ARL-TR-8019 ● MAY 2017

US Army Research Laboratory

Sensing Applied Load and Damage Effects in 

Composites with Nondestructive Techniques

by Colleen Rosania, Mulugeta A Haile, Natasha C Bradly, 

Asha Hall, Michael Coatney, and Fu-Kuo Chang

Approved for public release; distribution is unlimited.
NOTICES

Disclaimers

The findings in this report are not to be construed as an official Department of the 

Army position unless so designated by other authorized documents.

Citation of manufacturer’s or trade names does not constitute an official 

endorsement or approval of the use thereof.

Destroy this report when it is no longer needed. Do not return it to the originator.
ARL-TR-8019 ● MAY 2017

US Army Research Laboratory

Sensing Applied Load and Damage Effects in 

Composites with Nondestructive Techniques

by Mulugeta A Haile, Natasha C Bradly, Asha Hall, and Michael 

Coatney

Vehicle Technology Directorate, ARL

Colleen Rosania and Fu-Kuo Chang

Department of Aeronautics and Astronautics, Stanford University, 

Stanford, CA

Approved for public release; distribution is unlimited.
ii

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the 

data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the 

burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. 

Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently 

valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE (DD-MM-YYYY)

May 2017

2. REPORT TYPE

Technical Report

3. DATES COVERED (From - To)

January 2016–February 2017

4. TITLE AND SUBTITLE

Sensing Applied Load and Damage Effects in Composites with Nondestructive 

Techniques

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Colleen Rosania, Mulugeta A Haile, Natasha C Bradly, Asha Hall, Michael 

Coatney, and Fu-Kuo Chang

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

US Army Research Laboratory

ATTN: RDRL-RDRL-VTM

Aberdeen Proving Ground, MD 21005-5066

8. PERFORMING ORGANIZATION REPORT NUMBER

ARL-TR-8019

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

11. SPONSOR/MONITOR'S REPORT NUMBER(S)

12. DISTRIBUTION/AVAILABILITY STATEMENT

Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES

14. ABSTRACT

Composite materials are desirable for many aerospace applications but pose challenges due to their complicated damage mechanics. 

Implementing a structural health monitoring (SHM) strategy for composite structures is one approach for assessing the health or damage state 

in real time. Application of SHM requires not only the ability to localize, classify, and quantify damage, but also compensate for changing 

environmental and operating conditions. One such problem is compensating for the effects of applied load on a composite structure to correctly 

estimate damage. To study the effects of applied load, composite coupons were tested in static and fatigue loading, gathering data in various 

damage and loading states with multiple nondestructive evaluation (NDE) techniques. Evaluation using piezoelectrically induced guided 

waves, acoustic emission, thermography, and X-ray imaging were compared and correlated. This report summarizes experimental setups 

involving the various NDE methods and technics and provide results on the effects of load and damage in the received ultrasonic signals.

15. SUBJECT TERMS

guided waves, feature extraction, NDE, fatigue, acoustic emissions

16. SECURITY CLASSIFICATION OF:

17. LIMITATION

 OF

 ABSTRACT

UU

18. NUMBER

 OF 

 PAGES

40

19a. NAME OF RESPONSIBLE PERSON

Mulugeta A Haile

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified

19b. TELEPHONE NUMBER (Include area code)

410-278-5289 Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18
Approved for public release; distribution is unlimited.

iii

Contents

List of Figures v

List of Tables vi

1. Introduction 1

1.1 Motivation 1

1.2 Composite Materials 1

1.3 Damage in Composites 2

1.4 Structural Health Monitoring 4

1.4.1 Guided Wave-Based SHM 5

1.4.2 Acoustic Emission 6

1.4.3 Thermography 6

1.4.4 X-Radiography 6

2. Experimentation 7

2.1 Composite Test Samples 7

2.2 Piezoelectric Sensor Networks 8

2.3 Data Collection 9

2.4 Testing Procedure 10

2.4.1 Static Procedure 11

2.4.2 Fatigue Procedure 12

2.5 Experiment Results 13

3. Signal Processing 15

3.1 Ultrasonic Signal Response 15

3.2 Feature Extraction 18

4. Signal Analysis 22

5. Conclusions and Future Work 27

6. References 28
Approved for public release; distribution is unlimited.

iv

List of Symbols, Abbreviations, and Acronyms 31

Distribution List 32
Approved for public release; distribution is unlimited.

v

List of Figures

Fig. 1 Example of a layup of a laminated [453/04/902/60] composite of 

unidirectional plies, with schematic (left) and stacking order (right)... 2

Fig. 2 Representation of the formation of transverse matrix cracks in a crossply laminate........................................................................................... 3

Fig. 3 Schematic of different states of delamination in a composite laminate 4

Fig. 4 Dimensions of the composite coupon geometry................................... 8

Fig. 5 Experimental setup with a composite coupon loaded in a mechanical 

testing system (MTS) machine (left) and the sensor schematic with 

example wave propagation (right) ........................................................ 9

Fig. 6 Photographs showing a composite sample loaded for testing with AE 

sensors (left) and the thermographic imager and cables attached for 

ScanSentry data collection (right)....................................................... 10

Fig. 7 Extracted X-ray image (left) from the X-ray CT scan of L1S01 after 7 

kips loading showing a small delamination and matrix cracking around 

the notch compared with collected AE hits (right) during the test ..... 14

Fig. 8 A 3-D view of the damage in L1S01 after 7 kips loading. The dye 

penetrant improves contrast between damaged and undamaged 

sections................................................................................................ 14

Fig. 9 Raw signal of L1S01 from path 5to8 at 150-kHz actuation frequency 

as the load increases from 0 to 4 kips................................................. 16

Fig. 10 Zoom in of the first wave packet (top) and one peak (bottom) of data 

collected from L1S01 path 5to8 as the load increases showing the 

change in sensor signal (no damage present)...................................... 17

Fig. 11 Example of the raw signal and the windowed first wave packet from 

L1S01 path 4to9 150-kHz actuation frequency .................................. 18

