On the characterization of continuous fibres fracture by quantifying

acoustic emission and acousto-ultrasonics waveforms

Y.Z. Pappas, A. Kontsos1, T.H. Loutas, V. Kostopoulos*,2

Applied Mechanics Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 00 Rio Patras, Greece

Received 22 October 2003; revised 13 November 2003; accepted 14 November 2003

Abstract

The aim of the present work is the classification of the characteristics of elastic waves, which are generated by fibre failures during quasi-

static tensile fibre bundle tests and captured by the use of acoustic emission (AE) method. In addition, elastic waves generated out of the fibre

bundle gauge length and propagated through the bundle at different stages of the loading process using an acousto-ultrasonic (AU) technique

are also classified. To this target, a large number of tests were conducted on organic, ceramic and carbon fibre bundles according to DIN

53942. An in-house developed analysis and quantification methodology of the captured AE and AU waveforms is proposed in order to

identify the frequency content of the fibre failure event, to characterize the medium of propagation and to investigate the effect of the

acquisition system on the monitored signals. In fact, the application of the proposed analysis on the results of the conducted mechanical tests

leads to the development of a useful database, concerning the ranges of AE features and the representative sets of frequency values that

correspond to fibre fracture. Furthermore, the proposed database offers valuable knowledge for the role of material parameters, such as fibre

structure and properties, on the characteristics of the recorded waveforms, constituting in this way a valuable tool that enables a better

understanding of elastic waves initiation and propagation through continuous fibres.

q 2003 Published by Elsevier Ltd.

Keywords: Acoustic emission; Acousto-ultrasonics; Database; Wave propagation; Fibre failure

1. Introduction

Several non-destructive inspection methods are currently

used as valuable and reliable tools for the characterization of

the structural integrity and the identification of the damage

mechanisms of complex material systems and structures,

which are subjected to different loading conditions. In the

case of continuous fibre reinforced composites, the fracture

of single as well as multiple fibres is strongly correlated to

the final failure of the structural component [1]. For these

complex materials, it is the integrity of the reinforcing phase

that determines the capability of the internal micro-structure

to withstand the external loads. Thus, the ability to identify

and quantify the failure of reinforcing fibres during the

loading process could provide valuable information

concerning the remaining life of a macro-structure.

In general, the genesis of this type of destructive events is

accompanied by the release of a considerable amount of

elastic energy, part of which propagates through the other

intact fibres of the reinforcement phase and/or through the

surrounding matrix of the composite structure. The

identification of an electrical signal, captured by a piezo-

electric transducer, as the result of fibre breakage offers

great advantages in the health monitoring process of the

overall composite structure. However, this result could not

be achieved using a straight-forward approach, since the

captured electrical signals (waveforms) contain information

coming not only from the source of the event but is also

affected by the medium that propagates the elastic energy,

the propagation path [2], the statistical nature of the physical

phenomenon and the system used for the acquisition [3].

Thus, any effort to extract knowledge about the elastic

waves that correspond to fibre failures should be based on

well-controlled testing conditions and the characterization

of the source of the failure event, under a statistical and

multi-dimensional analysis scheme.

In the present work, the characteristics of the elastic

waves generated as acoustic emission (AE) activity by

0963-8695/$ - see front matter q 2003 Published by Elsevier Ltd.

doi:10.1016/j.ndteint.2003.11.004

NDT&E International 37 (2004) 389–401

www.elsevier.com/locate/ndteint

1 Present address: Department of Mechanical Engineering and Materials

Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.2 Institute of Chemical Engineering and High Temperature Chemical

Processes/FORTH, Platani Rio, Patras, Greece.

* Corresponding author. Tel.: þ30-2610997234; fax: þ30-2610992644.

E-mail address: kostopoulos@mech.upatras.gr (V. Kostopoulos).

the failure of various types of fibres are classified in the time

and frequency domain. To this aim, a large number of quasi-

static mechanical tensile tests were conducted on commer-

cially available and extensively used fibre bundles, such as

Al2O3, carbon, SiC, Kevlar, glass and polyethylene, using a

special testing device according to DIN 53942 [4]. The

classification of the AE activity is achieved adopting

efficient de-convolution methodologies and results in a

database that characterizes the ‘signature’, in time and

frequency domain, of each fibre type failure. This approach

establishes a new methodology for the characterization of

the source of the failure event and the effect of the

propagation path.

2. Methodology for database development

The idea of a database development with representative

values of characteristic AE features corresponding to

several failure phenomena is not a new issue, since many

researchers have given this kind of information for specific

testing conditions, material structures and damage mechan-

isms before. A useful discussion concerning this aspect is

whether these values can be used to evaluate the response of

the same material in different structures and testing

conditions. Thus, the real question that arises is whether a

developed database created by information extracted from a

micro-scale approach (single filament, fibre bundle and

mini-composite tests) is applicable to the macro-scale

(plates, big structures, etc.) and vice-versa. In fact, many

efforts have been made towards this direction. Hamstad [5]

worked in single and multiple Kevlar 49 filaments tensile

tests using AE, and proposed that these results could be used

in a macro-scale approach for fibre failure identification,

while Giordano [6] assumed that there is only one fibre

failure mechanism in polymer-composites, enforcing

the same idea. The latter standardized the AE signatures

of single fibre failures and proposed a time/frequency

analysis, which can be applied to any captured waveform

from materials testing with more complex failure modes. In

2001, Pappas [7] proposed a complete scheme of micro-to-

macro scale utilization of the AE findings in composites, by

creating a database for several damage mechanisms.

