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Use of Infrared Thermography in a Data Fusion Framework for Thermal and Damage Properties Quantification A Thesis Submitted to the Faculty Of Drexel University By Satish S. Rajaram in partial fulfillment of the requirements for the degree of Masters of Science in Mechanical Engineering June 2013

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Page 1: Use of Infrared Thermography in a Data Fusion Framework ...4252/datastream... · Infrared Thermography (IRT) is already a widely used technique for Non-Destructive Testing (NDT) for

Use of Infrared Thermography in a Data Fusion Framework for Thermal and

Damage Properties Quantification

A Thesis

Submitted to the Faculty

Of

Drexel University

By

Satish S. Rajaram

in partial fulfillment of the

requirements for the degree

of

Masters of Science in Mechanical Engineering

June 2013

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Dedication

I would like to dedicate this work to my all my closest friends and family, especially my

Mother, Father and Sister. It because of you all that I am able to accomplish this work, to

find the joy in life, the ones who always give me advice when needed and keep me

focused. Even thru the toughest of times you have stuck with me every step of the way

and it’s something I will always cherish.

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Acknowledgements

I would like to thank Dr. Antonios Kontsos for allowing me the opportunity to join his

research group, TAMG. You are one of the smartest and most passionate people I have

ever met and your enthusiasm seeps into all the projects we have worked on and I could

not be happier with my decision to work with you. More importantly it is the care and

dedication to your student’s success that I have always appreciated. You are not just an

advisor to me but someone I look up to and consider a friend. Dr. Bartoli I truly enjoyed

getting to work with you and expand my knowledge in Civil Engineering, you always

have an easy going and fun approach to the projects we have worked on and always make

them more enjoyable.

I would like to thank all of my TAMG colleagues: Aditi, Utkuu, Rami, Andrew, Allison,

Lara, Fuad, Shane, Eric, Raghav who have all been great help to accomplish my work or

have been good friends that improved my time at Drexel. I would like to express my

great appreciation for Jefferson Cuadra, Prashanth Abraham and Kavan Hazeli, the first

three members of the group that I met. You three have made such an impression on me

with your hard work, dedication, and knowledge in so many different fields. More

importantly I consider you all great friends and having you guys around to ask questions

and help me with my work has been invaluable and I know great things are ahead in all

your futures

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Table of Contents

List of Tables ..................................................................................................................... iv

List of Figures ..................................................................................................................... v

Abstract ............................................................................................................................ viii

Chapter 1: Motivation ......................................................................................................... 1

1.1 Introduction and Background ............................................................................... 1

1.2 Thesis Objective ................................................................................................... 4

1. 3 Chapter Contents and organization .......................................................................... 5

Chapter 2: Infrared Thermography ..................................................................................... 7

2.1 Historical Background............................................................................................... 9

2.2 Principles of Infrared Thermography ...................................................................... 11

2.3. Infrared Camera Background ................................................................................. 14

2.4 Passive Thermography and Active Thermography ................................................. 16

Chapter 3: Infrared Thermography in Non-Destructive Evaluation ................................. 26

3.1 Coefficient of Thermal Expansion .......................................................................... 26

3.2 Coefficient of Thermal Expansion of Anisotropic Materials .................................. 34

3.3 Damage Detection in Materials ............................................................................... 37

3.4 Application of IRT in Infrastructure Evaluation ..................................................... 44

Chapter 4: Validation of Using IRIC To Measure CTE ................................................... 48

4.1 Experimental setup of CTE Calculation.................................................................. 49

4.2 Calculating Local Coefficient of Thermal Expansion Values ................................ 57

Chapter 5: Application of IRIC as Damage Identification ............................................... 60

5.1 Heating Damaged AL2024 Specimen ..................................................................... 60

5.2 Damage Progression in GFR Composite ................................................................ 66

Chapter 6: Concluding Remarks and Future Work........................................................... 74

6.1 Concluding Remarks ............................................................................................... 74

6.2 Future Work ............................................................................................................ 74

Appendix A: Matlab Code to Calculate Temperature Gradient ....................................... 76

List of References ............................................................................................................. 78

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List of Tables

Table 1: Infrared Sub-regions ............................................................................................. 8

Table 2: Emissivity Value for Metallic and Non-Metallic Materials [8].......................... 14

Table 3: Comparison of Active IRT Techniques .............................................................. 25

Table 4: Defect Depth estimate by 3 PT methods [64] ..................................................... 43

Table 5: Properties of Aluminum 2024 T-3 ...................................................................... 49

Table 6: FLIR A3254sc Specifications [71] ..................................................................... 50

Table 7: Experiment 1 Average Strain and CTE Values Obtained in X and Y ................ 54

Table 8: Experiment 2 Strain and CTE Values ................................................................. 56

Table 9: Local Strain and CTE Values Measured in Region 1 and 2 ............................... 59

Table 10: CTE Values of Damaged Specimen ................................................................. 62

Table 11: GFREL Experimental Bulk Properties ............................................................. 66

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List of Figures Figure 1: Integrated Structural Health Monitoring Approach [15] ..................................... 4

Figure 2: Generalized Thesis Layout .................................................................................. 6

Figure 3: Electromagnetic Spectrum .................................................................................. 7

Figure 4: Advancement of Infrared Thermography .......................................................... 11

Figure 5: Planck and Wien’s Displacement Laws Graphical Representation .................. 13

Figure 6: Air Leakage in Building Detected using PT ..................................................... 17

Figure 7: Typical Passive Thermography Setup ............................................................... 17

Figure 8: Common setup for an Active Thermography Setup .......................................... 18

Figure 9: Setup for Lock-in Thermography [29] .............................................................. 20

Figure 10: Pulsed Phase Thermography Setup ................................................................. 22

Figure 11: A) Thermal Image Obtained From Pulse Thermography B) Image Obtained from Pulsed Phase Thermography .................................................................................... 22

Figure 12: Vibro-thermography Setup [41] ...................................................................... 23

Figure 13: Setup of Thermo-sonic Imaging ...................................................................... 24

Figure 14: Length vs. Temperature of sample material [50] ............................................ 27

Figure 15: Mechanical Dilatometry using double push setup [50] ................................... 28

Figure 16: Typical Arrangement for Optical Interferometers [50] ................................... 29

Figure 17: Thermo-mechanical Analyzer setup [49] ........................................................ 30

Figure 18: Temperature Change caused Be Applied Uniaxial Tensile Stress [53] .......... 32

Figure 19: Experimental Setup for Proposed IRIC Method a) Heating Chamber b) Measurement Setup [55] ................................................................................................... 33

Figure 20: a) Close up of Speckle Patter b) specimen used c) Close up of speckle and non-speckle region [55] .................................................................................................... 33

Figure 21: A) Strain vs. ΔT for Aluminum Alloy B) Strain vs. ΔT for GFRP C) Measured Values of CTE for AL Alloy and GFRP ........................................................................... 34

Figure 22: Micromechanical Models for transverse CTE of composites ......................... 36

Figure 23: Uses of IR in NDE........................................................................................... 38

Figure 24: (a) Measured tensile stress vs strain curve correlated with the distrabution of of extrated amplitude of AE events and temperature differential values (b) full field strain

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maps (top) and temperature differential maps (bottom) (c) The raw images of specimen [9] ...................................................................................................................................... 39

Figure 25: Temperature Contrast Method [64] ................................................................. 40

Figure 26: Stainless Steel Sample with Embedded defects [64] ....................................... 41

Figure 27: Thermal images taken after different time intervals [64] ................................ 42

Figure 28: Curve of Load-elongation with Thermal Images of the Drawing Fiber [65] .. 44

Figure 29: Infrared Thermography can identify regions of significant heat loss [17] ...... 45

Figure 30: A) Initial IR Image B) Iinitial IR Image with Lower Temperature Range C) Green areas are Delmaination D) Close up of Delamination Area [17] ........................... 46

Figure 31: Shows Void Placement in Concrete along with Depth and Estimate by Radar [66] .................................................................................................................................... 47

Figure 32: (Top) Thermal Images of Concrete Structure after Heating (Bottom) Void detection at different frequencies [66] .............................................................................. 47

Figure 33: IRIC Setup ....................................................................................................... 48

Figure 34: FLIR A325sc Camera ...................................................................................... 50

Figure 35: Experimental setup Calculating CTE of AL2024 ........................................... 51

Figure 36: A) Strain Map in X B) Strain Map in Y C) Thermal Image with ROI around the specimen...................................................................................................................... 52

