Acoustic Emission Technique for Fracture Analysis in Wood Materials

7/25/2019 Acoustic Emission Technique for Fracture Analysis in Wood Materials

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Int J Fract (2015) 192:5770

DOI 10.1007/s10704-014-9985-x

O RIG IN A L PA P E R

Acoustic emission technique for fracture analysis

in wood materials

Frdric Lamy Mokhfi Takarli

Nicolas Angellier Frdric Dubois

Octavian Pop

Received: 19 March 2014 / Accepted: 16 December 2014 / Published online: 15 January 2015

Springer Science+Business Media Dordrecht 2015

Abstract Understanding the failure mechanisms of

construction materials, as well as their damage evo-

lution, constitute two key factors to improving struc-

tural design tools. Depending on the failure modes

to be highlighted and studied, several test methods

and analysis tools have been developed. One such

development, the acoustic emission technique (AET),

is an experimental tool appropriate for characterizing

material behavior by means of monitoring the frac-

ture process. Despite the widespread uses of acoustic

emission techniques to characterize and monitor thedamage evolution of composite materials, only a few

research studies have focused on using AET to char-

acterize the mechanical behavior of wood materials.

In the present work, the failure process in Douglas

fir under monotonic loading is studied by comparing

three experimental methods: force-displacement curve

analysis, acoustic emission measurements, and digi-

tal image acquisition. First of all, results show good

F. Lamy M. Takarli (B) N. Angellier F. Dubois O. Pop

GEMH, EA 3178, Universit de Limoges, 19300 Egletons,France

e-mail: [email protected]

F. Lamy


N. Angellier


F. Dubois


O. Pop


correlation and complementarities among the methods

employed. Second, analyzing acoustic emission sig-

nals by considering the event number and the cumula-

tive events yields interesting information on crack ini-

tiation and growth without the material. Moreover, an

additional analysis of acoustic emission data (involving

the determination of source locations and the study of

amplitude distributions)opens the possibilityto charac-

terize the fracture process zone which is a key damage

mechanism in wood materials.

Keywords Acoustic emission Fracture process

Wood Thermodynamic approach

1 Introduction

Renowned for its environmental benefits, wood is

widely used in civil engineering structures like timber

bridges and industrial buildings. As a natural material,

wood is prone to the presence of pre-cracks. In terms of

lifetime sustainability, constructive systems must take

into account the risks of crack growth to the integrity

of timber elements. Furthermore, the diagnostic of tim-

ber structures led to considering specific tools for the

monitoring of the cracking state. In this context, NDT

methods can give interesting alternatives.

Today, several scientific works propose NDT

methods in order to define the mechanical state

around a crack tip. Lets cite image analysis meth-

ods, which include digital image correlation techniques

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58 F. Lamy et al.

(Pop et al. 2011; Dubois et al. 2012). These methods are

effective in analyzing crack growth initiation and prop-

agation, though only brittle cases have been studied. In

reality, the fracture process in wood material is char-

acterized by a quasi-brittle fracture, thus necessitating

the development of specific tools to define nonlinear

damage in the crack process zone and to highlightingthe crack bridges phenomenon (Coureau et al. 2006;

Morel et al. 2005). None of these methods, despite

being based on energy approaches, can deliver a char-

acterization of the actual crack separation process and

process zone development.

This paper proposes an alternative method that is

based on AET and considered quite original among the

set of NDT methods. The potential for AET control in

NDT research on wood and wood materials has been

clearly demonstrated in the literature review. In the case

of fracture analysis, various damage mechanisms andloading tests are discussed: mode I fracture behavior,

shearing fracture, quasi-static tensile test, static three-

point bending test, static and fatigue torsional loading,

and Pin Forcing.

Berg and Gradin (2000) relied on AE monitoring

during wood compression to investigate the fracture

history, with special emphasis on its dependence upon

temperature, moisture content, strain and loading direc-

tion. These results mainly showed that the elastic mod-

ulus, compressive strength and cumulative number of

AE events all decreased with increasing temperature.Landis and Whittaker(2000) compared the energy

released by mode I crack propagation in wood with the

resulting AE energy. The energy comparison results

showed a good correlation. An investigation of mode

I fracture behavior of both softwoods and hardwoods

under the splitting test associated with AE measure-

ments was also reported by Reiterer et al. (2000).

