram in Fig. 7. The distance
between the maximum and minimum positions of the differential
normalised phase is calculated as the size of the subsurface
defect, and the zero value position of the differential normalised
phase defines its location. The sizes and central location of the
three rows of defects are listed in Table 1.
The measurement results are compared with the actual values,
and the measurement errors are calculated. The maximum errors
in the defect locations are less than 4%, and their size errors
are less than 7.5%. These values also show good agreement
with the actual locations and sizes. The depths of the defects
were estimated by the artificial neural network. Each pixel of the
normalised phase image and the frequency is an input variable,
and the depth of the defect is an output parameter. The defect
depths that were estimated by the ANN are listed in Table 2. It is
noted that the maximum error of the ANN calculations is less
L. Junyan et al. / NDT&E International 45 (2012) 104–110
than 5%. These values also show good agreement with the
actual depth.
4.2. Specimen 2: rectangular Teflon insert of CFRP sheet face foam
sandwich
Fig. 8 shows the defects of specimen 2 detected using the
normalised phase image at the optimum modulated frequency of
0.042 Hz.
All of the rectangular defects that form the subsurface at a
depth of 2.0 mm are clearly distinguished from the normalised
phase image with different filtered coefficients b. The background
noise of the normalised phase image is critically reduced at higher
filtered coefficients b Z0.3, as seen in Fig. 8(c), and the contrast is
mainly enhanced at higher filtered coefficients. The subsurface
defects can be identified in the differential normalised phase
image shown in Fig. 8(e). In Fig. 8(f), the differential normalised
Table 2
Results of the ANN and errors.
Actual depth
h (mm)
Depth of ANN
AN
NN (mm)
((m
mm)
Error
Erro
or (%)
((%
%)
8
8
8
10
10
1.0
1.4
1.8
1.0
1.4
1.0035
1.003
35
1.4617
1.461
4617
461
4
1
1.
.77
772
7
7 6
1.7726
1
. 29
.01
9
1.0129
1.4651
0
0. 35
4.40
4.
4
.40
1.52
1
.29
1.29
4
.65
65
65
4.65
phase profile presents the location and boundary of the defect.
However, in the first-order differential normalised phase profile,
two maximum normalised phase points exist in the position
range from 140 to 180 mm, and the defect boundary is difficult
to distinguish from the first-order differential normalised phase
profile. The second-order differential normalised phase profile is
used to detect the defect boundary, and the zero value point
corresponds to the boundary of the defect in the second-order
differential normalised phase profile.
The defect classifier and the depth of the defect obtained from
the ANN are presented in Fig. 9. Two defects can be identified by
the defect classifier of the ANN, as shown in Fig. 9(a). In addition,
there are other defects visible in Fig. 9(a) that may be the CFRP
defect or manufacturing defects. The depth estimation of the ANN
is less than that of the actual depth. The estimation error reaches
approximately 25% for the second defect, and the size of the
defect is smaller in comparison with the differential normalised
phase profile in Fig. 8(f).The estimation error is less than 5% for
the first defect, and the size of the defect is similar to the
diffffer
di
eren
eren
enttiial process in Fig. 8(f).
differential
4 3.
4.
3. Specimen
Sp
pec
ecim
cim
men
en 3:
3 round
ro
oun
und Teflon insert of CFRP sheet face honeycomb
4.3.
0 shows
ssh
how
ws the
the
e defects
de
d
efe
fects of specimen
sp
Fig. 10
3 detected using the
no
orm
rmal
a ised phase
ph
has
ase
e image
im
ma
age
e at
at a modulated
mo
normalised
frequency of 0.1 Hz.
Th
Th
he
e entire
ent
ntire round
nd
d defects
defe
de
fects forming
for
orming the subsurface at a depth of
The
ht l:
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:/ 6
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w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
Diameter (mm)
109
te
Fig. 8. Normalised phase filtered images and differential normalised phase filtered images of specimen 2: (a) filtered coefficient b ¼ 0.1; (b) filtered coefficient b ¼0.2;
(c) filtered coefficient b ¼ 0.3; (d) filtered coefficient b ¼ 0.4; (e) differential normalised phase filtered image; and (f) defect location and boundary identification in Line IV.