Fig. 12 Example of how the time domain features of TOF and amplitude are 

calculated from the sensed signal by using the envelopes of the 

waveforms path 4to9 150-kHz actuation frequency ........................... 19

Fig. 13 Example of the PSD of L1S01 path 4to9 150-kHz actuation under a 0-

kips load.............................................................................................. 21

Fig. 14 Extracted PSD of the actuation frequency (150 kHz) of L1S01 path 

4to9 at 0 kips (i.e., one slice at 150 kHz extracted from Fig. 13)....... 21

Fig. 15 Schematic showing actuator and sensor numbering and the 4 straight 

paths used in analysis whose direction coincides with the shown 

loading direction ................................................................................. 23

Fig. 16 Extraction of the PSD of L1S01 at the actuation frequency 150-kHz 

path 5to8 as applied load increases..................................................... 24
Approved for public release; distribution is unlimited.

vi

Fig. 17 Normalized amplitude change in the first wave packet of data with 

actuation frequency 150 kHz from path 2to11 for ARL-tested coupons

............................................................................................................. 25

Fig. 18 Normalized TOF change in the first wave packet of data with actuation 

frequency 150 kHz from path 5to8 ..................................................... 25

Fig. 19 Normalized maximum PSD value change in the first wave packet of 

data with actuation frequency 150 kHz from path 3to10.................... 26

Fig. 20 Normalized energy change in the first wave packet of data with 

actuation frequency 150 kHz from path 4to9 ..................................... 26

List of Tables

Table 1 Material properties of a T700G/2510 unidirectional composite ply..... 7

Table 2 Initial static and fatigue cycle count order for testing of coupons...... 11

Table 3 Summary of completed tests at the US Army Research Laboratory 

(ARL).................................................................................................. 13
Approved for public release; distribution is unlimited.

1

1. Introduction

1.1 Motivation

Composite materials are desirable for aeronautical and aerospace applications for 

many reasons including their high strength-to-weight ratios, fatigue and corrosion 

resistance, design adaptability, and performance capabilities in harsh environments.1–5

Because of these qualities composites are useful in many applications such as in armor, 

helmets, and helicopters, and as structural components. However, when in-service 

composite materials experience very different damage mechanics than metals. 

Performance and quality of composite materials can suffer from fatigue, 

environmental conditions, and external damage just as metals, but due to their 

inherent complexity and the difficulty of detecting damage in composites with 

traditional inspection techniques, maintaining and guaranteeing the safety of 

composite structures is a challenging problem. Strategies such as structural health 

monitoring (SHM), a system with which to monitor a structure in real time, are 

particularly useful for composite materials, because they combat these difficulties 

by giving warning of any changes to the system. Implementing reliable SHM 

strategies into composite structures will allow the Army to fully take advantage of 

the performance capability of composite materials while increasing efficiency by 

saving time and expense on repair, decreasing the need to take structures out-ofservice for inspection, decreasing the occurrence of sudden failures, and extending 

the lifetime of composite structures by enabling efficient maintenance. This project 

studies damage progression in composite materials as monitored by multiple 

techniques used in nondestructive inspection to further understanding of the 

material itself and the capabilities of various nondestructive evaluation (NDE)

techniques. Correlating and validating NDE strategies for sensing damage and 

operational effects on composite materials will add to the knowledge of composites 

and efficient strategies to monitor these materials and use them to their full 

capabilities.

1.2 Composite Materials

There are many types of composites used in industry based on materials such as 

glass fiber, Kevlar, and carbon fiber combined with various types of epoxy.4,6

Carbon fiber-reinforced polymers are particularly useful in necessarily low-weight 

but high-strength applications. Unidirectional prepreg composites, which have 

fibers oriented in the same direction held together by an epoxy matrix, are one of 

the most commonly used of these materials. Individual plies can be oriented and 
Approved for public release; distribution is unlimited.

2

stacked together to create a laminated composite structure with specific material 

properties.7,8 Figure 1 illustrates an example of a laminated composite of 

unidirectional plies. In this way, a designer can create a composite laminate with 

tailored material properties necessary for a specific application.

Fig. 1 Example of a layup of a laminated [453/04/902/60] composite of unidirectional plies, 

with schematic (left) and stacking order (right)8

This unique design capability and adaptability coupled with potential high strengthto-weight ratios make composite materials extremely desirable for highperformance applications. However, composites have relatively complicated and 

situation-dependent damage mechanics. Composite materials experience multiple 

damage types that can occur together or in sequence to contribute to a weakened 

structural state or final failure. The main modes of failure are matrix cracking, 

delamination, fiber breakage, and fiber buckling, which all can contribute 

individually or together to the progressive degradation and weakening and then 

final failure of a composite structure. In fatigue loading, the types of failure that 

predominantly occur and are a signal of potential areas for final failure are matrix 

cracking and delamination.8–12

1.3 Damage in Composites

Matrix cracking is often the first type of failure to occur in a composite laminate 

and is a precursor to other types of failure. It does not result in the final failure of 

the composite but does contribute to strength degradation in the laminate. Matrix 

cracking is a matrix-dominated failure, which occurs in the matrix between 

individual fibers and perpendicular to the fiber direction, as shown in Fig. 2. Once 

a matrix crack appears, it quickly spreads through the entire width of the material. 

Matrix cracks can form due to fatigue, impact, or thermal stress mismatch between 

plies, which can occur during manufacturing. Matrix cracking decreases a ply’s 

stiffness and strength, which contributes to the degradation of the global stiffness 

and strength of the composite. The increasing density of matrix cracks in a ply lead 
Approved for public release; distribution is unlimited.

3

to increased stress mismatch between plies, which in turn causes delamination. In 

this way a high density of matrix cracks can “spread” to between the plies due to the 

interfacial stress mismatch, forming a region of delamination.

8,9,13,14

Fig. 2 Representation of the formation of transverse matrix cracks in a cross-ply laminate15

Delamination is a type of composite damage that can result in final failure of a 

composite laminate. Delamination refers to separation between adjacent plies in a 

layup (Fig. 3), which can form during manufacture or as a result of loads during 

use. Results of delamination include a reduction in stiffness, strength, and loadcarrying capability of the composite. Repeated loading on a composite laminate 

with internal delamination causes growth of the area damage, which may result in 

complete failure of the composite structure. There exists a critical stage of 

delamination for each application after which the growth of the delamination 

becomes unstable, much like the growth of cracks in metals, which can result in 

rapid failure. This makes delamination a critical damage type to sense and monitor 

in composite structures in use.8
Approved for public release; distribution is unlimited.