Figure 37: A) Average Strain in X and Y of AL2024 B) Average Temperature of AL2024........................................................................................................................................... 53

Figure 38: Average Strain in X and Y plotted against Change in Temperature ............... 54

Figure 39: A) Temperature Profile of AL2024 Sample B) Average Strain Profile in X and Y Directions ...................................................................................................................... 55

Figure 40: Experiment 2 Strain vs. Temperature .............................................................. 56

Figure 41: Local CTE Regions ......................................................................................... 58

Figure 42 A) Local Strain Values XY Measured in AL2024 B) Temperature Profile for Region 1 and Region 2 C) Local Strain Y in Region 1 and 2 D) Local Strain Values in X vs. Change in Temperature ............................................................................................... 58

Figure 43: (Left) Undamaged Sample from Chapter 4 (Right) Damaged Sample .......... 61

Figure 44: Damaged sample 1 with two partitions A) Thermal image B) Strain map of specimen ........................................................................................................................... 61

Figure 45: (Top left) initial Heating Stage, (Top Right) Formation of defects (Bottom) Clearly visible Defects ...................................................................................................... 62

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Figure 46: A) Strain vs. Time of Top and Bottom Regions B) Temperature Profile of Specimen ........................................................................................................................... 63

Figure 47: Average Strain in Y during Thermal Loading of D1 ...................................... 64

Figure 48: Local regions and corresponding CTE values ................................................. 64

Figure 49: A) D1 Temperature Field B) D1 Temperature Gradient X Direction C) Temperature Gradient Y ................................................................................................... 65

Figure 50: GREFL Specimen used during testing ............................................................ 67

Figure 51: Micro II Digital AE System ............................................................................ 68

Figure 52: MTS Servohydraulic Server used during tensile testing ................................. 69

Figure 53: Load and Absolute Energy of Tensile Test 1 .................................................. 70

Figure 54: Temperature profile of GFREL during tensile test till failure......................... 71

Figure 55: GFREL tensile test after 170 sec A) Strain in X(µm/m) B) Strain in Y(µm/m) C) Temperature profile (°C) D) Temperature gradient in X (°C/mm) E) Temperature gradient in Y (°C/mm) ...................................................................................................... 72

Figure 56: Actual specimen during test at 170 seconds .................................................... 72

Figure 57: A) Surface Damage Occurs on GFREL last DIC Image, 182 seconds B) Thermal Image 182 seconds C) Thermal Image 190 seconds D) Thermal Image 199 seconds. ............................................................................................................................. 73

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Abstract Use of Infrared Thermography in a Data Fusion Framework for Thermal and

Damage Property Quantification Satish S. Rajaram

Antonios Kontsos, Ph.D. Ivan Bartoli, Ph.D

Infrared Thermography (IRT) is already a widely used technique for Non-Destructive

Testing (NDT) for infrastructure evaluation and damage detection. This thesis has two

goals; the first is to calculate a thermo physical property, the Coefficient of Thermal

Expansion (CTE) using two non-contact and full field measurement techniques, IRT and

Digital Image Correlation (DIC) which provide temperature and displacement fields,

respectively. The second goal is to use IR and DIC (IRIC) as a full field non-contact

technique that allows not only measurements of a thermal property but can further detect

damage within a material. By providing full field data it is shown that it is possible to

measure CTE in partitioned regions which can be used to identify damage.

Simultaneously the accurate measurement of thermal expansion can be used to

differentiate thermal strains generated in a material due to thermo-elastic effects from the

mechanical induced strain and therefore assist the understanding of the behavior of a

material. Literature reviews show a multitude of ways to calculate just CTE which

demonstrates the novelty of this thesis to simultaneously measure a material property and

identify damage. In summary, the aim of this thesis is to produce a quick, efficient and

reliable technique to provide accurate CTE measurements of both isotropic and

anisotropic materials, which can potentially be used to make corrections of strain

measurements induced by mechanical loading, and therefore provide a way to identify

progressive damage.

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Chapter 1: Motivation

1.1 Introduction and Background The focus of this thesis is twofold; first to develop an accurate non-contact technique

based on the combined use of full field methods of IRT and DIC to calculate the

Coefficient of Thermal Expansion (CTE) an important thermal property needed in design

of structures and components. The second is to investigate the hypothesis that this

technique and the measured thermal properties can be used to provide both existing and

progressive damage descriptions.

Nondestructive Testing and Evaluation (NDT&E) methods have long been used in

conjunction with mechanical and structural testing methods to measure materials

properties and quantify damage behavior. By using NDT methods such as ultrasonic

measurement of relative velocity, magnetic methods, electrical methods etc… mechanical

properties such as strength and stiffness have been measured [1]. In Fiber reinforced

Composites (FRP) individual defects may affect strength characteristics; simultaneously

two same size voids may affect ultimate strength of FRP differently if for example the

fiber volume fractions of two fiber composites are not exactly the same. Acouto-

ultrasonic techniques, a combination of Acoustic Emissions (AE) and ultrasonic, provides

a way to predict the ultimate performance of a newly made fiber composite [2].

Ultrasonic Testing [3] has been used to examine concrete structures to determine property

materials such as dynamic moduli of elasticity and damage states due to fracture. Pulsed

Eddy Current (PEC) [4] is an emerging NDT technique using broadband pulse excitation

with rich frequency information used for defect detection. Kontsos et al.[5] reported on

the mechanical behavior characterization of a nanocrystalline Mg-Matrix composite

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reinforced with Ti2AlC by using Acoustic Emission (AE). The results they obtained

provided evidence related to the activation and evolution of a possibly novel deformation

mechanism in this nanocomposite. Research has been done using multiple NDT

techniques, Reis et al. [6] investigated residual stiffness and temperature rise during

fatigue damage using thermographic and AE techniques for polypropylene/glass fiber

composite. They concluded that it was possible to use the residual stiffness and

temperature rise to predict final failure of a structure or component. The temperature

maps obtained from thermographic techniques were capable to map precise sites of

failure.

There are two major driving forces for the research and applications described in this

thesis. The first stems from the effort to develop robust procedures for applying non-

contact measurements methods to compute properties and evaluate changes in materials

and structures, and therefore address some of the constraints imposed by having to attach

a number of sensors (e.g. strain gauges, thermocouples, and dilatometer) on the surface of

the inspected specimen. The second is based on the increasing improvement of full-field

measurement methods and the need to increase both their metrology, as well as their

diagnostic and prognostic capabilities through both their integration with existing test

setups, as well as though the development and use of data fusion approaches both at the

hardware and software level.

In this thesis, two measurement technologies are primarily used: a) infrared

thermography and b) digital image correlation. Infrared thermography (IRT) is a full field

non-contact measurement method that transforms the thermal energy emitted by an object

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in the infrared band of the electromagnetic spectrum into a visible image in the form of

temperature maps [7]. Infrared Thermography has been developed since the early 1900s,

primarily starting with military applications. As technology IRT continues to improve,

and it has been applied to measure temperature maps in large structures including

buildings, bridges and aircrafts, as well as in components/processes. Infrared

thermography has been further applied in defect detection and characterization,

maintenance, medical/veterinary, properties and public services [8]. To address some

limitations of IRT, including variable emissivity, absorption of IR signal by the

atmosphere, transitory nature of thermal images and the depth of a defect, hybrid NDT

methods by combining various techniques that include IRT and other NDT methods have

been developed [9-12].

Digital Image Correlation (DIC) is a full field optical metrology technique used for

measuring deformation surface deformation. Digital images can be recorded using one or

a pair of fixed CCD high resolution cameras. Results include 2D or 3D in and out of

plane measurements and are based on comparison of the un-deformed, or reference, to

deformed states. In principle DIC is based on digital image processing and numerical

computing. The shape change and relative moment is tracked by a random or regular

speckle pattern applied to the surface of the specimen which is used to create a light

grayscale intensity field. DIC has been used to examine strain localizations in material

testing, fracture mechanics studies, progressive damage mointoring in composites,

structural behavior, high temperature strain mapping and dynamic vibrational analysis

[13-15]. Vanniamparambil [15] has used DIC as part of an integrated Structural Health

Monitoring (SHM) approach for crack growth monitoring of AL-2024 compact tension

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specimen. This novel SHM approach combined DIC with Guided Ultrasonic Waves

(GUW) and Acoustic Emissions (AE) and produced a proof-of-concept investigation of

real-time optical and acoustic nondestructive testing as seen in Figure 1.