Finally, in 2002, Ni [8] worked on the investigation of

model composites fracture with single carbon fibre, and

proposed a stage-by-stage approach for the given problem.

Probably, the most effective methodology to solve the stated

problem is to work on the AE waveforms, in time and

frequency domain, assuming that, in micro-scale, matter

vibration is related to eigenmodes, and that the classical

frequency domain analysis techniques (Fourier transform

(FT), dispersion curves, etc.) are applicable. This approach

is called modal AE, and is based on the assumption that the

most dominant eignemodes of the micro-structure are

present during any kind of AE activation and acousto-

ultrasonic (AU) response. Examples of this approach are

given by Prosser [9] and Mizutani [10].

An important issue in the analysis of the AE waveforms

and the efforts to construct a valid and useful database is the

ability to characterize efficiently the source event of each

captured waveform. In the case of fibre bundle testing,

source events could be single fibre failures, sliding friction

between fibres, etc. However, the propagation of an emitted

elastic wave from the source to the sensor and then to the

recording system is affected, in time and frequency domain,

by a set of external factors, such as the propagation medium

(properties, damage state, attenuation), the acquisition

system (sensors, acoustic coupling, pre-amplifiers, record-

ing system) and the boundary conditions (gripping/support-

ing apparatus, etc.). Thus, in order to create a valid database

for the evaluation of each waveform, it is important to know

the contribution of these factors in the overall response.

Nomenclature

AE acoustic emission

Amp amplitude

AvF average frequency

AU acousto-ultrasonic

Co counts

CNTP counts to peak

d fibre diameter

Dur duration

Di damage set of fibre i

Di;j common damage set between fibres i and j

E11 modulus of elasticity in loading axis

FFT fast Fourier transform

FT Fourier transform

GL fibre bundle gauge length

HDT hit definition time

HLT hit loc-out time

RiF rise frequency

PAC Physical Acoustics Corporation

PDT peak definition time

Pi propagation set of fibre i

Pi;j common propagation set between fibres i and j

ReF reverberation frequency

RT rise time

Sy ‘system’ set

STFT short time Fourier transform

TE true energy

TH feature extraction threshold

THA threshold/amplitude

UTS ultimate tensile strength

WFT windowed Fourier transformation

WTC wavelet transformation coefficients

r volume density

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401390

Alternatively, it has been proposed that a characteristic set

of AE features/parameters could help to identify the

signature of specific damage mechanisms and thus to assist

the evaluation of the monitored AE activity. In the first

approach (continuous in frequency domain), de-convolution

methodologies are usually applied in order to determine the

characteristics of the overall system (acquisition system,

propagation medium, boundary conditions) separating this

knowledge from the source event contribution. Some

examples of this approach are given by Qi [11] who used

pencil breaks to estimate system transfer functions on

carbon fibre reinforced composites at several loading stages,

Giordano [6] who worked on polymer composites and

Ageorges [12] who worked on carbon fibres reinforced

composites. In the second approach (discrete in time and

frequency domain), a representative set of significant

frequencies that corresponds, with high probability, to

specific damage mechanisms is proposed. In 1977, Russell

[13] introduced the issue of AE signature in the case of

graphite/epoxy composites, while in 1981, Clough [14]

referred to the same issue for fatigue/stress corrosion

cracking in aluminum alloys.

In the present work, a new methodology (Fig. 1) for the

development of a reliable database concerning AE charac-

teristics of single fibre failures, in time and frequency

domain, is proposed. This methodology is based on the

assumption that there is a distinct fibre failure signature. In

order to evaluate this signature in time domain, many

different techniques that quantify the AE waveform

information are proposed, such as AE characteristic

parameters histograms and multi-dimensional projections,

together with the application of advanced signal processing

methodologies. Furthermore, in order to classify the

frequency domain content of the AE waveforms, the de-

convolution scheme (source, propagation medium, acqui-

sition system, captured waveform) is applied, in a more

‘discrete’ way, combining both presented approaches.

The proposed methodology is based on the assumption of

an overall system, with one input (source event or AU pulse)

and one output (AE activity or AU response), at any time

Fig. 1. A schematic representation of the applied methodology for the analysis and classification, in time and frequency domain, of the captured AE and AU

waveforms, during fibre bundles quasi-static tensile tests.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401 391

step. However, this overall system is assumed to be identical

for all the executed experiments, except the part of the

propagation medium, which is dependent on the fibre type.

Under these assumptions, it is possible to identify the AE

signature in the frequency domain, since the differences

between the AE and AU captured waveforms are due to the

variety of the source of failure events and the propagation

medium characteristics corresponding to the different types

of fibre bundles.

3. Testing procedure

3.1. Material

In order to investigate the role of essential material

parameters, such as fibre mechanical and physical proper-

ties, fibre structure and anisotropy level (material class), on

the characteristics of the generated elastic waves during

tensile loading, a number of different fibre types were tested.

The selected fibre types belong in one of the three following

material classes: organic, ceramic and carbon, providing in

this way the necessary diversity in material origin for the

justification of the proposed methodology for database

development. The most important properties of the selected

fibres are shown in Table 1 [1], where r is the volume

density, d the fibre diameter, UTS the ultimate tensile

strength and E11 the modulus of elasticity in the loading

direction (axial).

3.2. Testing conditions

For the failure modes investigation of the selected fibre

types, a number of fibre bundle quasi-static tensile tests were

performed according to DIN 53942/1984. For the justifica-

tion of this standard, all tensile tests were conducted

utilizing a special metallic device (Fig. 2a), attached on the

grips of a MTS Testing Machine (Type 858 Mini Bionix,

equipped with a 2.5 kN load cell), under displacement

control (LVDT measurements) and controlled environmen-

tal conditions of 25 8C and 70% relative humidity [7]. For

each material type, a gauge length (GL) of 30 mm was used

and three different displacement rates (0.05, 0.1 and

0.5 mm/min) where applied in order to investigate the

dependence of the testing results and captured waveforms

on the applied rates.