4

Fig. 3 Schematic of different states of delamination in a composite laminate16

Traditional failure mechanics and inspection techniques used with metals do not 

work when applied directly to composite materials because of their inherent 

differences. NDE techniques exist that can evaluate composite structures for 

damage including C-Scan, radiography, thermography, shearography, and laser 

ultrasonics. However, these technologies are cumbersome and time-consuming, 

often requiring a structure to be disassembled to inspect all components. 

Additionally, composites’ layered construction, tendency for subsurface damage, 

and complicated damage mechanics make damage assessment challenging 

especially due to the fact that there is no established NDE regimen across the board 

to evaluate composite materials.17–20 Finally, composite materials can suffer a lot 

of internal damage and subsequent stages of weakening in the structure before final 

failure occurs.8,21 Therefore, damage progression is harder to predict in addition to 

being more difficult to detect with current NDE technologies.

1.4 Structural Health Monitoring

One solution to this problem is using SHM strategies for composites (i.e.,

integrating a strategy into the composite structure with which to detect damage and 

state in real time). The idea is to observe the system or structure over time to gather 

data about the system and from measurements extract features that are sensitive to 

damage. By using statistical methods to track changes in damage-sensitive features 

throughout the life of the structure, the current state of health can be determined. 

Depending on the application, SHM can be used to observe long-term slow 

progression of damage such as fatigue or extreme events such as impacts.22 There 

are many methods for implementing SHM systems,

23-27 and multiple NDE 
Approved for public release; distribution is unlimited.

5

techniques are used in this study for mutual correlation and verification. The 

following subsections discuss the methods compared.

1.4.1 Guided Wave-Based SHM

Guided ultrasonic waves can be propagated through a structure to gather 

information about changing conditions in the material. Ultrasonic waves can be 

excited through a composite structure by attaching or embedding ultrasonic 

transducers, such as piezoelectric elements, to the material. Applying voltage to a 

piezoelectric transducer causes corresponding strain in the piezoelectric material, 

and when the transducer is attached on or embedded in a material, this instigates a 

wave of elastic deformations that propagate through the material and can be 

detected by another piezoelectric element. When a disturbance such as a guided 

ultrasonic wave interacts with the piezoelectric element causing strain deformation, 

the opposite effect occurs, producing an output voltage that can be collected and 

analyzed. When these properties are used, a small number of these sensors can scan 

relatively large areas by introducing the wave at one point and sensing in another, 

therefore detecting changes along a certain propagation path.28

Properties of ultrasonic waves depend on the medium through which they 

propagate. The material properties of the host structure, such as the stiffness and 

density, affect the wave velocity and also the dispersion of the wave as it travels 

through the material. Because of wave dispersion and attenuation the amplitude and 

energy content of the wave changes as it propagates. Specifically in this case, 

analysis is focused on Lamb waves, a type of guided ultrasonic wave that 

propagates in plate-like panels in 2 categories of modes, symmetric and 

antisymmetric. As Lamb waves are through-thickness waves, collected data include 

information on material properties, damage, and material state through the entire 

thickness of the material along a travelled propagation path.29–31

Damage in a structure affects wave propagation because when waves interact with 

a structural discontinuity such as damage, it causes reflections, scatter, and energy 

loss. The global plate properties of stiffness and damping are also affected by 

damage, which in turn affects wave propagation. Additionally, environmental and 

operational changes will affect these same global properties and therefore wave 

propagation. The gathered voltage data from piezoelectric sensors include these 

differences in wave propagation as the structure changes, which can be analyzed 

for SHM.

22,28
Approved for public release; distribution is unlimited.

6

1.4.2 Acoustic Emission

Acoustic emission (AE) relies on the same basic principles of guided waves, 

namely, that vibrations travelling through a material can be detected and analyzed. 

However, where guided wave-based SHM is an active form of SHM, in that a signal 

is applied to the system to induce a response, AE is passive in that it senses 

vibrations naturally generated by the material. When events such as matrix 

cracking, fiber-matrix debonding, delamination, or fiber breakage occur in a 

composite material, stress waves result from these defects. An array of highly 

sensitive piezoelectric elements can detect these events as they propagate 

concentrically from their origin point. AE is a valuable method because of both its 

passive nature, not requiring any input signals to operate, and its ability to detect 

dynamic processes occurring in the material, therefore differentiating between 

static and developing defects. Though advantages of AE include its high sensitivity 

to multiple types of events and ability for global inspection, advanced methods are 

required to interpret and correlate AE data to specific damage mechanisms.17,19,20

1.4.3 Thermography

Thermography, also known as thermal imaging, uses the fact that the thermal 

conductivity of a material may change when defects are present. A rapid heat pulse 

is applied to a specimen’s surface, causing a temperature rise on the surface. Given 

a short enough pulse, temperature diffusion is governed solely by the material. Any 

inhomogeneity in the material in the form of manufacturing defects or damage will 

appear as temperature differences. Flaws such as delamination cause a change in 

the thermal radiation in their local area causing these changes. Advantages of 

thermography include being noncontact and its ability to inspect large areas, while 

disadvantages include the necessity of expensive and sensitive equipment, highly 

skilled operators, and the fact that defects deep below the surface will be 

obscured.

17,19,20

1.4.4 X-Radiography

Using X-rays for inspection is the most common testing method. In combination 

with X-ray opaque dye penetrants, which illuminate damage in the images, X-ray 

imaging is an accurate way of detecting damage in a composite material not visible 

to the naked eye. As one of the oldest and most established forms of inspection 

widely understood across multiple disciplines, X-radiography is often used in 

conjunction with other NDE methods as a way to validate SHM techniques. Though 

X-rays are useful and reliable for validation, on a large scale or onboard a system,

they are impractical due to the harmful nature of the radiation.19,20,27
Approved for public release; distribution is unlimited.