Figure 1: Integrated Structural Health Monitoring Approach [15]

1.2 Thesis Objective The first goal of this thesis is to combine IRT and DIC to validate as a full field and non-

contact hybrid NDT technique to calculate CTE. The second goal is using successful

measurement of CTE as an indicator of damage which should be able to be identified

using kinematic and thermal fields obtained in calculating CTE. Almost all known

techniques for CTE measurements have used thermal chambers with temperature probes;

these techniques are reviewed in sections 3.1 and 3.2, this thesis will show that

alternative heating sources can be used as a means to measure acceptable thermal

expansion values. In order to obtain these goals the first step is to measure and obtain

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acceptable values compared with reported literature results to show the validity of this

technique. The second is to measure CTE of a damaged and undamaged specimen to

show the change in material property due to damage but also use the change in thermal

expansion as a means to identify damage within a material.

1. 3 Chapter Contents and organization Chapter 1 has two important goals; the first is lay the foundation the rest of this thesis

will proceed to follow. The second is to lay the goals this thesis hopes to achieve. Chapter

2 will be focused on providing a historical look at the how IRT has evolved over the

years. This is followed by the important principles that are involved such as the Stefan-

Boltzmann, Planck and Wien’s displacement laws.

Chapter 3 will review the current methods used for measuring CTE that have and

currently being used. Starting with the most general cases for isotropic material and then

shifting for anisotropic materials. The last two sections deal with the use of IRT as a

damage detection tool, first primarily in metals and composites followed by civil

engineering applications for infrastructure evaluation.

Chapter 4 the primary focus is using IRIC as an acceptable way to measure CTE value

of a specimen. After which it will be shown if smaller more localized regions can have

accurate CTE measurements given the full field data now obtained.

Chapter 5 intends to demonstrate the use of CTE as a damage indicator. First step to

calculate the CTE on a global scale, and comparing it to an undamaged specimen. The

next phase is trying to calculate local CTE values of regions to see if we can identify

damaged regions compared to its undamaged state. The use of IRIC will be shown as an

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effective way to identify damage, in doing so a link will be made that now that full field

data can be accurately maintained, the measurement of CTE value for a specimen that

deviates from accepted values can be understood. Full field data obtained using IRIC will

be used as method to detect damage will by using a Glass Fiber Reinforced Composite

(GFRC) specimen that will undergo a tensile test till failure. After this is shown a similar

sample will undergo a tensile load, but will not obtain surface damage, CTE will be

measured between this and an undamaged sample to compare measurement values. With

the thermal loading it will be shown why the CTE measurement is now different by

comparing

Figure 2: Generalized Thesis Layout

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Chapter 2: Infrared Thermography Any object at a temperature above absolute zero, -273oC, emits infrared energy in the

form of rays which fall into the infrared (IR) portion of the electromagnetic spectrum as

shown in the Figure 3 where wavelength and its associated frequency are noted. IR

portion of the infrared spectrum can be generally categorized into four sub-regions as

seen in Table 1 [8, 16].

Figure 3: Electromagnetic Spectrum

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Table 1: Infrared Sub-regions

Sub-Regions Wavelength (μm) Near Infrared

(NIR) 0.75-1.4

Short Wave Infrared (SWIR)

1.4-3

Middle Wave Infrared (MWIR)

3-8

Long Wave Infrared (LWIR)

8-15

Extreme Infrared (FIR)

15-1000

Thermal imaging is a technique for converting the thermal radiation pattern objects emit,

invisible to the human eye, into a visual image. An infrared camera is used to measure

and image the emitted IR radiation from an object which is dependent on the surface

temperature of an object. The radiation measured by the camera can also be affected by

an objects emissivity, which is further discussed in section 2.2, its absorption by the

atmosphere and further radiation such as the sun [17]. Infrared Thermography is a full

field non-contact and non-intrusive that enables visualization of this thermal energy. The

energy released by particles in oscillation produces thermal emissions. These oscillations

are caused by the temperature of the matter and the energy an object emits is proportional

to the surface temperature. IRT has many advantages as a NDT technique such as fast

inspection rate, generally non-contact, easy to interpret visual images and has unique

capabilities such as inspection of ceramic coatings which other NDT techniques are

rarely used. Chaki et al. [18] have studied interfacial defect detection in plasma-sprayed

ceramic coating components using two different IRT techniques. Artificial holes and real

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(disbanding) interfacial defects were examined, both techniques allowed ease in detection

and location of interfacial defects (1 mm depth) but less accurately the small defects (1

mm diameter). The difficulties with IRT include obtaining a quick uniform and highly

energetic thermal stimulation over a large surface, effects of thermal losses from

convection and radiation, cost of equipment and emissivity problems [19].

2.1 Historical Background The first temperature measurements occurred in 1593 by Galileo by designing the first

thermometer. A liquid filled glass bulb connected to a partially filled capillary tube,

where differential expansion of the liquid with respect to the glass bulb caused by an

increase in temperature would cause the liquid to rise in the capillary. William Herschel

in 1800 discovered the existence of infrared (IR) rays through a famous experiment.

Herschel was looking for an optical filter material to reduce the brightness of the sun’s

image in telescopes during solar observations. During this testing Herschel used a prism

to separate colors from blue to red, using a mercury thermometer he observed the

temperature was still elevated beyond the red band where no radiation was visible. This

discovery prompted what is now known in the electromagnetic spectrum as visible and

infrared rays. In 1829 the first thermocouple was invented. A thermocouple is a constant

sensor formed of two distinct metal junctions where one junction is set at a different

temperature with respect to the other producing a proportional difference in voltage. In

1833 the first thermopile was invented which connected multiple thermocouples and was

ultimately used to detect a person 10 meters away. In 1840 the first infrared image was

produced using an evaporograph, which was formed by the differential evaporation of a

thin film of oil. Between 1870 and 1920 advances in technology led to the first quantum

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detectors based on the interaction between radiation and matter. The detection process for

quantum detectors relied on the direct conversion of radiation into electrical signal as

opposed to the creation of an electrical signal due to heating of effect of radiation. By the

time of World War I infrared techniques were being applied for military use. From 1930-

1944 due to military research the first lead sulfide (PbS) detectors were created, sensitive

in the 1.3-3 μm. From 1940-1960 indium antimonide (InSB) extended the detection in the

spectral range to 3-5 μm. In 1960 mercury-tellurium-cadmium (HgTeCd) detectors

reached the long wave infrared range, 8-14 μm. With technological advances IRT has

become a more widely accepted and used technique that has branched into a variety of

different fields including NDE, maintenance, agriculture, medicine and thermo-fluid

dynamics as shown in Figure 4 [8, 20, 21]. In medicine infrared thermal imaging of the

skin has been used to monitor temperature distribution of the human skin. Abnormalities,

malignancies, inflammation and infection cause localized increases in temperature which

reveal themselves as hot spots [22]. Odlare et al. [23] discuss the use of near infrared

reflectance spectroscopy for the assessment of spatial soil variation in an agricultural

field. It is important to measure within-field spatial variation in soil when establishing

agricultural field trials in precision farming [23]. Infrared Thermography has been used

as an optical method to measure wall convective heat fluxes as well as the surface flow

field behavior over complex geometries [24]. Examples of IRT in NDE and maintenance

will be further explored in chapter 3.

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Figure 4: Advancement of Infrared Thermography

2.2 Principles of Infrared Thermography A blackbody is an instrument that absorbs the irradiated energy totally from any direction

and wavelength while also reemitting this energy until thermodynamic equilibrium is

reached with the surrounding environment. The radiation emitted from the blackbody is a

function of the temperature and wavelength. Commonly referred to as isotropically

diffuse no other surface can emit more energy than a blackbody at a given temperature.

One of the most important governing laws of thermal emissions is given by Planck’s law

in Equation 1. Planck’s law describes the distribution of emitted energy as a function of

the wavelength for a given temperature. The spectral radiance is the power irradiated by a

blackbody per unit surface per unit of solid angle. . In Equation 1 c1 is first radiation

constant, 1.1910439 x 10-16 Wm2, and c2 is the second radiation constant, 1.438769 x 104.