In these experiments, each fibre bundle specimen was

aligned by two steel miniature grips, called fibre grips,

which are attached on the main frame of the fibre testing

device. The distance between the fibre grips was pre-

determined by the use of an aluminum block, which set the

gauge length of each sample, called GL Block. For the

stabilization of each fibre bundle, a pre-stressing apparatus

included at each fibre grip was employed, generating the

necessary gripping pressure at the bundle clamping section

(Fig. 2b). Due to the fragile nature of the selected fibre

types, thin pieces of plastic foil were attached on both sides

of the clamping section in order to avoid any fibre damage

before the testing procedure.

During the testing procedure, AU signals were generated

by a pulsing system (excitation every 0.5 s of a wide band

transducer (WD), placed on the upper fibre grip (Fig. 2c) and

with sensitivity:261 dB V/mbar at 525 kHz, by a C-101-HV

device). Two miniature sensors (NANO 30) were placed on

the upper and lower fibre grips, respectively (Fig. 2a), and

detected AE activity and AU response (sensitivity of AE

sensor placed on the upper fibre grip: 267 dB V/mbar at

326 kHz and AE sensor placed on the lower fibre grip:

266 dB V/mbar at 452 kHz). All the sensors were mounted

permanently on the fibre grips by the use of cyanoacrilic glue.

The pulsing system and the sensors are provided by Physical

Acoustic Corporation (PAC), while 2/4/6-AST pre-ampli-

fiers were used for the AE (NANO 30) sensors (gain 40 dB

and band pass filtering of 20–1200 kHz). AE and AU signals

were captured and recorded, as pairs for each AE sensor, by a

Mistras 2001 acquisition system from PAC, using a 4 MHz

sampling rate and 4k-sample signal size. For the active

channels, Threshold (TH) and gain were set at 40 and 20 dB,

respectively, while the peak definition time (PDT), hit

definition time (HDT) and hit lockout time (HLT) were set at

80,800 and 1200 ms, respectively. For any mechanical noise

elimination, aDt filter was used together with the application

of event location software.

4. Results and discussion

4.1. Mechanical response and non-destructive evaluation

The applied testing program was based on the dry-bundle

approach [5,15,16], which enables the stimulation and

identification of single fibre failure events due to captured

elastic waves with high energy (presence of single fibre/air

interface). However, by the use of dry-bundle testing

Table 1

A list of physical and mechanical properties of the tested fibres, together

with the used coding

Fibre type r

(g/cc)

d

(mm)

E11

(GPa)

UTS

(GPa)

Fibres # Code

Nextel 312 2.70 12 150 1.70 400 H

Nextel 440 3.05 12 190 2.00 400 J

Nextel 610 3.88 12 373 2.93 400 K

Nextel 720 3.40 12 260 2.10 400 A

Altex SN-11 3.30 15 210 2.00 1000 B

S-Glass 2.48 20 87 4.89 6000 E

Hi Nicalon 2.74 14 270 2.79 800 T

Carbon M40-B 1.81 7 392 3.90 3000 P

Carbon M40-J 1.77 7 377 4.41 6000 S

Kevlar 29 1.44 12 83 3.60 1000 L

Spectra PE-40 0.97 6 101 3.08 120 C

Spectra PE-30 0.97 10 73 2.57 120 D

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401392

conditions, several other damage mechanisms are triggered,

like secondary multiple filaments failures and sliding

friction [5,15]. Also, it is assumed that the frequency

content of the griping/supporting apparatus is mainly related

with the high rigidity of the fibre gripping mechanism and

less affected by the loaded fibre compliance. In Fig. 3, a

typical mechanical response of Hi Nicalon fibre bundle

tensile test is shown (0.05 mm/min or 2.8 £ 1025 strain/s),

together with representative/repeatable captured AE wave-

forms at significant stages of the loading process.

According to all the resulting force–displacement

plots, it can be stated that the displacement rate is a

critical testing parameter that affects the mechanical

behavior of different types of fibres. For example, testing

of Ceramic fibres (like Hi Nicalon) requires low rates in

order to achieve well-controlled force–displacement plots

(continuous increase until maximum force and then

multiple decrease steps corresponding to multiple fibres

failures), instead of sudden bundle failure after the

maximum force. However, in the case of other fibre

bundles like Polyethylene, high displacement rates are

suggested for reliable results, due to experienced high

value of strain-to-failure. In addition to this, it should be

noted that the result of sudden specimen failure could

also be attributed to the large amount of elastic strain

energy stored in fibre bundle due to high force levels.

Thus, when a single fibre breaks (the weakest in strength

or the smallest in diameter), the dynamic equilibrium of

elastic energy is lost and the bundle structure collapses.

For the present study, this kind of bundle failure was

characterized as non-valid for further use. As long as the

non-destructive evaluation procedure is concerned, it can

be noted that, in most of the tensile tests the AE activity

was continuous, having a smooth accumulation of events

[7]. This activity could be separated mainly in three

stages: the first one from the beginning of the test until

force levels around the maximum value, the second

one in the vicinity of maximum force and the third one

till the final bundle failure. According to the test

outcomes, there were no statistically significant evidences

of dependence between the AE activity and the

displacement rate, while the type of the tested

material affected significantly the captured AE activity

and AU response.