7

2. Experimentation

A series of experiments were conducted to collect NDE data at different applied 

load levels and progressive damage states eventually through the fatigue life of 

composite coupons. Multiple composite samples of the chosen layup were 

instrumented with piezoelectric elements and loaded in tension-tension fatigue to 

induce first matrix cracking and then delamination in the material. NDE data were 

collected in both loaded and unloaded conditions at each damage state, and X-ray 

images were taken at each interval to assess current damage. The experiments are 

described in detail in the following sections. 

2.1 Composite Test Samples

All composite coupons were composed of Torayca T700G/2510 unidirectional 

carbon fiber prepreg material. Unidirectional ply properties were given by the 

manufacturer and are shown in Table 1. The composite coupons for this 

experimental series were manufactured by Composites Universal Group. They 

were vacuum bagged and oven cured and then water-jet cut to experimental 

specifications. Glass fiber tabs approximately 2 inches in width were attached to 

the top and bottom of the coupons for clamping during testing. The manufacturer’s 

specifications sheet reported a void content of 5%–7% in the laminates, and though 

this will lead to variability between the coupons and in the experimental results, the 

material and manufacturing process are approved by the Federal Aviation 

Administration for use in aircraft. 

Table 1 Material properties of a T700G/2510 unidirectional composite ply

Property Value

E1 (Msi) 18.5

E2 (Msi) 1.2

G12 (Msi) 0.9

G23 (Msi) 0.5

ν12 0.3

ν23 0.22

h (ply thickness, inches) 0.00625

A cross-ply laminate was used in this experimental series. This layup was chosen 

due to its high-fatigue resistance such that a density of matrix cracks form in the 

90° plies in the laminate prior to delamination, and that damage will be easily 

visible on X-ray images. The chosen layup was a [02/904]S cross-ply laminate 

(0.075 inches thick). The geometry used is of a “necking” or “dogbone” 

configuration. Each coupon has a notch cut into the left edge at the center of the 
Approved for public release; distribution is unlimited.

8

coupon. Notched samples develop matrix cracking and delamination quickly due 

to the notch serving as a damage initiation point. The dimensions of the coupons 

are shown in Fig. 4.

Fig. 4 Dimensions of the composite coupon geometry28

2.2 Piezoelectric Sensor Networks

Piezoelectric elements were attached on the surface of each composite coupon to 

the top and bottom of the coupon to act as actuators and sensors respectively (as 

shown in Fig. 4). Twelve piezoelectric elements were attached to each coupon in 

the form of two 6-lead zirconate titanate (PZT)-sensor SMART Layers from 

Acellent Technologies, Inc.34 The SMART Layers consist of 1/4-inch-diameter, 

0.007-inch-thick piezoelectric discs, which are layered in a dielectric film with 

wiring for data collection. The SMART Layers were adhered to the surface of the 

composite coupons using aerospace-grade adhesive. The SMART Layers were 

placed approximately 3 inches from the center of the notched coupons. Actuator 

and sensor coordinates were recorded for each sample with reference to the bottomleft corner of the coupon, as shown with the coordinate axes in Fig. 4.

Ultrasonic data were actuated and collected using an Acellent ScanSentry 

32-channel data acquisition system paired with a computer with ACESS interface 

software. Figure 5 shows a photograph and diagram example of the experimental 

setup with a notched coupon. Piezoelectric elements were numbered for each 

coupon as shown in Fig. 5 with elements 1–6 at the top of the coupon acting as
Approved for public release; distribution is unlimited.

9

actuators and elements 7–12 at the bottom of the coupon acting as sensors. With 

this configuration, 36 total propagation paths with varying path angles were used 

for data collection.

Fig. 5 Experimental setup with a composite coupon loaded in a mechanical testing system

(MTS) machine (left) and the sensor schematic with example wave propagation (right)

2.3 Data Collection

At each data collection point Lamb waves were propagated from each actuator to 

each sensor, adding up to a total of 36 paths of information to analyze for each 

actuation frequency at each time point. The actuation signal was a 5-peak Hanningwindowed tone burst signal with a center frequency ranging from 150 to 450 kHz. 

The average input voltage for each signal was 50 V and the average gain was 20 dB, 

though actuation parameters were varied for each actuator-sensor event such that 

the amplitude of the sensed signal was approximately 0.8 V to avoid saturating the 

sensing system. To choose and verify the wave modes present from these 

propagation signals, dispersion curves for these composite plates were computed 

using Disperse software (by Dr Cecilia Wilson). When these actuation frequencies

were used, data with distinguishable fundamental symmetric and antisymmetric 

modes were collected. The testing apparatus is shown in Fig. 6.
Approved for public release; distribution is unlimited.

10

Fig. 6 Photographs showing a composite sample loaded for testing with AE sensors (left) 

and the thermographic imager and cables attached for ScanSentry data collection (right)

After each stage in testing the sample was removed from the testing apparatus to 

take an X-ray image to quantify the current damage. As discussed previously, 

damage in composite coupons is often internal and therefore NDE methods are 

required to “see” this “invisible” damage. X-ray images of the composite coupons 

were taken at Chesapeake Testing with an industrial X-ray and computed 

tomography (CT) machine. Coupons were treated with an X-ray opaque dye 

penetrant, 1,4 diiodobutane, which enhances X-ray absorption to illuminate damage 

on the images.27,30

2.4 Testing Procedure

Testing on all coupons begins with static loading, which progresses into tensiontension fatigue testing. Initial loading is done slowly and in steps to capture data 

while the composite sample is under loading but before damage appears. These 

initial data can be used to study the isolated effects of applied load on the various 

types of NDE. Additionally, slow initial loading allows slow progression of damage 

in the sample, allowing the collection of data containing only effects of matrix 

cracking and later data influenced by delmaination. Table 2 shows the testing 

process for each coupon. Fatigue settings for the sample are based on static-tofailure tests done previously at Stanford University to determine the ultimate tensile 

strength of the composite coupons. Maximum fatigue load was set at 70%–80% of 

the static failure strength of the laminate, and the load ratio (R) for the tests was 

approximately 0.14. The loading profile for the fatigue tests is sinusoidal at a 

frequency of 5 Hz. The steps taken during each test are outlined in the following 

sections.
Approved for public release; distribution is unlimited.