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2

2 1 11,

( 1)5b c

T

cL W m m sre λ

µλ

− −λ

−=

(Equation 1)

Wien’s displacement law states that the distribution of the wavelength of thermal

radiation from a blackbody at any temperature has essentially the same distribution at any

other temperature. The locus of the maximum spectral radiance for a given temperature

of a line can be obtained by deriving Planck’s law given in Equation 2 where the constant

2897.7 has units μm*K. The Stefan Boltzmann law states that the total energy radiated

per unit surface area of a blackbody across all wavelengths per unit time is directly

proportional to the fourth power of the temperature as given by Equation 3. The Stefan-

Boltzmann law is obtained by integrating Planck’s law for all wavelengths.

max2897.7

Tλ = (Equation 2)

4 8 2 45.670400*10bM T W m Kσ σ − −= = (Equation 3)

It is common to represent Planck’s law with a curve family graphically where for a given

temperature the emitted radiation can be shown varies with time. In Figure 2 Planck and

Wien’s displacement laws are shown graphically [8].

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Figure 5: Planck and Wien’s Displacement Laws Graphical Representation

Real surfaces do not behave like blackbodies and consequently they could absorb a part

of the incident flux, reflect one part and transmit another at different locations. The

absorbed, transmitted and reflected incident flux depends on wave length, orientation,

angle and temperature. Because materials do not behave in the ideal manner as

blackbodies the emission of these materials must be corrected by a factor known as

emissivity. Emissivity is a surface property that states the ability to emit energy and is the

ratio of the radiation emitted by the surface to the radiation emitted by the blackbody in

the same conditions. Emissivity is a unit-less quantity that falls within the range 0 to 1. In

general emissivity is not a constant and will depend on wavelength, temperature and

spatial orientation.

A few emissivity values of various materials can be found in Table 2. Because most

objects do behave like blackbodies the spectral radiance given by an object can be given

by equation 4 [19] and the Stefan-Boltzmann equation can be given by equation 5 [17].

2

2 1 1

( 1)5

2hcKT

hcL W m m sre

λλ

ε µλ

− −

−= Equation 4)

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4M Tεσ= (Equation 5)

Table 2: Emissivity Value for Metallic and Non-Metallic Materials [8] Material Temperature (°C) Emissivity

Metallic Materials Aluminum

Polished 50-100 0.04-0.06 Anodized 100 0.55

Steel Polished 100 0.07 Oxidized 200-600 0.80

Non-Metallic Materials Asphalt ---- 0.96 Concrete 20 0.92

Carbon Graphite 20 0.98 Fused Silica 20 0.93

2.3. Infrared Camera Background The foundation of thermography today is the use of IR camera and it is important to

understand the basic underlying principles. Up until the 1950’s the first thermal imaging

cameras were large and extremely expensive because the detectors used needed to be

filled with liquid nitrogen, known as cooled detectors. In order to make more compact,

portable and cheaper cameras a new class of thermal cameras needed to created, known

as Uncooled Detectors (UD). Today two basic UD types have emerged, ferreoelectric and

microbolometer. Ferroelectric uses ferroelectric transition in certain dielectric materials

near this phase transition the electric polarization of the dielectric is a strong function of

temperature. These small fluctuations of temperature in the material cause large changes

in electrical polarization. Microbolometer is a specific type of resister used as a detector.

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It is a tiny vanadium oxide (VOx) or Amorphous Silicon (a-SI) resister with a large

temperature coefficient on a silicon element with large surface area, low heat capacity

and good thermal isolation. Infrared radiation from a specific range of wavelengths

strikes the resistor and changes its electrical resistance. Changes in scene temperature

cause changes in the bolometer temperature which are converted to electrical signals used

to produce the image. Furthermore VOx microbolometers are being the more widely used

detectors as ferroelectric detectors have started to slowly fade out. Infrared detectors are

given a Noise Equivalent Temperature Difference (NETD) value. This value is the

amount of radiation required to produce an output signal equal to the detectors own noise

which means it provides the minimum detectable temperature difference. In order to

compare NETD values of different cameras they should have the same f-number. The f-

number is the focal length divided by the aperture diameter. The choice of detector type

will also specify the spectral range it operates in. The useful portion of the infrared

spectrum ranges from 0.8 to 20 μm, the selection of operating wavelength will depend on

the application because the atmosphere does not have perfectly flat transmission

properties. As can be seen from Planck’s law higher temperature bodies emit more in

short wavelengths. Radiation emitted from objects at near room temperature peak in the

long wavelength range, which also is less affected by outdoor operations where signals

can be negatively impacted by weather such as rain or fog. Generally IR ranges of 3 to 5

and 8 to 12 μm are the most commonly used bands because they match the atmospheric

transmission bands. Detailed studies have shown that from temperature range -10 to 130

°C measurements can performed without much difference between the 3-5 and 8-12 μm

bands [8, 16, 25]

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2.4 Passive Thermography and Active Thermography

The term Passive Thermography (PT) is used to describe the use of thermography with

no external heating sources. In this case, the thermal energy captured by an IR camera is

that of just the material in its surrounding environment. Any temperature anomalies from

the surrounding environment is typically a region of interest, since it is a parameter used

to compare images of an object to an original reference and therefore by measuring the

ensuing key region of interest, one parameter of using a passive method is comparing

images of an object to an original reference and measuring the ensuing ΔT (typically,

greater 5o C) areas of interests that are possibly related to e.g. damage are selected. [8].

The strength of passive thermography is the fact that no external heat sources are

required; there is no interaction with the tested and there is no physical contact. The

drawback when applying this technique is related to the need to have a strong, naturally,

occurring thermal contrast to be capable of identifying Regions of Interest (ROI), which

often make this modality of IRT qualitative and not a quantitative tool. As a result of the

naturally occurring thermal contrast necessary PT has found as a useful technique

studying infrastructure such as energy efficiency in buildings, studying air-seas gas

transfer and micro turbulence at the ocean surface and detecting splits and tears in

automotive stampings in real time [26-28]. Figure 6 shows the corner of building where

air leakage is occurring and a typical setup for PT can be seen in Figure 7.

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Figure 6: Air Leakage in Building Detected using PT

Figure 7: Typical Passive Thermography Setup

Active thermography (AT) employs the use of an external heating source to obtain

noticeable temperature differences that would allow for detection of subsurface

anomalies. A variety of different AT techniques have been developed but all primarily

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function with the same basic concept, external thermal loading is used by some heat

source, which will cause homogenous diffusion on the tested specimen’s surface except

where subsurface anomalies exist [8]. A typical arrangement AT set up can be seen in

Figure 8 below. Table 3 shows the advantages and disadvantages of the popular AT

techniques used in damage detection.

Figure 8: Common setup for an Active Thermography Setup

Pulsed Thermography (PLT) is using a short thermal pulse lasting anywhere from a few

milliseconds, for high conductivity material such as metals, to a few seconds for low

conductivity materials. Uniform temperature decay is should be observed for a sample

with no defects, regions with anomalies though will produce localized high temperature

regions that appear on the sample surface above the defect, this effect is produced by an

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insulation effect caused by the defect. The location, shape and size of the defect can be

estimated from the temperature distribution. For materials with high conductivity the

localized regions produced by defects will disappear quickly, while ceramics and steel,

materials with lower conductivity it will be easier to capture localized contrast regions

because the change in temperature will last longer [8, 29]. In PT a relationship exists

between thermal propagation time, t, and the depth of the subsurface z, given by Equation

6 where α here is the thermal diffusivity (m2 s-1) [30].

2ztα

(Equation 6)

Lock-in thermography (LT) uses thermal waves generated inside a specimen and detected

remotely. The wave generation is performed by periodic deposition of heat on a

specimen. The key to this technique is monitoring the exact time dependence between the

output signal and the input signal. Also referred to as ‘singular method’ for crack

identification, in this method cracks are identified from the singular electro-thermal field

generated around crack tips and concentrated temperature rise is observed in the vicinity

of the crack tips. When a periodically modulated electric current is applied to the

cracked sample, the intensity of the singular current field oscillates in the same

frequency. Causing cyclic changes of the singular electro-thermal field, the lock-in

measurement using the reference signal of the modulated electric current can be made for

the cyclically changing temperature distribution. Figure 9 below shows an illustration of

a LT setup typically used. Meloa et al. in 2006 [31] used LT to evaluate damage such as

delamination, impact and fatigue failure in various aerospace materials such as

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composites, hybrid composites, metals and sandwiches. They concluded that LT could

be used for the detection of defects size position and nature, the extension of impact

damage, evaluation of honeycomb conditions, the behavior of metal fibers and epoxy

layers in hybrid composites. Other research performed with this technique including

studying industrial techniques, plastered mosaics leakage sites in electrical circuits and

solar cells [32-35].