The measured AE parameters included cumulative

counts, amplitude and frequency spectra. These results

revealed that the AE counts until reaching the maxi-

mum force are much higher for softwoods, thus sup-

porting the interpretation that softwoods behave with

more ductility and hence build a process zone contain-

ing many more microcracks. It was also shown that the

differences in macrocrack formation and propagation

may be visible in the shape of the cumulative AEcounts

and AE amplitudes.

Aicher et al. (2001) proposed tracing the damage

evolution in a spruce loaded in tension perpendicu-

lar to the fiber direction by means of AE analysis.

The two-dimensional burst source location in cross-

sectional slabs of boards utilized the full waveform

recording of AE signals, as monitored by six simulta-

neously triggered, multiple resonant longitudinal wave

sensors. During the test, a distinct burst location was

obtained in the range of 8090 % of the ultimate load,

coinciding well with the theoretically highest stressedareas of the fracture plane at brittle failure. In addi-

tion, the correlation of AE event rates with global strain

made it possible to trace damage evolution, especially

when events in confined areas were being observed.

Ando et al.(2006) examined the process of micro-

scopic shearing fracture by comparing AE characteris-

tics with the fracture surface observed under a scanning

electron microscope (SEM), in the aim of understand-

ing the deformation and fracture characteristics of old

wood. These results indicated that the number of AE

occurrences at low load levels was greater in the oldwood than in new wood. Moreover, the period over

which AE with small amplitudes were frequently gen-

erated was longer in the old wood than in new wood.

AE was used byChen et al. (2006) to monitor the

failure processes of hardwood and softwood test pieces

under static and fatigue torsional loading. In static tor-

sional testing, the acoustic activity prior to maximum

load suggests that some microcrack initiation is taking

place before visible cracking in both the hardwood and

softwood. This finding is corroborated by microscopic

observations. Hardwood produced more AE countsthan softwood during testing, and the grain angle of test

pieces influenced the total AE counts. During torsional

fatigue fracture, increased acoustic activity indicates

the onset of microcrack formation. Fatigued test pieces

produced a higher number of total AE counts during

fracture than the static test pieces, provided however

that the twist angle exceeds a minimum value.

Brunner et al.(2006) performed quasi-static tensile

tests on two different types of laminated wood spec-

imens monitored with AET. Various AE signal para-

meters point to an exponential increase in the cumu-

lative AE, and AE rate curves were observed versus

either time or load. The time constant of this exponen-

tial increase depends on the material, which is anal-

ogous to the AE rate behavior observed for various

glass fiber-reinforced, polymer-matrix composite spec-

imens under similar tensile load conditions. To obtain

a basic knowledge of the fracture toughness of Sugi

specimens, Ohuchi et al. (2011) conducted fracture

toughness tests with: six types of compact tension (CT)

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Acoustic emission technique for fracture analysis in wood materials 59

specimens (RL, TL, LT, RT, LR, and TR), a different

load direction, and crack progression for the anisotropy

consideration. The AE generated during the test were

measured, and the correspondence of fracture tough-

ness and AE was examined. Results revealed that the

peaks in the average AE signal were admitted into the

fracture process of the TR specimen, and these corre-sponded to the position of the late wood part progress-

ing in the crack. The AET is therefore a promising one

for detecting the fracture process of the late wood part

in a TR specimen.

Svobodova and Svoboda(2012) performed experi-

mental work on AE as a means of evaluating damage

processes in wood material. Various types of mechan-

ical loadings (three-point bending test, CT specimen

measurements and Pin Forcing) were proposed for

three species of coniferous wood specimens. The inves-

tigated wood species (fir, spruce and pine) have showndifferent material characteristics, in correspondence

with the level of AE activity. Furthermore, the posi-

tion of local crack onset in the wood specimens can be

detected at the least by a two-channel AE system. A

study to identify the sources of AE generated during

the wood specimen static bending test was presented

byVarner et al.(2012). Information on the wood struc-

ture, wood failure behavior and a computer-generated

finite element method (FEM) simulation of the static

bending test were all used to estimate the power of indi-

vidual AE sources. Strong AE sources are expected inthe specimen in two key areas: under the upper central

support (throughout the bending test run), and in the

tension portion of the specimen centered on the lower

baseline (at the final fracture time).

In the works presented above, a global approach

is investigated in order to confirm the advantages of

acoustic emissions (AE) in the signature analysis of

crack growth within wood elements. Various experi-

mental processes are presented and compared to high-

light the effective monitoring provided by the AE tech-

nique. After this first literature review in terms of intro-

duction, Sect.2presents the experimental set-up based

on a double cantilever beam sample and both AE and

vision devices. All experimental protocols, including

AE calibration and crack tip location, are developed.