Furthermore, in order to eliminate any possible sources

of noise, event location techniques were applied in each

test, providing valuable information about the strength/-

diameter distribution along the sample length [5,15].

However, a significant dependence of the event location

with the used displacement rate was identified, i.e. the

higher the rates the closer to the fibre grips the AE

activity sources are located. Finally, in order to increase

the reliability of the used non-destructive evaluation

scheme (location, source types, etc.), extensive fractro-

graphic examination was applied in many fibre bundle

specimens, during specific stages of loading process and

after testing, following procedures available in the

literature [7,10,15,17]. Based on this evaluation, it was

possible to identify single fibre failures as well as multiple

fibre failures sites and sliding friction mechanisms.

Fig. 2. Experimental set-up for fibre bundle tensile tests (according to DIN 53942/1984), together with AU and AE sensors positioning.

Fig. 3. A typical mechanical response plot for fibre bundle tensile test,

together with representative AE waveforms (NANO 30) corresponding to

several stages of loading process.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401 393

4.2. Quantification and evaluation of the waveform content

4.2.1. Selection of valuable waveforms

In order to perform the characterization of the fibre

failure events, it is necessary to create a representative set of

waveforms. However, the existence of such a set is

obviously an assumption that can be applied in most types

of fibres for the larger part of the loading process, but before

the maximum force is reached (Fig. 3) due to undefined

attenuation caused by damage in the bundle after this point.

Moreover, this assumption is valid in the particular case that

there is no dependence of the captured waveform with the

corresponding force/stress level, which apparently is not

true for some fibre types [7]. For all these reasons,

waveform selection criteria for single fibre failure were

established, taking into account the available knowledge

from the literature [5,7,9,12,15,16,18]:

† Voltage level: values of single fibre failures should be

10–100 times lower than multiple fibre failures.

† Force–displacement plot: selection area should start at

the first non-linearity (if this exists) until the maximum

value. The area after the maximum force should be

avoided due to many friction or pull-out phenomena and

uncontrolled attenuation values.

† Location events: the source location of the selected

waveforms should not be close to the fibre grips, but near

the middle of the gauge length.

† Sensitivity analysis: the dependence of each AE feature

values on the applied TH should be taken into account.

† Fast Fourier transform (FFT), spectrogram and wavelet

analysis: information gained from the use of the

candidate waveforms’ frequency response could become

essential in the selection procedure.

† Statistical aspects: the higher the number of representa-

tive waveforms of single fibre failure events the lower

sampling uncertainty and force dependency.

† Literature review: use of all the available information

about AE feature values for fibre failures.

The selection of each candidate AE waveform is

accompanied by the proper AU one, identified as the first

waveform prior to the selected AE waveform. However, the

success of the proposed overall selection procedure has to

be evaluated, taking into account the fact that random single

fibre failures occur at every stage of loading process.

4.2.2. Database development—time domain

The development of the time domain part of the proposed

database is presented in this section. The aim of the

proposed approach is the identification of specific events by

the use of AE features. This asset was introduced at early

70s by Curtis [19], who investigated material damage

mechanisms by means of AE and it was continued by

Russell [13] on AE signals from Graphite/Epoxy compo-

sites. In 1986, Suzuki [20] correlated AE features with

structural factors of composite materials damage mechan-

isms and 1 year later, Okada [21] investigated the fracture

behavior of Carbon fibre reinforced aluminum by the use of

AE method. Finally, in 1995, Anastasopoulos [22] studied

the actual use of AE features for damage mechanisms

identification in composite materials and Groot [23] applied

AE method for the identification of different fracture

mechanisms in Carbon/Epoxy composites.

The first step in the proposed analysis is the use of the

established waveform selection criteria in order to create the

necessary representative set of captured AU/AE waveform

pairs for each single event. To this aim, the most important

parameter is the TH (an Amplitude level). It should be noted

that, in general, many extracted AE features in the time

domain are affected by this parameter. This correlation

becomes even stronger in the case of time dependent

features such as rise time (RT), duration (Dur) and average

frequency (AvF). Minimization of this effect can be

achieved by means of a proper parametric sensitivity

analysis, identifying the TH value which affects less the

results of the feature extraction procedure [7]. This study

has to be performed for every fibre type and the minimum

acceptable TH value for all fibre types should be used for the

final feature extraction. Moreover, it has been proved [7]

that the threshold/amplitude ratio (THA) is a useful tool for

AE features comparison between different materials and

damage mechanisms. For the present study, feature extrac-

tion was executed only for signals with THA ,0.8, in order

to increase the reliability and the efficiency of the developed

database.

In Table 2, ranges of some basic AE features are

presented for both AE sensors corresponding to the failure

of different fibre types (Amp, waveform amplitude, TE, true

energy). Based on these values, a general comment could be

that a single fibre failure generates AE waveforms with

medium Amp (50–80 dB), low RT (,40 ms) and medium

Dur (150–400 ms). Furthermore, it is obvious that different

fibre types emit AE waveforms with different time

envelopes at failure, even those that belong in the same

material classes (Nextel or Spectra). In addition, the

calculated distributions of many AE features values are

neither Uniform nor Gaussian, but non-symmetrical. There-

fore, use of feature mean values is not a reliable tool for AE

event characterization. Finally, the estimated variation of

AE features for each fibre type is related with the available

population of AE waveforms for statistical analysis and in

some cases affected the final results in the developed

database (for example types E and S).