Figure 9: Setup for Lock-in Thermography [29]

Step heating (SH) employs a similar configuration as PT except uses a long pulse of low

intensity heat stimulation. Pulse thermography records the temperature decay of the

surface of a material, while step heating records the temperature rise of the surface of a

specimen. PT is a more commonly used approach but it may not be suitable for all

materials such as thick composite materials. Composites generally have low conductivity

which means a higher intensity must be used which could cause damage to the specimen.

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LT may provide better temperature contrasts but requires longer acquisition times and

more sophisticated equipment for measurement recordings and heat stimulation when

compared to SH [29, 36]. Marchnetti et al. [37] have used SH to detect sub surface

defects in road materials. The heating phase lasted for as short as 60 seconds and

concluded that the heterogeneity of materials used in the pavements could be bypassed

and defects 1.3 cm below the surface can be discovered.

Pulsed Phase Thermography (PPT) is a technique that combines the advantages of PLT

and LT, set up can be seen in Figure 10. In PPT a specimen is pulse heated precisely like

PT and the mix of frequencies of the thermal waves induced into the specimen can be

separated by using a Fourier transformation of the temperature decay on a pixel by pixel

bases. This allows the computation of phase images similarly to LT. PPT is performed in

the transient mode while LT is performed in a stationary mode. This enables LT to

provide higher quality images due to the summation process involved in the computations

while PPT requires only one single pulsed experiment while LT may require a larger

number of tests using different modulated frequencies. Using PPT the depth of the defect,

z, can be given by Equation 6 where α is the thermal diffusivity, C1 is a regression

constant and fb is the blind frequency, the frequency used to first detect a crack. [19, 30,

38-40]. Figure 11 shows a comparison of an image obtained from PLT and from PPT.

Both images show two localized hot spots, but the image from PLT has more undefined

shape due to the diffusion of heat, this aspect can be minimized in PPT [30].

1b

z Cfαπ

= (Equation 7)

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Figure 10: Pulsed Phase Thermography Setup

Figure 11: A) Thermal Image Obtained From Pulse Thermography B) Image Obtained from Pulsed Phase Thermography

Vibro-thermography (VT) uses the primary principles of lock-in thermography with the

addition of mechanical heat excitation. Stresses are generated by vibrations; the

mechanical energy is converted to thermal energy due to acoustical damping. Regions

with stronger damping and stress concentrations will produce higher temperatures and

correlate with the locations of defects like cracks and delamination. Polymers provide a

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good thermal signal even at low stress levels because of strong acoustical damping; have

a high emissivity for accurate IR camera readings and low conductivity which allows the

heat generated by defects to be maintained in localized defect regions [41-43]. A VT

setup can be seen below in Figure 12.

Figure 12: Vibro-thermography Setup [41]

Thermo-sonic imaging (TSI) also known as Sonic IR (SIR), uses the principles vibro-

thermography but rather than using mechanical vibrations, this technique uses a pulse of

low frequency ultrasound to cause the defect interface to clap or rub. This results in hot

spots that can be captured by IR camera depending on the diffusion of the heat from the

defect zone. This technique was created by Favro et al [44] at Wayne State University in

2001 and have successfully shown defects small as 20 μm have been detected in metal

samples but require IR camera speeds up to 500 Hz while using a short pulse, lasting

from 50-200 ms, of high frequency, 20-40 kHz. A setup up TSR can be seen in Figure

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13, this technique has been shown to remarkable for detection of small vertical cracks

such as fatigue cracks in metals and interplay delamination impact damage in graphite

fiber-0reinforced polymer composite. The depth and size of the crack can be interpreted

post mortem analysis of the transient temperature distribution. SIR has shown to have

better sensitivity speed and convenience compared to conventional NDT such as x-ray,

ultrasonic, dye penetrant and magnetic particle techniques. In Britain this technique has

been used for damage detection for carbon fiber reinforced plastic (CFRP) composites, in

Germany research has focused on modulated thermo-sonic imaging and in China it is

used to help detect cracks in aluminum alloys correlated with studies in numerical

simulation of the temperature field of cracks [45-48].

Figure 13: Setup of Thermo-sonic Imaging

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Table 3: Comparison of Active IRT Techniques

Active Technique Advantages Disadvantages

Pulse Thermography

• Quick thermal wave

• Requires external thermal excitation

Step Heating • Measures

Temperature Rise instead of decay

• Risk of overheating specimen • Does not provide the thermal

contrast of Lock-n Thermography

Lock-in Thermography

• Large Surfaces can be inspected

• Longer observation time • Depth of defect relates to

unknown modulated frequency • Complex instrumentation

Vibro-thermography

• Closed cracks revealed

• Difficult to generate mechanical loading

• Physical contact necessary • Complex instrumentation

Thermo-sonic Imaging

• Micron level crack measurements have been detected

• Understanding of ultrasonic waves

• Relatively new technique • Need very high speed IR

cameras

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Chapter 3: Infrared Thermography in Non-Destructive Evaluation

3.1 Coefficient of Thermal Expansion The coefficient of thermal expansion (CTE, α) is material property that indicates the

extent to which a material expands upon heating or contraction when cooled. It is

defined as the fractional increase in length per unit temperature. Over small temperature

ranges the thermal expansion of an isotropic material is linear due to temperature

changes. CTE is a temperature dependent property and for most metals and alloys will

gradually increase with increase in temperature, whereas instances of phase change will

cause discontinuities. Materials are often in contact with materials with different

properties, and therefore knowing the CTE of different materials can help avoid adverse

effects on product design. Large differences in the CTE can lead to motors binding,

solder joints failing, composites spitting on bond lines or internal stress build up [49].

Generally for metals and alloys CTE values will fall between 10x10-6 to 30 x10-6

μm/m*K while for ceramics will have lower values ranging from 1 x 10-6 to 2 x 10-6

μm/m*k. CTE is calculated by measuring two quantities, change in temperature and

length and can be categorized in two different ways, over a temperature range and at a

single temperature as shown by Equations 8 and 9 respectively. In equation 8, a material

is at an initial value length L0 at an initial temperature T0 when expands to L1 at T1 and L2

at T2. In this case αm is related to the slope of the chord between two points on the curve

length versus temperature. Calculating CTE at single temperature is referred to as the

‘true’ coefficient of linear thermal expansion and is also known as thermal expansivity. It

is related to the derivative of the dL/dT at a single temperature. The difference between

these two methods can be seen in Figure 14. Additionally CTE can be calculated

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similarly by replacing the change in length with change in volume as shown in equations

9 and 10. This volumetric CTE is used often for liquids and assumes constant pressure.

For isotropic materials the true volumetric CTE is equal to 3 times the true linear CTE.

1 1

m to

L dL dL T T L dT dT

ε εα α∆= = = =

∆ ∆ (Equation 8 and 9)

1 1 ( )m t P

o

V VL T V T

β β∆ ∂= =

∆ ∂ (Equation 9 and 10)

Figure 14: Length vs. Temperature of sample material [50]

A wide variety of techniques have been developed to calculate the CTE of isotropic

materials that include dilatometry, optical, interferometry, thermo-mechanical analysis

[51] and x-ray diffraction [52] to name a few. Mechanical dilatometry techniques are one

of the most widely used techniques; a specimen is heated in a furnace and the

displacement of the ends of the specimen are transmitted to a sensor by push rods. The

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push rods used can be vitreous silica, high purity alumina or isotropic graphite as seen in

Figure 15. This technique is applicable to materials material with CTE above 5 x 10-6/K

over a temperature range -180 to 900 °C. Using an alumina system can extend this

temperature range further up to 1600 °C while graphite can reach up to 2500 °C. Using a

sufficiently long furnace to place the specimen, a heating rate of less than 3 °C min-1 is

used. At high temperatures dilatometers have to employ pyrometers to measure

temperature and proper calibration techniques are needed. The contact area between the

push rods and the specimen must either be flat or rounded to a large radius to minimize

errors.

Figure 15: Mechanical Dilatometry using double push setup [50]

One of the simplest optical techniques is to use targets placed on the specimen to measure

their displacement, this is known as optical comparator. A specimen being heated in a

conventional oven must have a suitable viewing port to constantly monitor expansion.