The image analysis is performed using the edge detec-

tion technique. The AE results, with respect to ther-

modynamic considerations, are discussed in the last

part of Sect.3.In coupling with the samples mechan-

ical behavior during the crack growth process, the AE

approach is employed to complete a global thermody-

namic assessment that allows both separating the dis-

sipated energy and validating crack growth behavior.

This analysis leads to a conclusion on replacing the

image analysis technique by the AE method. Moreover,

accordingto a probabilistic signalanalysis, understand-

ing of crack growth process is completed by an attemptto detect the process zone before the crack tip and then

crack bridging along the crack lips.

2 Experimental set-up

In this section, we briefly describe wood samples and

the measurement techniques used to characterize the

failure process in Douglas fir under monotonic loading.

2.1 Sample preparation

The experimental protocol is based on using a double

cantilever beam specimen. The chosen specie is a Dou-

glas fir. Four samples have been machined in a radial-

longitudinal (RL) configuration, Fig. 1. All samples

are conditioned in a climatic chamber in which tem-

perature and relative humidity are regulated at 20 C

and 40% RH, respectively, corresponding to an aver-

age moisture content level around 9.8%. After con-

ditioning, a pre-crack with an initial length a0 equalto 50 mm is performed along the grain direction with

a band saw (3mm thick). The geometry and loading

symmetry allow assuming an open mode configuration

according to a particular choice of grain alignment with

the crack.

2.2 Mechanical loading and image acquisition

The Zwick electromechanical testing machine, with

a 50 kN load capacity, is controlled in displacement,which allows forcing stable crack growth during the

experimental test (Fig.2). This load is applied to the

specimen by use of shafts pushed into holes drilled

through the top and bottom cantilevers. Specimens are

tested at a constant displacement rate of 0.5 mm/min.

Synchronized with the testing machine, an 8-bitcharge-

coupled device camera measures the displacement

fields. Thanks to this full-field optical method, the dis-

placement evolution on the specimen surface could be

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60 F. Lamy et al.

Fig. 1 Double cantilever

beam specimen

15mm

80

mm

R

LT

oa 50mm= 3mm

Fig. 2 Experimental testing machine

recorded throughout the test. Also note that the image

rate of the camera is set at 2fps.

2.3 Location of the crack tip by image analysis

The fracture analysis procedure requires localizing the

crack tip advance during a mechanical test. A num-

ber of methods is available today. In considering that

all tests are recorded using a CCD camera, a classi-

cal visual technique allows for easy location of thecrack tip. The main disadvantage herein is the lengthy

processing time; moreover, the visual display does not

enable accurately localizing the crack tip in the frac-

ture process zone. Other more effective techniques

are based on a digital image correlation (DIC) step

that relies on both the XFEM principle and finite ele-

ment couplings(Rthor et al. 2009;Pop et al. 2011).

These methods offer a highly accurate crack tip loca-

tion and a good definition of the mechanical fields in

the crack tip vicinity. On the other hand, they present

the difficulty of having to analyze all images. In this

work, we propose an alternative between the visual and

DIC techniques based on an edge detection method.

This technique makes use of the gray scale analysis

of an image by detecting major changes in intensity

(Gonzalez et al. 2009). As shown in Figs.3 and 4, by

combining a simple derivativefunction and a correction

of intensity thresholds, the technique allows amplifying

crack appearance.

2.4 Acoustic emission equipment

During the test, AE event signals are monitored and

recorded using a Euro Physical Acoustics (EPA) sys-tem:

Four piezoelectric transducers (miniature sensors

Nano30), with a characteristic band extending from

125 to 750kHz and a 140 (and 300) kHz resonant

frequency, are mounted on the specimen (Fig. 5).

The transducers are coupled to the specimen with

silicon grease in order to avoid any loss of acoustic

signal at the transducer-sample interface.

A pre-amplification of the AE signals is provided

by four preamplifiers (IL40S model) with a 40-dB

gain set.

AE signals are sampled at 20MHz and filtered with

an amplitude threshold of 40dB. It is apparent that

the detected events depend on the value of this

threshold. The peak definition time (PDT), hit def-

inition time (HDT) and hit lockout time (HLT) are

set at 40, 200 and 300s, respectively.

A signal conditioner and software, that allow

recording the AE features in a computer, are intro-

duced for further analysis.