Fig. 9. Defect classifier of specimen 2 by the ANN: (a) defect classifier and (b) depth distribution profile in Line V by the ANN.
110
L. Junyan et al. / NDT&E International 45 (2012) 104–110
Fig. 10. Phase and differential normalised phase filtered images of specimen 3: (a) normalised phase image; (b) differential normalised phase filtered image; and (c) defect
location and boundary identification in Line VI.
1.0 mm are detectable from the normalised phase image shown in
Fig. 10(a) and the differential normalised phase image shown in
dif
ifffe
ere
renenn
Fig. 10(b) at the modulated frequency of 0.1 Hz. The differenn is
is plotted
pllot
p
otte
t d in
in
tial normalised phase at line VI of the specimen
ions and boundaries
bounda
da
arriie
ess (sizes)
(siz
zess) can
can
an be
be
Fig.10(c), and the defect locations
differenti
tial
ti
al normalised
norm
no
rm
ma
alliisse
ed
d phase
p ase
ph
obtained and calculated from the differential
profile.
5. Conclusions
Acknowledgement
This
Th
is wo
w
ork
r was supported by the Chinese National Natural
This
work
S ie
Sc
en
ncce Foundation
Fou
und
ndatio
attio
ion under
under Contract no.
no 60776802, no. 51074208
Science
an
a
nd no.
no. 51173034,
51
5
117
117
1730
3034
34
4, the
the
he Fundamental Re
and
Research Funds for the Central
Univ
Un
iver
ersi
s ttiies
es u
un
nder
der C
de
ontr
on
trac
a t no. HIT.NSR
Universities
under
Contract
HIT.NSRIF.2009025, the 111 Project
(B0701
018)
01
8)) and
8
an
a
nd the
th
he
e Heilongjiang
He
H
eil
i on
ng
gjjia
jia
iang Provence
Provenc Project under Contract no.
(B07018)
GB
B06
0 A512.
GB06A512.
Re
R
effe
erenc
er
ces
e
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ht l:
tp +8
:/ 6
/w 41
w 18
w 3
.il 72
gw 6
el 985
ls
.c
om
antita
tative
ta
tat
e analysis
anallysis of the boundary,
bound
nd
dar
ary
y,,
This paper proposes the quantitative
face
e defects
defe
de
fect
cts by
by lock-in
lock-in thermograthermogrraalocation and depth of subsurface
o reduce
re
ed
du
uce
ce noise
noisse disturbance,
no
disturbance, and the
th
he
phy. The phase is normalised to
al equation
eq
qu
ua
attiio
on (PDE)
(PD
P E) model is used
d to
to
heat transfer partial differential
mage
ge
g
e noise.
nois
nois
no
isse
e. The
e.
Th
T
he differential normalnorm
malfilter the normalised phase image
em
mploye
y d to
to determine
determine the
e defect
def
efect
ised phase profile method is employed
e maximum,
ma
ax
xiim
mu
um, minimum and
and
nd zeros
location, boundary and size. The
fectt boundary
bou
und
ndarry and
and location
loca
ati
tion in the
points correspond to the defect
profi
file. An
fil
An artificial
arrttiificial neural
neurrra
al network
n
net
e work
differential normalised phase profile.
epth of
of the
th
he defect.
deffec
ect.
t The location
loc
oca
attiio
on and
nd
nd
is proposed to estimate the depth
ment with
with
ith the
it
the
he actual
accttu
ual
a value
valu
ue for
for
o all of
of
size of the defect are in agreement
thes
esse values
va
allu
ue
es are
are
re less
l ss than
le
n 10%.
10
0%.
the specimens, and the errors for these
mated by
y the
t e ANN
th
ANN
NN is
is close
clo
clos
cl
osse to
to the
The depth of the defect estimated
eel. The proposed
propo
posed
po
o d method
met
met
etho
h d is
is helpful
hel
elpf
p ul
actual value for ANSI 1045 steel.
def
e eccts
ts using
usiin
us
ng lock-in
loc
ock
ck-iin
n
in the quantitative analysis of subsurface defects
thermography.