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The targets that have been used are indentations grooves, wires or pins attached to the

specimen or holes machined into the specimen. The displacement of these targets can be

measured using two telemicroscopes with high magnification, use photodiodes or CCD

array onto which an image of the targets is projected as to automate the procedure. The

optimal results are obtained when the regions between two targets is at uniform

temperature. This technique does not use a reference material and is considered an

absolute CTE measurement and the accuracy of displacement has been estimated to be at

plus or minus 2μm. Interferometry techniques use optical interference and the

displacements of the ends are measured in terms of the number of wavelengths of

monochromatic light. A specimen is placed between two optical flats which move apart

as the expansion occurs. Monochromatic light is reflected from the bottom surface of the

upper flat, which is transparent, and the top surface of the lower flat. These two rays

interfere with each other destructively or constructively depending on the separation as

shown in Figure 16 where A and B are the optical flats, S is the specimen and x is the

separation. Interferometry techniques are more accurate than dilatometry but employs

complex and expensive instrumentation.

Figure 16: Typical Arrangement for Optical Interferometers [50]

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Thermo-mechanical Analysis measurements are used by using a thermo-mechanical

analyzer (TMA) that consists of a specimen holder and probe that transmits the changes

in length to a transducer which is converted into an electrical signal. A temperature

control system of a furnace, heat sink and temperature measuring device such as a

thermocouple surround the sample [49]. The specimen is heated uniformly in a furnace

and is applicable in the range -120 to 600 °C. ASTM Test Method E831 describes the

standard testing procedure using thermo-mechanical analysis.

Figure 17: Thermo-mechanical Analyzer setup [49]

Wang et al [53] measured CTE of polymeric materials by combining infrared

thermography couple with thermo-elastic stress effects. This method is capable to be

applied to any material regardless of shape, size or crystalline structure. Thermo-elastic

stress analysis (TSA) is the relationship between small temperature changes, T by the

change in the stress state of a linear elastic homogenous material and the strain in the

solid can be derived [54].

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, 1, 2,3ijij

T QT for i jC T Cε ε

σε

ρ ρ∂

∆ = + =∂∑ (Equation 11)

Where Q is the heat input, Cε is the specific heat at constant strain, σ is the stress change

tensor, ε the strain change tensor and ρ is density. The heat input is generally neglected

because the specimen is loaded at a rate that no heat conduction occurs. The following

stress-strain temperature relationship is used for an isotropic material under plane stress

condition:

11 1 22

22 2 12

12 12

( )(1 ) (1 )

( )(1 ) (1 )

(1 )

E E T

E E T

E

ασ ε νεν ν

ασ ε νεν ν

σ εν

= + − ∆− −

= + − ∆− −

=+

(Equation 12)

Where E is Young’s modulus, ν is Poisson’s ratio and α is the CTE. By assuming that E

and ν are independent of temperature Equation 11 can be written as Equation 12 and

using standard Hooke’s law relationship equation and a relationship between specific

heat at constant strain and specific heat at constant pressure Equation 13 [54]. Equation

13 was used as the governing equation to calculate the CTE of polymeric materials

during a tensile test performed at a constant extension rate, Figure 7 shows the obtained

results, CTE values calculated by solving for α using equation 13 matched well with

results obtained by TMA [53].

1,2(1 ) ii

i

T ETCε

α ερ ν =

∆ =− ∑ (Equation 12)

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1,2

iiip

T TCα σρ =

∆ = − ∑ (Equation 13)

Figure 18: Temperature Change caused Be Applied Uniaxial Tensile Stress [53]

Recently a new method proposed by Montanini and Freni [55] use Infrared image

correlation (IIC) for a quick assessment of CTE of solid materials. This proposed

technique is a full field non-contact measurement that uses digital image correlation

(DIC) to measure the displacement fields and an infrared camera to get accurate thermal

measurements as shown in Figure 19.

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Figure 19: Experimental Setup for Proposed IRIC Method a) Heating Chamber b) Measurement Setup [55]

IR readings benefit greatly from using high emissivity black paint (ε ≈0.95) while DIC

requires a speckle pattern to produce light contrast to measure displacement fields. In

order to satisfy both of these conditions a fine grained gypsum powder was applied to the

surface of the material to create a high emissivity speckle pattern ε ≈0.96. A small region

on the surface of the specimen was left with only the black paint where the IR camera

would be focused in to capture temperature measurements that would be considered

representative for the whole material as depicted in Figure 20.

Figure 20: a) Close up of Speckle Patter b) specimen used c) Close up of speckle and non-speckle region [55]

The authors used samples, an aluminum allow and a glass fiber reinforced plastic (GFRP)

and measured their obtained values with a commercial pushrod dilatometer. As expected

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the aluminum alloy showed good isotropic correlation when calculating CTE in the x and

y directions while the GFRP should anisotropic behavior in x and y. The values obtained

by using the prosed IIC method shows good correlation with dilatometer results, shown

in Figure 21.

Figure 21: A) Strain vs. ΔT for Aluminum Alloy B) Strain vs. ΔT for GFRP C) Measured Values of

CTE for AL Alloy and GFRP

3.2 Coefficient of Thermal Expansion of Anisotropic Materials Composites are complex materials due to unknown features such as chemical

compatibility, wettability, absorption characteristics and development of complex stress

states resulting from difference in thermal and moisture expansions. These factors help

limit the complete characterization and understanding the matrix and fiber materials are

necessary for being capable to successfully predict the properties of composites [56].

Anisotropic materials will not undergo a uniform thermal strain if changes in temperature

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occur. This occurs because of the mismatch in thermal behavior of fibers and matrix. The

CTE for anisotropic material can be described by Equation 14.

yy xyxxxx yy xyT T T

ε εεα α α= = =∆ ∆ ∆

(Equation 14)

The CTE can be expressed using coordinate transformation equations such as using

principal CTE α1 α2 if known. The CTE at a degree 45° with respect to the principal

directions can be given by Equation 15. To take into the account the temperature

dependence, CTE can be written as Equation 16 [57]. These equations allow composite

materials to have their CTE calculated in a similar manner to isotropic materials

mentioned in the section 3.2, such as by electronic speckle pattern interferometry [57,

58], dilatometer [55] and even strain gages [59].

1 245 2

α αα += (Equation 15)

( )( ) d TTdTεα = (Equation 16)

The CTE of the constituent phases of composites determine how much thermally induced

stresses are generated. Stiffness mismatch between the constituent phases can potentially

result in mechanically induced stresses during loading which can adverse effects such as

interface de-bonding, cracking and elastic/plastic deformation of the constituent phases

[60]. Dong [61] created a model to predict the transverse CTE of unidirectional carbon

fiber reinforced composites (CFR). Finite element analysis (FEA) was originally used,

using a representative unit cell and were compared to the micromechanical models that

have already been established and can be viewed in Figure 22. Micromechanics study the

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mechanical properties of composites in terms of their constituent materials. The simplest

equation is the law of mixtures, given by Equation 17, that become progressively more

complex as shown in Figure 22. In Equation 17 αc is the CTE of the composite, αf is the

CTE of the fiber, αm the CTE of the matrix, νf the volume fraction of the fiber, νm the

volume fraction of the matrix [56] . While the Hashin’s model provided the most

accurate results, the calculating process was complicated and was not practical for

applications. A regression based model was developed and validated by FEA and

experimental data. This newly developed model has shown to be a simpler and more

accurate method for predicting the transverse CTE of composites.

c f f m mα α ν α ν= + (Equation 17)

Figure 22: Micromechanical Models for transverse CTE of composites

Vaidya and Chawla [62] looked into calculating the CTE of metal matrix composites.

The thermal expansion behavior of a metal matrix composites are very complex, since the

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metal matrix can deform plastically due to the thermal stresses that are generated.

Thermal strains which increase with temperature increase the plastic deformation at

elevated temperatures. The number of thermal cycles also affects the CTE of the

particulate and fibrous composites because of strain hardening of the matrix in the first

cycles and plastic deformation in subsequent cycles become much harder. It is important

to understand the plastic deformation in the matrix because it will affect the properties of

the composite. Huang et al [63] more recently were able to extract properties of metal

matrix composites by studying the instantaneous CTE curves. By observing the

instantaneous CTE curve plotted against temperature information about precipitating and

matrix plastic deformation was obtained.

3.3 Damage Detection in Materials Non-destructive evaluation has been applied to evaluate infrastructure and measure

material and damage properties. This section will look at a few different research topics

that have been employed with IRT which can be broken down into two broad categories,

specimen and infrastructure and further broken down into various topics as shown in

Figure 23.