11

Table 2 Initial static and fatigue cycle count order for testing of coupons

Test no.

Total cycle 

count

Cycles for 

current test Loading type

1 0 ~1/2 Static up to 4 kips, stopping 

every 0.5 kips for NDE data

2 1 1 (effectively) Static up to 7 kips, stopping 

every 0.5 kips for NDE data

3 10 9

Fatigue at 5 Hz: max 7 kips, 

min 1 kip, mean 4 kips 

(span 3 kips)

4 100 90

5 1,000 900

6 10,000 9,000

7 20,000 10,000

8 30,000 10,000

9 40,000 10,000

10 50,000 10,000

11 60,000 10,000

12 70,000 10,000

13 80,000 10,000

14 90,000 10,000

15 100,000 10,000

16 125,000 25,000

17 150,000 25,000

18 175,000 25,000

19 200,000 25,000

20 250,000 50,000

If necessary continue past 

250,000 cycles at 50,000

intervals

2.4.1 Static Procedure

1) Take traction-free ScanSentry data.

2) Clamp sample into MTS, make sure to match central aligning marks.

3) Take clamped ScanSentry data, remove cables.

4) Take thermographic image, wait until finished.

5) Attach AE sensors and adjust for noise.

6) Begin collecting AE data.

7) Load sample 0.5 kips at a rate of 0.5 kips/min. The MTS program should 

be set up to ramp and hold at each load step.

8) Pause AE collection.

9) Remove AE sensors, wipe off gel.

10) Take loaded ScanSentry data and remove cables from sample.

11) Take thermographic image.
Approved for public release; distribution is unlimited.

12

12) Reattach AE sensors.

13) Restart AE data collection.

14) Repeat steps 7–13 for up to the second-to-last load step (i.e., through 

3.5 or 6.5 kips).

15) Repeat steps 7–11 for the last load step (i.e., at 4 or 7 kips).

16) Unload the sample from the maximum load (4 or 7 kips) at a rate of 

1 kip/min.

17) Take clamped ScanSentry data.

18) Unload the sample from the MTS machine.

19) Take traction-free ScanSentry data.

20) Take X-ray of sample before next test.

2.4.2 Fatigue Procedure

1) Take traction-free ScanSentry data.

2) Clamp sample into MTS.

3) Take clamped ScanSentry data, remove cables.

4) Take thermographic image.

5) Attach AE sensors and adjust for noise.

6) Begin collecting AE data.

7) Load sample to 4 kips at a rate of 1 kip/min.

8) Pause AE collection.

9) Remove AE sensors, wipe off gel.

10) Take loaded ScanSentry data and remove cables from sample.

11) Take thermographic image.

12) Reattach AE sensors.

13) Restart AE data collection.

14) Begin fatigue cycling for the number of “Cycles in Current Test” as 

indicated in Table 1. Fatigue settings are a mean of 4 kips and span of 

3 kips (i.e., min 1 kip and max 7 kips) at 5 Hz.
Approved for public release; distribution is unlimited.

13

15) Stop AE data collection, remove AE sensors and wipe off gel.

16) Take loaded ScanSentry data and remove cables.

17) Take thermographic image.

18) Unload sample from 4 kips to 0 at a rate of 1 kip/min.

19) Take clamped ScanSentry data.

20) Unload sample from MTS.

21) Take traction-free ScanSentry data.

22) Take X-ray of sample before next test.

2.5 Experiment Results

Table 3 summarizes the 3 samples tested during this study.

Table 3 Summary of completed tests at the US Army Research Laboratory (ARL)

Sample Test no. completed Loading completed Observed damage

L1S01 2 Static: 7 kips Small delamination and 

few matrix cracks

L1S02 1 Static: 4 kips Few matrix cracks

L1S03 1 Static: 4 kips Few matrix cracks

Examples of an extracted X-ray image and the AE data collected are shown in 

Fig. 7 and an example of a 3-D X-ray CT of damage is shown in Fig. 8. After the 

dye penetrant was applied, damage was visible on the X-ray CT scans. Without dye 

penetrant there was not enough contrast between damaged and undamaged sections 

for the damage to be visible on X-ray CT. The damage location shown on the Xrays correlates with the observed AE hits during experimentation as shown in 

Fig. 7.
Approved for public release; distribution is unlimited.

14

Fig. 7 Extracted X-ray image (left) from the X-ray CT scan of L1S01 after 7 kips loading 

showing a small delamination and matrix cracking around the notch compared with collected 

AE hits (right) during the test

Fig. 8 A 3-D view of the damage in L1S01 after 7 kips loading. The dye penetrant 

improves contrast between damaged and undamaged sections.
Approved for public release; distribution is unlimited.

15

3. Signal Processing

During experimentation, NDE data are gathered in the form of AE data, 

thermographic images, and guided-wave signals. As stated earlier, one of the main 

contributions of this report is in the processing and interpreting of the guided-wave 

data. Future work will included analysis of data from acoustic emission sensors and 

flash thermography. In this report, we explain the results gathered from the 

ScanSentry guided-wave data.

3.1 Ultrasonic Signal Response

To assess the effects of load and damage on Lamb waves, the collected wave data 

must by processed and analyzed. Because guided-wave properties are dependent 

on the medium through which they propagate—as discussed in Section 1—when 

the host material is affected by applied load or damage, the wave signal is affected 

as well. By extracting features from the sensor signal and examining their change 

over time, parameters sensitive to the applied load and/or present damage can be 

identified. Qualitatively there are noticeable differences in sensor signal when it is 

effected by applied load and different levels of damage. Following are examples of 

raw sensor signal showing some of these variations. Figures 9 and 10 show how 

increasing levels of applied load on a coupon effect guided-wave data propagating 

through that coupon without damage effects.
Approved for public release; distribution is unlimited.

16

Fig. 9 Raw signal of L1S01 from path 5to8 at 150-kHz actuation frequency as the load 

increases from 0 to 4 kips

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Time (s) 10 -4

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Voltage (V)

L1S01 path5to8 150 kHz

0 kips

0.5 kips

1 kips

1.5 kips

2 kips

2.5 kips

3 kips

3.5 kips

4 kips
Approved for public release; distribution is unlimited.