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Fig. 3 Edge

characterization : gray level

threshold

edge pixels

Spatial distribution x

( )f x

( )f x

( )f x

( ) thresholdf x >

Fig. 4 Crack tip location

using the edge detection

method

Zone of Interest

Image analysis

Crack tip location

Crack length

Sensor 1 Sensor 2

Sensor 3 Sensor 4

Fig. 5 Sample instrumented with acoustic emission sensors

2.5 Acoustic emission calibration

In practice, many different ways of AE locating can

be used to obtain the required resolution in one, two

or three dimensions. The most appropriate technique

will depend on: the experimental objective, the required

solution, and the geometric shape(Grosse and Ohtsu

2008). In the literature review, reported byKawamoto

and Williams (2002), it was shown that the wave veloc-

ities for wood are 4 103 to 5 103 m/s for the longi-

tudinal direction, 1.5 103 to 2 103 m/s for the radial

direction, and 103 to 1.5 103 m/s for the tangential

direction. Consequently, conventional AE source loca-

tion techniques, which assume an isotropic velocity,

cannot be easily used for wood.

Most location methods are based on evaluating time

differences between wave arrivals at different sensors.

The Time Of Arrival (TOA) of AE waves at sensors

can be detected as the first threshold crossing by an AE

signal, or as a time of peak of the AE signal, or as a

time of first motion. The TOA can be evaluated sep-

arately for each wave mode (longitudinal, shear, sur-

face, etc.). Another parameter required for the time

difference location method is effective velocity; this

parameter can be established experimentally with or

without considering different wave propagation modes.

When propagation modes are not separated, the error in

AE source location evaluation can be significant. The

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62 F. Lamy et al.

detection of different wave mode arrival times sepa-

rately and the evaluation of their velocities can sig-

nificantly improve location accuracy. Nevertheless, the

detection and separation of different wave modes is

computationally expensive and inaccurate in the case

of complex geometries or under high and variable back-

ground noise conditions (Muravin 2009).In order to overcome difficulties related to wood

properties (heterogeneity, grain orientations, orthotropy,

etc.) and the geometric shape of the tested samples, a

pseudo-linear AE source location combined with a spe-

cific calibration procedure is adopted for this study. The

wave velocity determination and the crack tip location

are separated.

2.5.1 Effective wave velocity and 1-dimensional

location of the AE sources

In this work, the AE calibration is performedwith a post

mortem sample. A one-dimensional location of the AE

sources is performed based on both arrival time dif-

ference and effective wave velocity in the studied sam-

ple. Wave velocity tends to be experimentally evaluated

by artificially generating AE at known distances from

sensors. Hence, the average effective AE wave veloc-

ity (V = 5,350 m/s) was evaluated by the conventional

Pencil Lead Break (PLB) performed on the upper face

of the test sample between sensors 1 and 2, Fig. 6. The

device provides an aid to simulating an AE event usingthe fracture of a brittle graphite lead in a suitable fitting.

This fracture generates an intense acoustic signal, quite

similar to a natural AE source that the sensors detect as

a strong burst. The solution to the linear location of the

AE source, between the two sensors, is given by Eq. 1.

Sensor 1 Sensor2

oa

d

Crack growth way

D

Fig. 6 Conventional one-dimensional PLB protocol

Sensor 1 Sensor 2

1l 2l

Event location

oa

Crack growth way

aPLB

aLinear -AEcalibration

correction

aP-Linear -AE

Fig. 7 PLB test on the crack path

d =1

2 (D T V) (1)

T designatesthe Time Difference Of Arrival(TDOA).

As indicated in the expression (1), the AE wave veloc-

ity allows optimizing the relationship between relative

position of the source and sensors. Nevertheless, thecalibration protocol, presented in this work, circum-

vents the necessity to know with accurate its value.

2.5.2 Crack tip location

In order to adapt the PLB method to the crack path, we

propose two additional protocols. Firstly, the PLB test

is performed on the fracture surface simulating a noise

source located at the crack tip vicinity, Fig. 7. The PLB

response allows the calibration or the projection of the

signal source, induced by the crack tip advance, to thesample surface. Secondly, in order to take into account

the crack tip correction in the sample thickness, three

paths are selected as shown in Fig.8.

Lets note that following this calibration proce-

dure, the location of AE sources on the fracture sur-

faceaPLB can be correlated with the source position

calculated by the one-dimensional AE location algo-

rithm aLinear-AE. In final, the calibration curves of

AE sources are shown in Fig.9. A quasi-perfect corre-

lation(R2 = 1)can be observed between the PLB test

locationaPLB and the equivalent linear AE locationaLinear-AE(aPLB = a aLinear-AE+ b).