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Figure 23: Uses of IR in NDE

Cuadra et al. [9] employed a novel hybrid NDT approach using acoustic emission (AE)

digital image correlation (DIC) and Infrared (IR) were combined for damage

quantification in polymer composites. The IRT portion of this work was capable of

showing temperature differential maps that reveal the appearance of hot spots. These hot

spots correlate well with prescribed strain and load increments and distinct changes in

recorded acoustic activity. Figure 24 shows the data collected from applying a tensile test

on a GFRP epoxy. The developments of hot spots that appear from IR data help identify

damage occurring before DIC. This hot begins to shift showing clear areas of damage

occurring.

Non-Destructive Evaluation in IRT

Specimen

Metals/Alloys

Composites

Ceramics

Polymers

Infrastructure

Bridges

Concrete/Masonry Structures

Power lines

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Figure 24: (a) Measured tensile stress vs strain curve correlated with the distrabution of of extrated amplitude of AE events and temperature differential values (b) full field strain maps

(top) and temperature differential maps (bottom) (c) The raw images of specimen [9]

IRT techniques have already been used as a means of defect quantification in composite

and ceramic materials it has not been attempted as frequently for metals such as steel.

Sharath et al. [64] have recently used PLT for defect depth estimation in type 316 L

austenitic stainless steels. Steels have good high temperature mechanical properties and

good corrosion resistance which make them well suited for nuclear industry among

others. The authors use 3 empirical methods based on PT, temperature contrast method,

contrast derivative method and log second derivative method for the defect

characterization. Temperature contrast method (TCM) refers to the difference in

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temperature between the defective region and an adjacent good or non-defective region as

shown in Figure 25 and is given by a simple formula as shown in Equation 18.

Figure 25: Temperature Contrast Method [64]

abs def NDTC T T= − (Equation 18)

In equation 18 Tdef refers to the temperature of the defective area and TND refers to the

temperature of a non-defective region. Better visibility of the defect occurs with higher

values of TCabs and is directly related to the depth of the defect. The deeper the defect the

lower the value of TCabs would be. The Contrast Derivative Method (CDM) takes the first

derivative of the temperature contrast and use the peak time determined from the plot of

Tdef and TND to calculate the depth of the defect given by equation 19 where x is the

defect depth, ts is the peak time and in this case α is the thermal diffusivity.

2

2

3.64s

xtπ α

= (Equation 19)

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The log second derivative method uses the relationship that time and temperatures have a

linear relationship in the logarithmic domain with a slope of -0.5. In a similar manner to

the CDM a peak time equation is used to calculate defect depth.

2

2xtπα

= (Equation 20)

The authors use thermal signal reconstruction to reduce the noise associated with PLT

and the IR camera by taking the temperature response at each pixel taken in a log scale. It

is fitted with a polynomial of a higher order and reconstructed by equation 21 where N is

the order of the polynomial.

]

0( ) exp[ (ln( )) ]

Nn

nn

T t a t=

= ∑ (Equation 21)

The stainless steel used has an emissivity ~0.7 and is compensated for by adding high

emissivity black paint to improve IR accuracy. Figure 26 shows a schematic of the

sample with defect regions.

Figure 26: Stainless Steel Sample with Embedded defects [64]

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The authors conclude that their technique shows great promise. From the results they

obtained the contrast derivative method and the logarithmic second derivative method

have a better accuracy in determining the depth of the defect. Figure 27 shows thermal

images obtained and Table 4 shows a sample of the results obtained compared to actual

depth defect.

Figure 27: Thermal images taken after different time intervals [64]

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Table 4: Defect Depth estimate by 3 PT methods [64]

Polymeric Fibers are often stretched in their final stage of processing to improve their

physical properties. During this process fibers stretch homogenously with increase in

tensile load and suddenly become thinner, known as necking, just after the yield point.

Deformation that produces neck propagation is referred to as cold-drawing. At high

extension rates a noticeable temperature rise occurs in the necking region which makes

understanding temperature rise during the drawing of the polymer important to

understand the deformation mechanism. During the necking phenomenon temperature

rises can reach up to 50 degrees °C, Figure 28 shows the change in temperature of the

necking region as long and elongation increase. Correlating the resolution of the camera

and using a matlab script, Wang et al. [65] show a rough estimate can be made of the

diameter of the necking region using the thermal images.

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Figure 28: Curve of Load-elongation with Thermal Images of the Drawing Fiber [65]

3.4 Application of IRT in Infrastructure Evaluation IRT has been used for a variety of concrete and masonry structures to detect sub surface

damage, near surface honeycombing and delamination. Alternatively to just damage

detection IRT has been used to assess thermal insulation in structures as seen in Figure

29. Many non-destructive testing (NDT) techniques including radar, ultrasonic, impact

echo, acoustic emission (AE) and digital image correlation (DIC) have already been used

and can be combined with IRT to give richer information.

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Figure 29: Infrared Thermography can identify regions of significant heat loss [17]

One investigation into concrete bridges performed by Clark et.al [17] studied detecting

delamination using IRT. The authors used ASTM D4788 which describes methodology

to obtain suitable information. A vehicle mounted with an IR scanner drives through the

center of each lane of a bridge, delamination will appear as hotter or cooler spots

depending on the day. During night the delamination will appear as dark or cooler areas

on a warmer background while during the daytime delamination will appear as a warmer

color. It is important to use a conventional video recorder as a way to map the bridge and

separate patches or surface defects that may not determine delamination. This testing

procedure is designed to determine specific areas of delamination that requires repair.

The delamination may indicate the lack of bond between the overlay and the underlying

bridge deck. Figure 30 below show the data obtained by using IR camera.

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Figure 30: A) Initial IR Image B) Iinitial IR Image with Lower Temperature Range C) Green areas

are Delmaination D) Close up of Delamination Area [17]

Delaminated areas were already confirmed with the use of ‘coin-tap’. Using IRT two

previously areas of delamination were detected and all but one of the known

delamination areas were detected except for one that was effected by sunlight near the

edges. The difference in temperature of non-delaminated regions with delaminated region

was about approximately 0.2-0.3 °C.

Maeirhofer et al. [66] have conducted research into using impulse thermography as a tool

to assess concrete structures. One of the experiments involved heating of a concrete block

size 1.5 x 1.5 x 0.5 m3 with 8 voids built in using polystyrene cuboids as shown in Figure

31 along with the use of traditional radar to estimate defect depth. Transient curves,

surface temperature as a function of time, were analyzed in the frequency domain based

on Pulsed Phase Thermography (PPT). Pulsed phase thermography is actually a

combination two IRT techniques, using the pulsed acquisition procedure of PT with the

phase and frequency concepts from LT [67]. A systematic series of tests were performed

with heating times varying from 5 to 60 minutes. Figure 32 shows the results from the

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thermal images obtained and phase images after 30 minutes of heating, in the thermal

images all 8 voids can be seen, but the smaller ones begin to fade over time while the

deeper voids actually increase their contrast. In the Phase images 0.145 mHz allows all 8

voids to be seen and increasing the frequency actually begins to cover some of them.

Figure 31: Shows Void Placement in Concrete along with Depth and Estimate by Radar [66]

Figure 32: (Top) Thermal Images of Concrete Structure after Heating (Bottom) Void detection

at different frequencies [66]

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Chapter 4: Validation of Using IRIC To Measure CTE The first goal of this thesis is to use a hybrid NDT technique using IRT and DIC (IRIC)

to measure CTE, a material thermal property. Figure 33 shows a schematic of the

proposed setup using an IR camera mounted directly between two DIC cameras.

Preliminary work has already been done using this technique to measure CTE, but does

not provide full field measurements and similarly to other techniques uses a thermal

chamber as its heating device [55]. The novelty of this thesis is to provide full field

measurements and using alternative heating sources to provide acceptable CTE values.

By accurately measuring full field strain and temperature profiles, it should then be

possible to measure accurately the thermal expansion on smaller localized regions to

extract information about the specimen. In addition accurate measurements of CTE

values for different materials will allow separation of the thermal strains that form from

thermo-elastic stress effects from total strain accumulation of a given material.

Figure 33: IRIC Setup

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4.1 Experimental setup of CTE Calculation An Aluminum 2024 T-3 (AL2024) specimen approximately 51 x 40 x 2 mm was used for

the first experiment. This specific type of aluminum is known for its wide use in

applications requiring high strength to weight ratio, it is heat treatable and has excellent

corrosion resistance with reported CTE values from literature. Table 5 [68, 69] shows

important material properties of AL2024. Some problems have been noted previously

about the use of IR and DIC which include the stochastic pattern needed for DIC and the

high emissivity uniform surface IR requires and space allocation for all the dedicated

equipment [70] these problems have been addressed in the setup for this experiment.