17

Fig. 10 Zoom in of the first wave packet (top) and one peak (bottom) of data collected from 

L1S01 path 5to8 as the load increases showing the change in sensor signal (no damage present)

3 3.5 4 4.5 5 5.5 6 6.5 7 7.5

Time (s) 10 -5

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Voltage (V)

L1S01 path5to8 150 kHz

0 kips

0.5 kips

1 kips

1.5 kips

2 kips

2.5 kips

3 kips

3.5 kips

4 kips

5.45 5.5 5.55 5.6 5.65

Time (s) 10 -5

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Voltage (V)

L1S01 path5to8 150 kHz

0 kips

0.5 kips

1 kips

1.5 kips

2 kips

2.5 kips

3 kips

3.5 kips

4 kips
Approved for public release; distribution is unlimited.

18

3.2 Feature Extraction

Based on initial raw data analysis and dispersion curves created pre-experiment, 

the most consistently clear wave packet was the first arrival, the S0 mode. Figure 11

shows an example of the sensed signal from an actuator-sensor pair with the 

actuation frequency of 150 kHz. In each data set, the first arriving wave packet is 

identified, as shown in Fig. 11, and analyzed for signal parameters in the time and 

frequency domains. Figure 12 shows the amplitude (A) and time of fight (TOF) 

were studied in the time domain and are extracted from the window enclosing the 

first arriving wave packet. 

Fig. 11 Example of the raw signal and the windowed first wave packet from L1S01 path 4to9 

150-kHz actuation frequency

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Time (s) 10 -4

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Voltage (V)

Raw Signal L1S01 path5to8 150 kHz

First Wave 

Packet
Approved for public release; distribution is unlimited.

19

Fig. 12 Example of how the time domain features of TOF and amplitude are calculated from 

the sensed signal by using the envelopes of the waveforms path 4to9 150-kHz actuation 

frequency

The amplitude relates to the amount of displacement caused by the propagating 

Lamb wave and is complexly related to the structural properties and attenuation 

caused by the material. Signal amplitude changes were extracted using Eq. 1, where 

s is the sensed signal and H is the Hilbert transform, which finds the envelope of 

the signal 

AA = max(|HH(ss)|). (1)

TOF is the measure of the length of time taken by an actuation signal to reach a 

sensor. The TOF gives a sense of the Lamb wave velocity, which is related to the 

material properties of the composite. The TOF calculation is given in Eq. 2

where a is the actuation signal and t is elapsed time.

TTTTTT = tt(max(|HH(ss)|)) − tt(max(|HH(aa)|)). (2)

The data can also be analyzed in the frequency domain by taking the short-time 

Fourier transform (STFT) of the sensor signal, as show in Eq. 3 where S(n) is the 

signal, F is the sampling frequency, and m is the discrete size of the window w. For 

each actuation frequency ω a STFT was performed to extract frequency-domain 
Approved for public release; distribution is unlimited.

20

features. The features examined were the maximum power spectral density (PSD) 

value, shown in Eqs. 4 and 5, and the total energy of the wave packet, shown in 

Eq. 6. An example of the calculated total PSD of a signal (as in Eq. 4) and the PSD 

extracted at a certain frequency (as used to find the maximum in Eq. 5) are shown 

in Figs. 13 and 14, respectively.

SS [SS(nn)] = SS(mm, ωω) = ∑ ss(nn)ww(nn − mm)ee ∞ −jjjjjj =−∞ , (3)

PP PP[SS(nn)] = PP(mm, ωω) = |SS(mm, )|

2

FF ∑ |ww( )| LL 2 nn=1

, (4)

PP PPPP PP(ωω) = max�PP(mm, ωω)�, (5)

and

EE EEEEEE(ωω) = ∑mm PP(mm, ωω). (6)

When used in analysis, all features are normalized by their corresponding baseline 

value for that path and that sample. In this way comparisons between coupons can 

be made and some of the variability effects between coupons are filtered out. These 

features were then analyzed to find one, combinations, and/or ratios that are 

sensitive to applied load or damage. Ideally, differing features that are more 

sensitive to one or the other effect can be identified so that different combinations

of features can be used to diagnose applied load, classify damage type, and quantify 

damage severity.
Approved for public release; distribution is unlimited.

21

Fig. 13 Example of the PSD of L1S01 path 4to9 150-kHz actuation under a 0-kips load

Fig. 14 Extracted PSD of the actuation frequency (150 kHz) of L1S01 path 4to9 at 0 kips 

(i.e., one slice at 150 kHz extracted from Fig. 13)

PSD L1S01 path4to9 150 kHz 0 kips

2 4 6 8 10 12 14

Time (s) 10 -5

0

0.5

1

1.5

2

2.5

3

Frequency (Hz)

10 5

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Time (s) 10 -4

0

1

2

3

4

5

6

7

8

PSD (V 2 /Hz)

PSD @150 kHz L1S01 path4to9 0 kips
Approved for public release; distribution is unlimited.

22

4. Signal Analysis

This section discusses analysis of sensed signal for effects from applied load. The 

goal of sensing these changes contributes toward the eventual overall goal of 

correctly diagnosing damage during operating conditions. 

Development of accurate diagnostic algorithm requires identification of features 

sensitive to the effects of load-only, damage-only, and the combined effects of loaddamage. Hence, the collected experimental data are analyzed for the qualitative 

differences driven by the effects outlined previously. Progression of applied load 

in the data will allow finding specific signal features that correlate to these effects. 

The wave data collected in the static loading at the beginning of each test (before 

damage propagation has begun) contain only effects from the applied load on the 

coupons at increasing load levels. In analyzing these data, the actuation frequencies 

that resulted in clear first wave packets were narrowed down to the 3 frequencies,

150, 200, and 250 kHz, and among these 3 frequencies the data from 150 kHz 

actuation are the clearest. Additionally only the propagation paths, which match the 

loading direction (“straight” paths, which are paths 2to11, 3to10, 4to9, and 5to8 in 

Fig. 15) are used in the analysis to avoid complications that arise when the 

directions are not concurrent. As the applied load and local material properties 

“seen” by paths at an off-angle direction from the loading direction and orientation 

of the composite layup, merging all data from different orientations together with 

directionality compensation would be incorrect.
Approved for public release; distribution is unlimited.