A schematic representation of the failure areas of several

fibre types are given in Fig. 4, by using the values of two AE

features taken from the developed database. In general,

identification of well-defined failure areas using 2-D

projections of AE features are possible for each fibre type,

with often limited overlapping in the projected plane,

constituting in this way a useful tool of comparative

evaluation. The fact that different fibre types have different

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401394

ranges of values should be quantified and correlated with

specific fibre characteristics (physical and mechanical

properties). An example of the proposed correlation

concerns the TE feature (integral of the captured waveform

voltage over the time). According to Tables 1 and 2, there

are evidences that the UTS and the product rd (mass per

fibre cross-section) are the most important parameters

(driving force) for the final formation of TE values of the

captured waveforms, which is related with the AE energy

released during a fibre failure. As a general rule for Ceramic

and Carbon classes, the higher the UTS the higher the TE for

fibre failures (the highest for Carbon fibres). However, for

the comparison of Ceramic and Organic classes, the rd

product is more important than the UTS or the diameter d

[24], and a rule is assigned: the lower the rd product the

higher the TE of the captured AE waveform after fibre

failure. Although the former proposed rules could be

applicable in many material cases investigated in the

present work, a more thorough examination of AE features

dependence (time domain) with fibre properties (diameter,

density, modulus of elasticity, tensile strength) should be

performed.

Another very informative graph is given in Fig. 5, where

the ranges of three AE features of the developed database,

which are related with ‘equivalent’ frequency domain

characteristics, are presented: rise frequency, RiF ¼

CNTP/RT (CNTP, counts to peak), AvF ¼ Co/Dur (Co,

counts) and reverberation frequency, ReF ¼ (Co-CNTP)/

(Dur-RT). Although, the values of these features do not

correspond to ‘real’ frequencies, they can provide useful

information about the captured waveforms. Indeed, RiF, for

example, seems to be a useful feature since it is related with

the source characterization of the emitted AE energy.

4.2.3. Database development—frequency domain

The procedure followed for the classification of the fibre

failure response in the frequency domain is given in the

present section. The efforts to assess the AE activity in the

frequency domain in order to enrich the damage identifica-

tion/characterization process started in 1971, when the fast

Fourier transform (FFT) analysis of AE waveforms was

introduced for the identification of material failure modes

[25,26]. Additional work in this area has been done by

Prosser [9], who applied an advanced AE waveform-based

detection technique of matrix cracking in composites based

on the modal analysis approach and by Ageorges [12], who

used an FFT analysis for damage identification in composite

materials (matrix cracking and fibre failure in single and

Carbon fibre bundles).

In general, a frequency domain database must be able to

identify all the possible ‘sources’ of the dominant

frequencies in the FT of the captured waveforms. These

sources characterize the overall system and the goal is to

‘extract’ and identify these frequencies from the wave-

forms’ FT. In the present study, such sources, which

constitute the overall system, are identified: the acquisition

Table 2

A representative part of the developed database based on AE features’

values, related with fibre failures under quasi-static tensile tests in room

temperature

Fibre type THA Amp (dB) RT (ms) Dur (ms) TE (fJ)

Nextel 312 0.46–0.80 61.8–72.0 13–27 160–370 11–17

Nextel 440 0.54–0.79 58.5–74.3 12–26 180–356 8–29

Nextel 610 0.58–0.77 57.5–68.9 9–39 149–341 1–14

Nextel 720 0.51–0.71 64.6–71.2 23–35 189–455 3–23

Altex SN-11 0.52–0.84 54.4–71.2 8–24 323–447 4–24

S-Glass 0.61–0.71 60.0–63.4 12–18 186–332 4–20

Hi Nicalon 0.51–0.83 58.0–71.8 23–71 166–298 5–30

Carbon M40-B 0.45–0.83 64.6–81.2 14–36 200–324 22–148

Carbon M40-J 0.63–0.75 57.8–65.1 20–24 171–275 22–126

Kevlar 29 0.61–0.74 58.1–77.9 8–27 229–331 38–69

Spectra PE-40 0.47–0.65 70.9–73.8 21–43 352–412 19–42

Spectra PE-30 0.55–0.87 55.0–73.6 39–51 254–410 6–39

Mistras 2001, NANO 30 and fibre grips based on DIN 53942/1984.

Fig. 4. A schematic representation of the developed database concerning

the failure areas of several fibre types at room temperature, by the use of AE

features (NANO 30).

Fig. 5. Mean values and variations of AE features (NANO 30) in time

domain representing the failure of different types of filaments at room

temperature.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401 395

system (sensors, acoustic coupling, pre-amplifiers and

calibrated A/D board), the pulsing system (pulsing system

and transducer), the gripping/supporting apparatus for fibre

testing (vibration eigenvalues) and the propagation path

(elastic waves in a medium with the specific geometric,

anisotropy and material properties of the fibres). In order to

evaluate the frequency content of each captured waveform,

a number of assumptions had to be made. Thus, the subset of

the overall system called system (overall system except the

propagation path characteristics) is considered constant for

every test, since the settings are kept constant (HDT,

sampling rate, sensors type and positioning, pulsing

settings, preamplifiers and settings). Furthermore, it is

assumed that, for a given fibre type, there should exist

common significant frequencies between the AE and AU

waveforms, because AE activity must include the propa-

gation signature of the medium (in frequency domain). This

propagation path could be every intact fibre or fibre

fragment after a local failure along the remaining length,

towards the fibre grips. However, for different fibre types,

AE activity and AU response should be different due to the

contribution of different source events and propagation path

characteristics. A precise estimation of the characteristics of

the system could be achieved by implementing these

assumptions into the results of a large number of tensile

tests on different fibre types and material classes. In

addition, it is assumed that the fibre breakage mechanism

generates elastic waves in the micro-structure, which

contain a unique set of dominant frequencies for each

fibre type. However, this set is present only in the AE

waveform and not into the AU one of each pair. Given this

knowledge, and following a discrete step-wise approach in

the frequency domain, it is possible to determine the sets of

dominant frequencies that correspond to the failure events

(single and multiple fibres breakage) for each fibre type and

the propagation of the emitted elastic waves in the medium

of each specific fibre type, by extracting/filtering the

characteristics of the system from the captured AE and

AU waveforms, in a FT level and evaluating the frequency

content of the AU waveforms. A schematic representation

of the above assumptions is given in Fig. 6, where the

frequency content is analyzed at the following sets: Sy

(system set), Pi (propagation set of fibre i), Pi;j (common

propagation set between fibres i and j), Di (damage set of

fibre i) and Di;j (common damage set between fibres i and j).