The linear regression calibration factors depend

however on the fracture surface thickness. As shown

in Table 1, it should nonetheless be pointed out that

despite this three-dimensional effect, the calibration

curves are very similar for both the central axis and

the 3-axis average.

Considering the 3-D problem and its symmetric

solution, the mean deviation in the pseudo-linear AE

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Fig. 8 Correction of the

PLB method with the crack

tip location in the thickness

0

20

40

60

80

100

120

0 20 40 60 80 100 120

along the front edgeaxis

alongthe central axis

along the rearedgea xis

average position along3 axes

linear (averageposition along3 axes)

( )Linear AEa mmLinearAE location

LocationofthePLB

aPLB(mm)

Fig. 9 Calibration curves for the pseudo-linear AE source

location

Table 1 Linear regression factors for the pseudo-linear AE

source location

aPLB = a

aLinear-AE + b

a b(mm) R2

Sample 1 Front edge axis (a) 1.58 33.40 1.00

Central axis (b) 1.63 36.31 1.00

Rear edge axis (c) 1.69 38.97 1.00

Average of 3-axes 1.63 36.18 1.00

Sample 2 Central axis (b) 1.56 31.40 1.00

Sample 3 1.61 31.65 1.00

Sample 4 1.71 33.99 1.00

source location (aP-Linear-AE) and the location error

induced by considering an average calibration curve

are presented in Fig. 10for each PLB position. Dis-

persion in acoustic location is more significant closer

to the transducers (1.13mm) and the average value

(0.72mm) is of the same magnitude order in com-

0

20

40

60

80

100

120

0 20 40 60 80 100 120

aP-L

inear-AE(mm)

aPLB (mm)

Fig. 10 AE location error resulting from the pseudo-linear

calibration

parison with the pencil lead break positioning error

(0.50mm).

2.5.3 Source amplitude and wave attenuation

It is commonly accepted that the AE signal depends

on the combined effects of specimen dimension, spec-

imen/transducer geometry, transducer location and

degree of attenuation, especially in the tangential and

radial directions. It is important herein to consider thetransducer orientation in relation to the AE wave propa-

gation direction. Piezoelectric transducers are typically

much more sensitive to vertical transducer vibration

than to horizontal vibration. Transducer frequency is

also an important parameter. In wood-based materials,

thematerialattenuation is about one order of magnitude

greater than geological materials and twoorders greater

than metals. Since material attenuation increases expo-

nentially with frequency, the usable upper frequency

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64 F. Lamy et al.

oa

Sensor 1Sensor 2

Emitter sensor

Sensor 1 Sensor 2

AST position

oa

Crack growth way

symmetric line between two sensors

symmetric position

L1,1

aAST

aAST-s

L2,1 L1,2

L2,2

(a)

(b)

ASTa

: angle between the grain direction and the AE wave path

Fig. 11 AST method for attenuation measurements.a Experi-

mental AST protocol,bschematic representation

level for transducers on wood-based materials is about

100200 kHz. Within this frequency range, attenua-

tion along and across the wood grain is about 30 and

200 dB/m, respectively. The effects of wood density

and moisture content on attenuation have not been

clearly determined yet still appear to be insignificantin comparison with grain angle.

Attenuation measurements are easily conducted

with a simulated AE source. The most widely used

simulated AE source is the breaking of a pencil lead

pressed against a structural member, as illustrated in

the pseudo-linear location procedure (Figs. 7, 8). A

good technique is required in order to generate high

reproducibility of the resulting stress wave. In this

study, the conventional PLB method has been replaced

by using the Auto Sensor Test (AST) protocol, as

shown in Fig.11. AST provides an automated means

of pulsing and receiving a simulated acoustic emis-

sion burst, which is then coupled to the structure by

controlling the acoustic emission level. This method

creates a short-duration and localized impulse, like

the breaking of a pencil lead. To develop the attenu-

ation curve, an impulse at 99dB is performed several

times at each of several distances on the fracture sur-

face. The amplitudes for each distance are averaged

and, subsequently, these average amplitudes are plot-

ted versus distance.On the attenuation curve, amplitude

is plotted on the Y-axis using the dBae decibel scale,

on which each 20 dBae increment represents a tenfold

increase in the signal peak voltage. The dBae scale

is universally accepted and very convenient because,

being logarithmic, it condenses the very wide range

of AE signal amplitudes. dBae provides a logarith-mic measurement of AE signal amplitude referenced

to 1V.