23

Fig. 15 Schematic showing actuator and sensor numbering and the 4 straight paths used in 

analysis whose direction coincides with the shown loading direction

Qualitative observations were done on the raw sensed signal to assess general signal 

changes and establish preliminary trends. Figure 10 shows the first wave packet of 

a coupon as the applied load on the coupon increases incrementally from 0 to 4 kips. 

It can be observed that there is a slight, steady increase in signal amplitude as the 

applied load increases and also a slight, steady decrease in the time the signal 

reaches that maximum amplitude, and therefore in the time of flight as the applied 

load increases. Similarly, when examining how the PSD profile changes at the 

actuation frequency of the signal with load in Fig. 16 an increase in the maximum 

PSD value and an increase in energy (the area under the curve) is observed as 

applied load increases.
Approved for public release; distribution is unlimited.

24

Fig. 16 Extraction of the PSD of L1S01 at the actuation frequency 150-kHz path 5to8 as 

applied load increases

Signal features that have been found to be most influenced by applied load are those 

described in the previous section: amplitude, maximum PSD value, and signal 

energy. Time of flight is also influenced by applied load, but less consistently than 

these other signal features. Plots showing the change in these 4 features (normalized 

to the baseline value for each coupon) as increased load is applied are shown for 

each straight path: amplitude of path 2to11 in Fig. 17, TOF of path 5to8 in Fig. 18, 

maximum PSD of path 3to10 in Fig. 19, and energy of path 4to9 in Fig. 20.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Time (s) 10 -4

0

5

10

15

20

25

PSD (V 2 /Hz)

PSD L1S01 path 5to8 @150 kHz

0 kips

0.5 kips

1 kips

1.5 kips

2 kips

2.5 kips

3 kips

3.5 kips

4 kips
Approved for public release; distribution is unlimited.

25

Fig. 17 Normalized amplitude change in the first wave packet of data with actuation 

frequency 150 kHz from path 2to11 for ARL-tested coupons

Fig. 18 Normalized TOF change in the first wave packet of data with actuation frequency

150 kHz from path 5to8

0 0.5 1 1.5 2 2.5 3 3.5 4

Load (kips)

1

1.1

1.2

1.3

1.4

1.5

1.6

Amplitude / Baseline

Amplitude Change vs Load path2to11 150 kHz

L1S01

L1S02

L1S03

0 0.5 1 1.5 2 2.5 3 3.5 4

Load (kips)

0.986

0.988

0.99

0.992

0.994

0.996

0.998

1

Time of Flight / Baseline

TOF Change vs Load path5to8 150 kHz

L1S01

L1S02

L1S03
Approved for public release; distribution is unlimited.

26

Fig. 19 Normalized maximum PSD value change in the first wave packet of data with 

actuation frequency 150 kHz from path 3to10

Fig. 20 Normalized energy change in the first wave packet of data with actuation frequency 

150 kHz from path 4to9

0 0.5 1 1.5 2 2.5 3 3.5 4

Load (kips)

1

1.2

1.4

1.6

1.8

2

2.2

Peak PSD Value / Baseline

Peak PSD Change vs Load path3to10 150 kHz

L1S01

L1S02

L1S03

0 0.5 1 1.5 2 2.5 3 3.5 4

Load (kips)

1

1.2

1.4

1.6

1.8

2

2.2

Energy / Baseline

Energy Change vs Load path4to9 150 kHz

L1S01

L1S02

L1S03
Approved for public release; distribution is unlimited.

27

Figures 17, 19, and 20 show that above 3 kips load inflection points begin to appear 

in the data—this is indicative of damage initiation and progression. Based on 

Figs. 17–20 and similar plots from other paths and actuation frequencies, X-ray 

images taken after the initial loading cycle, and notes about audible cracking during 

tests, it can be hypothesized that matrix cracking can begin in a sample between 

2.5- and 4-kips load. By filtering out data beyond these inflection points (based on 

the hypothesis that they are also affected by damage), Lamb wave data influenced 

only by applied load can be isolated. This will be done when creating modeling 

strategies to predict applied load from ultrasonic data, as only data with applied 

load influences and not those from the combined influences of applied load and 

data should be used for this inverse problem.

5. Conclusions and Future Work

From the results in previous sections, a clear effect is observed from applied load 

on the ultrasonic wave data. Using these trends, mathematical models that estimate 

the applied load from the sensor signal can be developed. By combining the data 

shown in this report with previous work by the authors, multiple linear regression 

models using various combinations of the sensor signal features as predictors for 

the applied load can be trained and tested. These models will be a part of a future 

work. 
Approved for public release; distribution is unlimited.

28

6. References

1. Michaels K. Market trends: aerospace composites market will quadruple by 

2026. Composites World; n.d. [accessed 2007 July]. http://www

.compositesworld.com/columns/market-trends-aerospace-composites-market

-will-quadruple-by-2026.

2. Soutis C. Recent advances in building with composites. Plast Rubber Compos.

2009;38(9–10):359–366.

3. Williams A. Aerospace growth climbs with carbon fiber. Composites World;

n.d. [accessed 2016 July 12]. http://www.compositesworld.com/columns

/aerospace-growth-climbs-with-carbon-fiber.

4. Curtis PT, Dorey G. Fatigue of composite materials. In: Proc Instn Mech Engrs, 

Part G. J Aero Engr. 1989;203(1):31–37.

5. Gay D. Composite materials: design and applications. 3rd ed. Boca Raton (FL): 

CRC Press; 2014.

6. Soutis C. Carbon fiber reinforced plastics in aircraft construction. Mater Sci 

Eng A. 2005;412(1–2):171–176.

7. Yancey R. Laminated composites: The original additive manufacturing 

process. Composites World; n.d. [accessed 2016 Mar]. http://www

.compositesworld.com/articles/laminated-composites-the-original-additive

-manufacturing-process.