In the present study, the propagation medium character-

istics were identified by the use of the AU method (AU/AE

waveforms pairs) instead of the pencil break method [3].

The advantages of the AU method compared with pencil

breaks are: generation of a repeatable and well-controlled

pulse, characterization of the full propagation path, simple

and continuous application during each test. The disadvan-

tages are limited in the use of complex electronics and the

frequency ‘coloring’ by the used sensor. For each AU/AE

waveforms pair, the AE waveform includes the source

event of fibre failure, the effect of the propagation path

(through the intact fibres) and the system, while the AU

signal contains the same information, considering as source

event the generated AU pulse. An example of such a pair of

waveforms captured by the AE sensor at the lower fibre

grip, in the time and time/frequency domain, is presented in

Fig. 7, corresponding to a quasi-static tensile test of a Nextel

720 fibre bundle. This representative pair was selected by

applying the proposed criteria described in Section 4.2.1,

while the AU waveform is considered as the one that was

first recorded before the candidate AE waveform of fibre

failure at the same acquisition channel.

The identification of the frequency set that can determine

damage mechanisms requires a detailed analysis of the

captured AU/AE waveforms (such as Fig. 7(c) and (d)),

which must take into account the non-stationary character of

this kind of captured electrical signals. In general, the

analysis of non-stationary or transient signals is conducted

by means of joint time/frequency domain methods based on

windowed FT (Spectrogram or WFT), short-time FT

(STFT) or recently Wavelet analysis. However, in the

present work, the aspect of frequency signature existence in

AE waveforms is followed. The existence of such a

frequency signature in the AE waveforms is actually a

recent approach. In 1995, Groot [23] proposed a real-time

frequency determination of AE waveforms for different

damage mechanisms in Carbon/Epoxy composites, identi-

fying Carbon fibre failures at 180–240 kHz ranges. More-

over, in 1997 Qi [11] worked on Carbon fibre reinforced

composites using Wavelets analysis and proposed that fibre

failure is represented by events with high intensity, low

duration and high frequencies. In 1998, Giordano [6]

characterized failure modes in polymer-composites by an

FFT analysis of the AE waveforms, where single Carbon

fibre failures were identified and their AE signature

corresponding to failure was determined below 450 kHz.

In 2002, Mizutani [10] applied Wavelet analysis for the

identification of wave modes, using the signal power

Fig. 6. A schematic representation of the basic assumptions adopted for the

determination of the frequency content of captured AE waveform during

fibre bundle tests.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401396

as a function of time and frequency in cross-ply Carbon

composites. At the same year, Ni identified as AE signature

a number of peaks in the FFT plot, which are correlated with

damage mechanisms in model Carbon fibre composites, and

Park [24] identified frequency response characteristics of

the AE waveforms for single Carbon, SiC and E-glass fibre

failures, applying fragmentation tests. Finally, in 2003,

Mattei [17] used the aspect of AE signature for a real time

identification of delamination onset in Carbon fibre

reinforced plastic laminates under fatigue testing, by

treating a number of significant FFT peaks as indicators of

damage mechanisms.

According to the above literature references, the use of

the joint time/frequency domain instead of the typical FT is

suggested as an efficient method, especially in AE modal

analysis. The results of such an investigation of joint time

frequency response of the captured AU/AE waveforms pair,

which is not the subject of the present work, is given at the

second part of the Fig. 7, where a Wavelet transformation of

the captured waveforms is applied (Wavelet transformation

coefficients (WTC), relevant to FT amplitude). For this

purpose, the AGU-Vallen Waveletq (V. 2.31) software was

used, which is based on the Gabor mother wavelet of 200

samples size and 5 kHz resolution, for each 4096-samples

captured AU/AE waveforms. This particular time/frequency

domain transformation was presented in the literature as an

efficient tool in the case of AE waveforms [27]. For the

waveform pair of Fig. 7 (Fig. 7(a) and (b)), a different

frequency content of the AU and AE waveforms can be

identified (propagation medium and damage mechanism,

together with system filter), while different micro-structural

perturbations during any type of excitation (damage or AU)

reflects to different decrease slopes of several frequency

ranges (transient character of AE and AU waveforms).