When AE is used to determine the source location

of an active defect, a single event can be detected on

several channels, producing a hit on each one. The AE

event amplitude is then considered as the amplitude

of the first recorded hit from the considered source.

Next, the AE source amplitude is calculated using

the attenuation curve. In our case study, the event

occurring at the left (right) of the axis of symme-

try between the two transducers will be assigned theamplitude of the hit recorded by the first (second) sen-

sor.

The symmetrical approach to the calibration proce-

dure is given in Fig. 11b. For each crack length incre-

ment aAST simulated by the AST method, a sym-

metrical position called aAST-s is defined. The dis-

tance between the two sensors is known, as is the sam-

ple height. Distances L1,1 = L2,2 and L1,2 = L2,1can then be easily expressed. The attenuation curve

can thus be plotted separately for each sensor or by

considering the average amplitude of the receivedsignals (hits 1 and 2). Figure 12 reports the ampli-

tude decreases with respect to AST position on the

fracture surface. These results show a similar linear

relationship (Asensor = 0.30 aPLB + 86.11),

with a correlation coefficient of 0.94 for the average

curve.

The amplitude reductions between the emitter (AAST)

and the receptor (Asensor) have also been plotted in

Fig. 13, according to the distance between AST sources

and sensor positions. With a 0.91 correlation factor,

this curve yields an evaluation of the global atten-uation factor TOT = 404 dBae/m), which depends

on various parameters: the material attenuation coef-

ficient (dispersion, scattering and eventually dissipa-

tion) affected by grain directions and grain angles; cou-

pling media effects; transducer frequency; and trans-

ducer geometry and location. Use of the attenuation

curve is critical to evaluating the amplitude decrease of

the acoustic wave when traveling between source and

reception.

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0

20

40

60

80

100

120

0 20 40 60 80 100Asensor(dBae)withASTsourceof99dBae

Location of the AST impulse ( aAST) on the

fracture surface (mm)

Sensor 1

Sensor 2

Fig. 12 Amplitude decreases versus AST test position on the

fracture surface

0

20

40

60

80

0

20

40

60

80

0 20 40 60 80 100 120

()

Amplitudereduction(dBae)

Distance betwen the AST impulse and the sensors

position (mm)

Sensor 1

Sensor 2

Fig. 13 Amplitude reductions between the AST source and sen-

sors

3 Results and interpretation

3.1 Acoustic emission analysis

For a good understanding of the results interpretation,

only one sample is discussed. Nevertheless, all speci-

mens present same characteristics. Acoustic emissionactivities have been shown to relate to different stages

of material fracture tests. Figure 14ad indicate, for

each sensor (Sensors 1 and 2), the force loading and

changes in AE activity versus displacement during a

mode I fracture test. Figure 14d displays changes in the

cumulative AE event numbers, as evaluated by the pro-

posed pseudo-linear location procedure. Similar results

are observed between 12 sensors and 34 sensors. So,

in this paper, let us focus only on sensors 1 and 2.

In considering the recorded AE hits during the frac-

ture test, significant AE activity is observed before

the maximum load is reached. The beginning of

this activity coincides with the time when the force-

displacement curve deviates from linear elastic behav-

ior. This deviation is probably due to the creation

of microcracks, which form a process zone aroundthe notched tip where the stress tends to concentrate.

Thereafter, propagation of the preexisting crack can be

considered as being either stable or unstable. Results

also reveal that the first sensor, which is near the

notched tip, exhibits higher AE activity during microc-

rack creation. It is observed that microcrack formation

started at about 92% of maximum force, before a grad-

ual increase in AE activity.

By comparing the changes in AE hits (detected and

measured signal for each channel) and AE events (a

located material change giving rise to acoustic emis-sion), it is noted that only a small part of the total

recorded hits from the 12 sensors group location are

located. For each sensor, the ratio AE events/AE hits

depends on the distance that crack has propagated. At

the end of the fracture test, around 30% of AE hits

are simultaneously recorded by sensors 1 and 2 and

localizedin accordancewith the effective wave velocity

(Fig.14d). In all likelihood, a proportion of the non-

located hits can be caused by external noise, such as

that caused by the loading system. The use of effective

wave velocity without separating the wave propagationmodes might also cause a loss of information in the AE

event location.

3.2 Fracture process zone and crack tip location

Taking the displacement as a common base, Fig. 15

juxtaposes the crack tip location by image analysis and

the localized AE events classified by their amplitude.

Propagation of the crack front is in good agreement

with results obtained with image analysis. We can note

that the crack propagation, given by the image analy-

sis method, corresponds to AE amplitudes higher than

90dBae.