8. Kollar LP, Springer GS. Mechanics of composite structures. New York (NY): 

Cambridge University Press; 2003.

9. Beaumont PWR, Dimant RA, Shercliff HR. Failure processes in composite 

materials: getting physical. J Mater Sci. 2006;41(20):6526–6546.

10. Poursartip A, Ashby MF, Beaumont PWR. The fatigue damage mechanics of a 

carbon fibre composite laminate: I-development of the model. Compos Sci 

Technol. 1986;25(3):193–218.

11. Waddoups ME, Eisenmann JR, Kaminski BE. Macroscopic fracture mechanics 

of advanced composite materials. J Compos Mater. 1971;5(4):446–454.

12. Wisnom MR, Chang F-K. Modelling of splitting and delamination in notched 

cross-ply laminates. Compos Sci Technol. 2000;60(15):2849–2856.
Approved for public release; distribution is unlimited.

29

13. Talreja R. Damage mechanics and fatigue life assessment of composite 

materials. Int J Damage Mech. 1999;8:339–354.

14. Hallett SR, Jiang WG, Khan B, Wisnom MR. Modelling the interaction 

between matrix cracks and delamination damage in scaled quasi-isotropic 

specimens. Compos Sci Technol. 2008;68(1):80–89.

15. Starke EAJ, Cornelia RH, Greszczuk LG, Karabin LM, Lewandowski JJ, 

Saxena A, Seferis JC, Tressler RE, Ward DD. Accelerated aging of materials 

and structures. The effects of long-term elevated-temperature exposure. 

Washington (DC): National Academy Press; 1996.

16. O’Brien TK. Characterization of delamination onset and growth in a composite 

laminate. Hampton (VA): Langley Research Center; 1982. NASA Tech 

Report.: NASA-TM-81940.

17. Scott IG, Scala CM. A review of non-destructive testing of composite 

materials. NDT Int. 1982;15(2):75–86.

18. Sloan J. The NDE challenge. Composites World; n.d. [accessed 2016 May]. 

http://www.compositesworld.com/columns/the-nde-challenge.

19. Cawley P. Inspection of composites – current status and challenges. In: 9th 

European Conference on Nondestructive Testing; 2006 Sep 25–29; Berlin, 

Germany.

20. Gholizadeh S. A review of non-destructive testing methods of composite 

materials. In: Procedia structural integrity. 2016;1:50–57.

21. Liu B, Lessard LB. Fatique and damage-tolerance analysis of composite 

laminates: Stiffness loss, damage-modelling, and life prediction. Compos Sci 

Technol. 1994;51:43–51.

22. Farrar CR, Worden K. An introduction to structural health monitoring. Philos 

Trans R Soc A. Math Phys Eng Sci. 2007;365(1851):303–315.

23. Haile MA, Hall AJ, Yoo JH, Coatney MD, Myers OJ. Detection of damage 

precursors with embedded magnetostrictive particles. J Intel Mater Syst Struct. 

2015;27(12):1567–1576.

24. Haile MA, Riddick JC, Assefa AH. Robust particle filters for fatigue crack 

growth estimation in rotorcraft structures. IEEE Trans Reliability. 

2016;65:1438–1448.
Approved for public release; distribution is unlimited.

30

25. Haile M, Ghoshal A. Application of compressed sensing in full-field structural 

health monitoring. In: Proc. SPIE 8346, Smart Sensor Phenomena, Technology, 

Networks, and Systems Integration; San Diego (CA). 2012. p. 46–53.

26. Haile MA, Ifju PG. Application of elastic image registration and refraction 

correction for non‐contact underwater strain measurement. Strain.

2012;48(2):136‒142

27. Haile MA, Chen TK, Sediles F, Le D. Estimating crack growth in rotorcraft 

structures subjected to mission load spectrum. Int J Fatigue. 2012;43:142–149.

28. Leo DJ. Piezoelectric materials. In: Engineering analysis of smart material 

systems. 1st ed. Hoboken, (NJ): John Wiley & Sons; 2007. p. 122–204.

29. Worden K. Rayleigh and lamb waves - basic principles. Strain. 2001;37(4):167–

172.

30. Su Z, Ye L, Lu Y. Guided lamb waves for identification of damage in 

composite structures: a review. J Sound Vib. 2006;295(3–5):753–780.

31. Williams WB, Michaels TE, Michaels JE. Characterization of guided wave 

velocity and attenuation in anisotropic materials from wavefield measurements.

In: 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation 

(QNDE), AIP Conf. Proc; 1706, 2016. vol. 1706. p. 30002.

32. Harris B. Fatigue in composites: science and technology of the fatigue response 

of fibre-reinforced plastics. 1st ed. Boca Raton (FL): Woodhead Publishing 

Ltd; 2003.

33. Larrosa CC. Monitoring matrix cracking in composite laminates using built-in 

piezoelectric sensors. Stanford (CA): Stanford University; 2013.

34. Lin M, Qing XP, Kumar A, Beard SJ. SMART layer and SMART suitcase for 

structural health monitoring applications. In: Proc. SPIE 4332, smart structures 

and materials 2001: industrial and commercial applications of smart structures;

2001. vol. 4332. p. 98–106.
Approved for public release; distribution is unlimited.

31

List of Symbols, Abbreviations, and Acronyms

3-D 3-dimensional

AE acoustic emission

ARL US Army Research Laboratory

CT computed tomography

MTS mechanical testing system 

NDE nondestructive evaluation

PSD power spectral density

PZT lead zirconate titanate 

SHM structural health monitoring

STFT short-time Fourier transform

TOF time of flight
Approved for public release; distribution is unlimited.

32

1 DEFENSE TECHNICAL

(PDF) INFORMATION CTR

DTIC OCA

2 DIRECTOR

(PDF) US ARMY RESEARCH LAB

RDRL CIO L

IMAL HRA MAIL & RECORDS

MGMT

1 GOVT PRINTG OFC

(PDF) A MALHOTRA

4 DIR USARL

(PDF) RDRL VT

E RIGAS

RDRL VTM

C ROSANIA

M HAILE

J RIDDICK