However, in order to classify and isolate the damage

frequency set, the main damage mechanisms that produce

significant elastic waves during a dry-bundle test should be

identified. These are fibre failures (single or multiple) and

sliding friction between the fibres. Each source event caused

by these mechanisms has different characteristics (for

example sliding friction appears to have longer duration

and slower velocities than the fibre breakage) and each fibre

failure event could stimulate others (i.e. vibrations after

single fibre failure can stimulate additional fractures due to

longitudinal wave propagation, causing single, double,

triple or more fibre failures). In the present work, in order

Fig. 7. Time and time/frequency domain representation of a AU/AE waveforms pair, captured during a quasi-static tensile test of a Nextel 720 fibre bundle,

under 0.1 mm/min displacement rate.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401 397

to perform a statistical analysis in the frequency domain

content of the valuable waveforms, a general procedure

scheme was utilized to assure that the same rules and criteria

were used for all the input data that produce the extracted

information. To this aim, the FFT analysis is applied, with

the same parameters for each waveform that the analysis

indicated as valuable (512 samples, Hanning window, 75%

overlapping). The range of the investigated frequencies was

determined by setting a threshold in the linear normalized

FFT amplitude vs. frequency plot of each waveform. The

dominant frequencies were selected by using pre-deter-

mined criteria (40, 20, 10 and 5 kHz), which determine the

minimum separation level of two close peaks and the

selection of the dominant frequency out of a group of

frequencies that are very close to each other. The results of

the applied procedure were compared to different tests of the

same fibre type in order to extract the most ‘pure’ damage

and propagation frequency content. Hence, the following

iterative scheme was applied on the AE and AU waveforms,

in order to develop the frequency domain part of the

proposed database:

† Identification of the common frequencies in each fibre

type (by using statistical rules and criteria) and subtrac-

tion of these frequencies from the main set.

† Repetition of the same procedure among the

different fibre types for the same material class.

† Repetition of the same procedure among the different

fibre types and material classes.

† Identification of system frequencies set, by comparing

the common frequencies between the extracted sets

from both the AE and the AU waveforms.

† Characterization of the remaining set of frequencies as

the one that contains information about the fibres. Any

common frequency between the FFT of AE and AU

waveforms corresponds to the propagation frequencies

while the remaining are representative of the fibre

breakage damage mechanism.

A representative example of the above-described pro-

cedure is given graphically in Fig. 8. In this plot, the

identification of dominant frequencies in the framework of a

FFT analysis is presented, for the selected pair of AU/AE

waveforms of Fig. 7, constituting the frequency signature of

propagation and fibre failure in the case of Nextel 720 fibres.

According to this plot and taking into account the outcomes

of Fig. 7, the main energy contribution in the AU waveform

is made by frequencies higher than 350 kHz (middle to high

frequencies), while in the AE waveform this region is below

250 kHz (middle to low frequencies). However, there are

overlapping areas of significant frequencies that should be

investigated in detail by the use of the proposed method-

ology. This identification procedure is recursive and it has

been proved that the larger the number of available AU and

AE waveforms (representative set size) the more reliable

this signature will be.

In Table 3, a list of frequencies identified by the

developed database is given, evaluating information from

both AE sensors. For the determination of these frequency

sets and their classification, a criterion of 10 kHz was used,

while the accuracy of the applied FFT was equal to ^1 kHz.

Moreover, these outcomes correspond to the most con-

servative criteria and selection rules in order to develop the

most reliable and sufficient database. For comparison

reasons, the estimated mean values of the ReF (propagation

characteristics) and RiF (source properties) are given for

every presented fibre type. According to these results,

frequency ‘signatures’ as FFT discrete peaks exist for many

fibre types, associated with system frequency coloring,

propagation ‘filtering’ and damage mechanisms. However,

the individual frequency peaks that characterize

the propagation via each fibre type vary in a range of

100–400 kHz (middle frequencies), while the correspond-

ing peak values for fibre failure varies in a range of

20–400 kHz. Moreover, it is obvious that values of ReF

(AE feature in time domain) can be used in a reliable way as

a mean approximation of the list of propagation frequency

peaks for the presented fibre types. Unfortunately, this is not

the case for the RiF, the values of which overestimate the

frequency response of the failure events.

In order to correlate the fibre properties (Table 1) with

the outcomes of the proposed methodology in the frequency

domain (Table 3), a new analysis scheme should be

established. The developed frequency database consists of

selected significant frequencies, after FFT mapping and

classification/shorting. However, any comparison of fre-

quency values among different fibre types and material

classes is difficult and another group re-arrangement should

be applied. The resulted groups will include significant

frequencies values (i.e. one class of frequencies is around

350 ^ 70 kHz), assuming a representation of specific

micro-structure deformation modes. Following this analysis

scheme for the present study, evidences of E11 dependence

Fig. 8. Frequency domain analysis of the captured AU/AE waveforms pair,

monitored during a quasi-static tensile test of a Nextel 720 fibre bundle at

27 N (80% of maximum force), under 0.1 mm/min displacement rate.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401398

on the identified significant frequencies of wave propa-

gation among all fibres types are found (higher values of E11

lead to shifting on higher significant values of frequency

signature inside each group). Furthermore, the product rd is

again a critical parameter for the comparison between

different material classes (ceramic vs. organic), while

ceramic fibres are characterized by relatively higher

significant frequencies than the polymers ones, for the

same groups with values ,100 kHz. Finally, there are

evidences that the major frequency sets of damage are

related to the UTS for groups with values ,100 kHz (higher

values of fibre UTS inside each group give higher identified

dominant frequencies) and with E11 and rd parameters for

groups of dominant frequencies .100 kHz (higher values

of E11 or rd inside each group lead to higher dominant

frequencies).

4.3. Database validation

In general, there are two different criteria, which can be

used to evaluate the efficiency of single fibre failures

identification (in time and frequency domain) by the

developed database. The first one concerns the comparison

of the proposed results with the available information from

the literature and the second one is related with the

successful use of the developed database in various testing

conditions.

For the first criterion, many researchers have provided

the scientific community with extensive knowledge about

fibre failure characteristics, mostly in time domain.