Figure 16a recalls the evolution of the AE events

during the test. Each value corresponds to the accu-

mulation of AE events during 1.4 s. If we consider the

experimental time of 160 s (corresponding to a max-

imal AE activity during the total cracking test), the

location of AE events put in evidence a relative crack

length location around 45mm, see Fig. 16b. A peak

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66 F. Lamy et al.

0,E+00

2,E+04

4,E+04

6,E+04

8,E+04

1,E+05

0

100

200

300

400

500

600

700

800

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

CumulativeAEhitsandcumulativeAEeve

nts

Load(N)

Displacement (mm)

Load

AE events (sensors 1-2)

AE hits (sensor 1)

AE hits (sensor 2)

0

100

200

300

400

500

600

0

100

200

300

400

500

600

700

800

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

AEh

its

Load

(N)

Displacement (mm)

Load

AE hits (sensor 2)

0

100

200

300

400

500

600

0

100

200

300

400

500

600

700

800

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

AEh

its

Load

(N)

Displacement (mm)

Load

AE hits (sensor 1)

(a) (b)

(c) (d)

0

40

80

120

160

200

0

100

200

300

400

500

600

700

800

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

AEevents

Load(N)

Displacement (mm)

Load

AE events (sensors 1-2)

Fig. 14 Force-displacement curve and AE activity.a AE hits for sensor 1, b AE hits for sensor 2, c changes in the AE events count

(number),d Cumulative AE events and AE hits

0

20

40

60

80

100

120

140

0,5 1 1,5 2 2,5 3

Crackposition

a(mm)

Displacement (mm)

Visual crack position

60-70 dBae

70-80 dBae

80-90 dBae

90-100 dBae

>100 dBaeAE-Eventsamplitude

Fig. 15 Visual crack tipadvance and AE events versus displace-

ment

of AE activity can be noticed and then correlated with

the crack tip. The location of this peak is obtained by

fitting experimental response by a Gaussian law. The

Gaussians pick is assimilated as the crack tip position.

The left part can represent crack bridging. The right

part can be assimilated as the process zone.Lastly, by repeating this operation for each given

experimental time, we obtain the crack tip advance by

AE analysis, as depicted in Fig.17.A good correspon-

dence between the crack tip positions can be concluded.

3.3 Thermodynamic view

A thermodynamic approach allows illustrating the sep-

aration energies induced in the crack growth process.

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0

200

400

600

800

0 80 160 240 320 400

AEeven

ts

Experimental time (s)

0

20

40

60

80

0 20 40 60 80 100 120

AEevents

Relative crack length (a-a0) (mm)

Gaussian Law

(a) (b)

Fig. 16 Crack tip location by AE analysis.aAE events time distribution over the entire duration of the fracture test,bAE events spatial

distribution at the experimental time of 160 s (i.e. 1.4 s time steps)

0

20

40

60

80

100

120

140

60 100 140 180 220 260 300

Cracktipposition(mm)

Experimental time (s)

Visual crack tip location

AE crack tip location by sensors 1-2

Fig. 17 Comparison between visual and AE crack tip location

results

The first thermodynamic principle is based on the sep-

aration of external work Wext in terms of a released

energyUe (elastic energy) and a dissipated energy Ws ,

which at the same time correspond to the dissipationinduced by the crack tip advance on new surface cre-

ations and the process zone development in a damaged

and nonlinear zone. Lets also take crack bridging phe-

nomena into account in this dissipation. At this stage

of the study, we can assess a global energy dissipa-

tion. Under these conditions, the first thermodynamic

principle is written as follows:

Wext = Ue + Ws (2)

A

Ak

ForceloadingF

Displacementu

%

Fig. 18 Load-displacement curve and energy separation

For all unloading phases, the crack is assumed to be

closed once again without any particular interference

regardless of the crack length (i.e. no fibers on the crack

lips). Figure 18 shows the load-displacement curve and

corresponding energy separation technique.

For any experimental point A, the corresponding

displacement and force loading are then uA and FA,

respectively. The external work is defined as follows:

Wext =

uA

0

F(u) du (3)

Lets now introduce the apparent stiffness kA, such that:

kA =FA

uA(4)

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68 F. Lamy et al.