Unfortunately, many times the provided information con-

cludes to contradictory results [7], reducing the reliability of

the carried out research. In the present work, the variation

margins for the estimated AE feature values for fibre

breakages comply with the values suggested by many

different researchers [5,6,7,8,13,20,21,23], especially for

Dur, AvF and RT. However, comparison between

the proposed results and the literature in the frequency

domain leads to more complicated conclusions. In the

literature, is has been suggested that the dominant frequency

values, extracted from AE waveforms, which correspond to

fibre failure events, are lower than 400 kHz or higher than

700 kHz. The developed database of the present study tends

to agree with the first part of the aforementioned statement.

In any case, the existence of these two different frequency

response regions should be investigated in detail in the

future, in order to be able to extract solid conclusions on the

characterization of the frequency domain content of

continuous fibre breakages.

As long as the results of the application of the developed

database on several testing conditions is concerned (micro-

to-macro approach), preliminary studies give proofs of its

effectiveness [7]. More precisely, in testing conditions of

isothermal mechanical fatigue loading of [0/90/^45]s

Carbon/Epoxy straight strip specimens (fibre type:

M40-J), a large number of the AE waveforms were

monitored as activity due to the fatigue damage accumu-

lation. This AE activity exhibits values of AE features inside

the calculated database margins. Moreover, the frequency

response of the captured AE waveforms is considerably

close to the database characteristic frequencies for fibre

failures of the specific type. In order to correlate the type of

developed damage during fatigue loading with the mon-

itored AE activity, microscopic examination was applied in

specific time intervals, together with C-Scan evaluation,

justifying the presence of multiple fibre failures in several

stages of fatigue loading. In addition, some of the results

proposed by the present analysis (developed database) have

been already validated by the identification of fibre failures

in quasi-static tension tests of coupons made of ceramic

matrix composites. However, the reliability and efficiency

of the proposed analysis should be also validated in

structural systems of higher hierarchical level.

Table 3

List of frequencies (in kHz), including system, propagation and damage (single fibre failure) representative values for different fibre types (criterion 10 kHz) in

comparison with the corresponding values of relative AE features

Material Applied methodology major frequencies AE featurea

System

48 102 144 154 169 280 213 320 328

Propagation ReF

Nextel 720 – 94 – 185 – 382 129–165

S-Glass – – 136 176 – 360 132

Kevlar 29 – – – – 229 352 151

Damage RiF

Nextel 720 26 84 – 204 – – 400–593

S-Glass – 59 115 – 223 – 411

Kevlar 29 31 – – 196 – – 639

Hi Nicalon – – – – 230 343 410

C M40-B – 67 – – – 395 573

a Extracted from the captured waveforms.

Y.Z. Pappas et al. / NDT&E International 37 (2004) 389–401 399

5. Conclusions

In the present work, a complete methodology for the

characterization in time and frequency domain of the elastic

waves generated as AE activity by the failure of different

types of fibres is proposed. The applied classification of AE

activity leads to the development of a database, which can

identify the time and frequency signature of each fibre type

failure. Furthermore, the information gained from each test

by the use of the AU method is evaluated in order to identify

the propagation medium characteristics, leading to the fact

that the construction of a valid and useful database depends

on the ability to characterize efficiently the source event of

each captured AE waveform. During the fibre bundle tests,

continuous AE activity was monitored, with smooth

accumulation of AE events. In addition, it was proved that

the same displacement rate does not lead to identical

mechanical behavior for different types of fibres and an

important dependency of the event location with the used

displacement rate was identified. However, there were no

evidences of dependence between the AE activity and the

displacement rate, while the material type exhibits a major

effect on the recorded AE activity and AU response.

In order to develop the AE database, a number of

assumptions were made to extract valuable information. In

addition, waveform selection criteria for single fibre failure

events have been established, taking into account the

available knowledge from the literature. In order to

eliminate the effect of the TH parameter on the AE features

database content, a parametric sensitivity analysis was

applied. As long as the database content is concerned, it was

proved that single fibre failure generates AE activity with

medium Amp, low RT and medium Dur. In addition, the

calculated distributions of AE features’ values are neither

Uniform nor Gaussian, but most of them are non-

symmetrical. In the frequency domain part of the proposed

database, frequency signatures as FFT discrete peaks were

identified for many fibre types. More precisely, the

individual frequency peaks that characterize the propa-

gation via each fibre type vary in a range of 100–400 kHz

(middle frequencies), while the corresponding peak values

for fibre failure vary in a range of 20–400 kHz. According

to this study, the proposed identification procedure of the

frequency signature of the tested fibre types is an iterative

process. Moreover, a large number of AE and AU wave-

forms (representative set size) leads to a more reliable

signature extraction and to the development of a sufficient

frequency domain database. In addition, an investigation of

possible correlation between the AE features’ values and

identified dominant frequencies from the developed data-

base with fibre type/material class properties (diameter,

density, modulus of elasticity, tensile strength) was

conducted, resulting to very informative results. However,

the outcomes of this analysis are limited to the present study

case and more work has to be done in the future for the

investigation of the role of material parameters on

the characteristics of the emitted elastic waves. Finally,

proofs for the applicability of the AE database in a micro-to-

macro approach are given by the preliminary results in

testing conditions of isothermal mechanical fatigue loading

of Carbon/Epoxy coupons.

Acknowledgements

The authors are grateful to Professor G. Grathwohl and to

Dr M. Kuntz (University of Bremen, ermany) for their

collaboration, in the framework of the Joint Greek-German

Research Project (1999-2000). Also, the authors want to

acknowledge the technical assistance received by ICE-

HT/FORTH while performing the experimental part of this

work.

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