0

200

400

600

800

1000

0

200

400

600

800

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

Energ

y(mJ)

External

load(N)

Displacement (mm)

Force-displacement curve

External work Wext

Released energy Ue

Dissipated fracture energy Ws

Fig. 19 Energy separation

The released energy can be defined as:

Ue =1

2 kA u

2A (5)

Finally, the total energy dissipation is written as:

Ws =

uA

0

F(u) du 1

2 kA u

2A (6)

The energy separation, i.e. Eq. (2), is illustrated in

Fig.19.

According to an energy release rate concept, the

energy release rate Gexpresses the energy loss inducedby the crack tip advance (on a created crack surface).

With the sample thickness being called b, the energy

released is correlated with the dissipated energy Wsand

crack lengtha as follows:

G =1

bWs

a(7)

Figure 20 presents the energy dissipation versus

crack length. Ws is calculated using Eq. (6). The rel-

ative crack length is obtained by both image analysis

and acoustic location. The relation between the dissipa-

tion energy versus the relative crack length seems to belinear with a good correlation coefficient

R2 >0.99

,

such as:

Ws = 6.54 (a ao) for visual crack location (8)

Ws = 6.26 (a ao) for AE crack location (9)

According to Eq. (7), these results allow assuming a

simplified crack growth law. During the crack growth

process, we note:

G = Gc (10)

0

100

200

300

400

500

600

700

800

0 20 40 60 80 100 120 140

DissipatedenergyWs(mJ)

Relative crack length (a-a0) (mm)

Visual crack location

AE crack location

Fig. 20 Dissipated energy versus crack length

Relative crack length ( )oa aDissipatedenergyWs

1

2

Fig. 21 Process zone and the crack bridging process

For a thickness sample of 15mm, the Eq. (7) allows

the estimation ofG cassimilated to fracture resistance,

which appears to be constant:

Gc = 436 J/m2 for visual crack location (11)

Gc = 417 J/m2 for AE crack location (12)

Although the dissipated energy versus crack length

curve seems to be linear, a zoom of the Fig. 20 nonethe-

less seems to show a number of specific scenarios, such

that:

The experimental curve shows greater dissipation,

which could be explained by the formation of a

process zone beyond the crack tip. This dissipationcan be interpreted as localized damage increasing in

the crack tip vicinity (see Fig.21, Zone No. 1).

Theexperimental curve presents smaller dissipation,

which could be interpreted by the crack bridging

process preventing or impeding crack tip advance

(Fig.21, Zone No. 2).

In the research on fracture process zone, the decou-

pling of damage evolution, crack tip advance and crack

bridging represents a real scientific problem. The AE

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analysis seems to offer a better understanding of the

quasi-brittle behavior by localizing microcracks before

visual or camera detection.

4 Conclusion and outlook

In this study, a double cantilever beam specimen and

AE measurements were used to study the open mode

behavior of Douglas fir wood samples. A pseudo-linear

location of AE sources was derived with a specific cal-

ibration procedure. The experiment showed that crack

initiation and crack growth detected by AE activities

is in a good agreement with the image analysis results.

The proposed AE calibration takes into account the

crack growth in the fracture plan. This technique seems

to be more efficient with an average error of 0.72 mm

for the source location. Moreover, the image acquisi-tion is performed at fixed time while AE is recorder in

real time and only when the sample presents a crack

activity.

For future work, this experimental investigation has

taught us that use of the location approach seems to

be a very promising method for investigating fracture

mechanisms, such as the process zone or the crack

bridging. Accordingly, it will be necessary to pro-

vide more sophisticated analysis of AE data (including

energy distribution, a study of mechanisms for indi-

vidual events and the investigation of emission eventfrequency characteristics). In addition, the coupling

and cross-referencing between thermodynamic mod-

els and the AE approach should open new paths in

the separation process between energy dissipation due

to the crack growth process, process zone develop-

ment and dissipation induced by viscoelastic proper-

ties. This would be a very important step if the AE tech-

nique can allow simultaneously defining crack growth

kinetics and its thermodynamic balance. This technique

could then be used for monitoring timber structures

in total autonomy without requiring any specific crack

detection gauges. However, this approach will need the

development of a specific calibration protocol like, for

instance, an auto-calibration method.

A second way consists on the introduction of mois-

ture content effects on the crack growth process. In the

literature (Dubois et al. 2010), it is shown that mois-

ture content induces an increase of the ductility and the

critical energy release rate. The fracture process zone

seems to play a most important role in this case. Under

these conditions, the AE technique needs an adaptation

in order to take into account moisture content gradients

in the sample and in the crack tip region. With these

same conclusions, an auto-calibration technique must

be developed for heterogeneous moisture content field

in the crack tip location problem and in the more global

thermodynamic approaches.

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