Table 5: Properties of Aluminum 2024 T-3

Modulus of Elasticity 73.1 GPa

Poisons Ratio 0.33

Ultimate Tensile Strength 483 MPa

Density 2.77- (g/m3)

Specific Heat (J kg-1 K-1) 0.875 (J/g °C)

CTE 23.2-24.7 μm/m∙C

A FLIR A325sc thermal camera with spectral range of 7.5-13 um, 320x240 resolution

and maximum 60 Hz frame rate acquisition as shown seen Figure 34 and where Table 6

gives more specifications of the camera [71]. During the test the IR camera recorded at 4

Hz and post mortem some data matching needed to be made. The IR camera was placed

directly in between the DIC camera lenses to efficiently use space and allow for the best

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possible viewing angle for both systems. The experimental setup can be seen in Figure 35

with the AL2024 specimen placed on the hot plate.

Figure 34: FLIR A325sc Camera

Table 6: FLIR A3254sc Specifications [71]

Detector Type Uncooled Microbolometer Spectral Range 7.5-13 μm

Resolution 320 x 240 Detector Pitch 25 μm

NETD <50 mK Time Constant < 12ms

Frame Rate 60 Hz Standard Temperature Range -20 °C to 120 °C or

0 °C to 300 °C F-Number f/1.3

A GOM ARAMIS 3D 5 megapixel DIC system with max image acquisition rate 30

frames/s (fps) was utilized to record full field deformation, using a 65 x55 mm (2400

x2100 pixels) field of view (FOV) during the test. 3D surface deformations were obtained

through triangulation of the two camera lenses. A stochastic speckle pattern is required to

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obtain an appropriate light contrast, to do so a layer of high emissivity black paint was

used to cover the specimen with a non-reflective white paint used to create a finely

grained pattern. This pattern not only provides a clear contrast to measure displacement

fields, it provides a high emissivity (e ≈ 0.95) and uniform coating for accurate

temperature readings from the IR camera.

Figure 35: Experimental setup Calculating CTE of AL2024

The temperature measurements were analyzed post mortem using the FLIR software

ExaminIR Pro 1.40.21. A Region of Interest (ROI) was placed around the specimen to

record the average transient temperature of the specimen during the heating process.

ARAMIS v6.3.0-7 software was used to measure the deformation of the specimen during

heating to calculate displacement fields and strain maps. Figure 36 shows both of strain

in the x and y directions along with a thermal image of the specimen heating. Equation 6

was used to calculate the CTE of AL2024 and the first step was to calculate strain.

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Aluminum 2024 is an isotropic material which suggests the strain in x and y should be

identical with difference occurring from noise. Noise can came from the specimen size,

speckle pattern or calibration process and because this may lead to statistical anomalies

the average strain in the x and y directions were used in calculations. The average strain

profiles in the x and y directions show a direct correlation to the transient temperature

profile of the specimen during heating as scene in Figure 37 Any discrepancy in the

average strain in x and y can be attributed to noise.

Figure 36: A) Strain Map in X B) Strain Map in Y C) Thermal Image with ROI around the

specimen

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Figure 37: A) Average Strain in X and Y of AL2024 B) Average Temperature of AL2024

The CTE of AL2024 was calculated when the specimen reached 30 degrees Celsius and

calculated at roughly every 10 degree interval until it reached approximately 120 degrees.

The temperature time intervals were noted and the corresponding strain values were

extracted and referenced with strain image of when 30 degrees was reached from the

data. Figure 38 shows the average strain in x and y plotted against the change in

temperature showing a strong linear behavior with similar values in each direction, as

expected for an isotropic material, while Table 7 shows the change in temperature, the

strain values in x and y and the CTE measured in x and y which show a strong correlation

with each other as expected but also match expected values from literature. The mean

value shown in x and y refer to the average value obtained from the 8 intervals to help

offset any statistical anomalies that happen during testing.

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Figure 38: Average Strain in X and Y plotted against Change in Temperature

Table 7: Experiment 1 Average Strain and CTE Values Obtained in X and Y

ΔT Average Strain X (um/m)

Average Strain Y (um/m)

Average CTE X

(um/m∙°C)

Average CTE Y (um/m∙°C)

0 0 0 - - 9.93 229.7 235.98 23.13 23.76 20.14 471.7 472.98 23.42 23.48 29.91 687.7 687.98 22.99 23.00 40.51 951.7 950.98 23.49 23.47 50.06 1179.7 1193.98 23.56 23.85

60 1459.7 1473.98 24.32 24.56 70 1739.7 1663.98 24.85 23.77 80 19999.7 2003.98 24.99 25.05

Mean CTE X: 23.8476

Mean CTE Y: 23. 8705

0 10 20 30 40 50 60 70 80 900

250

500

750

1000

1250

1500

1750

2000

2250 Hot Plate Heating

Aver

age

Stra

in (µ

m/m

)

∆ T (C)

AverageEx AverageEy

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The same sample was again measured to ensure repeatability in this technique,

Experiment 2. A lower temperature with a slower heating profile was used. The specimen

was heated only to 60 degrees Celsius over 10 minutes to potentially gain more accurate

DIC results. The temperature profile and strain profile in the x and y show strong

correlation once again as seen in Figure 39, The strain in the y-direction shows a lot more

fluctuation which could be the result of noise from calibration or surface abnormality but

generally shows good agreement with the strain in the x-direction as temperature

increases, seen in Figure 40. The measurements obtained can be seen in Table 8

Figure 39: A) Temperature Profile of AL2024 Sample B) Average Strain Profile in X and

Y Directions

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Figure 40: Experiment 2 Strain vs. Temperature

Table 8: Experiment 2 Strain and CTE Values ΔT Strain X

(um/m) Strain Y (um/m)

CTE in X (um/m∙°C)

CTE in Y (um/m∙°C)

0 0 0 -- --

6.06 142 165 23.43234 27.22772 9.16 225 196 24.56332 21.39738 12.8 302 309 23.59375 24.14063

16.1148 387 381 24.01519 23.6428 21.43 508 531 23.70509 24.7783

27 651 631 24.11111 23.3703 30 738 707 24.6 23.5666 Mean: 24.00297 Mean: 24.01768

In this section two different experiments were run, which used different heating rates and

temperature ranges. Regardless of the setup used, both experiments showed strong

correlation in their respective transient strain and temperature graphs and as expected

generally linear profiles measuring strain vs. change in temperature in the x and y

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directions. CTE value is generally an average value over a temperature range, multiple

values were measured which incurred very few irregularities. These irregularities can be

derived from the spike in strain that occurred near the end of experiment 1, Figure 37A

and the high fluctuations in strain measured in Y in experiment 2. Regardless of these

deviations generally, values of CTE measured in x and y fell within in the expected range

23.2-24.7 μm/m∙°C and the mean values for each experiment showed that even with

variation they showed good agreement with reported values.

4.2 Calculating Local Coefficient of Thermal Expansion Values The values obtained for strain, temperature and CTE from section 4.1experiment 1, will

be referred to as the global values. Furthermore, two local regions, as seen in Figure 36,

were designed by creating two equal halves, Region 1 (R1) and Region 2 (R2)

correspond to the top and bottom halves of the sample respectively, where temperature

profiles and strain profiles in x-y directions were measured. Knowing the specimen is

undamaged we expect to see similar values for each region. From Figure 42 A and B we

can see the similar behavior with temperature and strain profile matching the

corresponding global profiles from section 4.1 and additionally strong linear behavior

between x and y in R1 and R2 can be seen in Figure 37 C and D. All strain and CTE

values measured for R1 and R2 can be seen in Table 9, strange behavior near the end of

the strain profile in Y shows a strong fluctuation which may attribute to some minor

variations in the CTE values measured but the mean values obtained for the 8 increments

are very close to the global values obtained.

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Figure 41: Local CTE Regions

Figure 42 A) Local Strain Values XY Measured in AL2024 B) Temperature Profile for Region 1 and Region 2 C) Local Strain Y in Region 1 and 2 D) Local Strain Values in X vs. Change in

Temperature

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Table 9: Local Strain and CTE Values Measured in Region 1 and 2 ΔT (°C)

Strain X R1

(μm/m)

Strain X R2

(μm/m)

CTE X R1

(μm/m*°C)

CTE Y R2

(μm/m*°C)

Strain Y R1

(μm/m)

Strain Y R2

(μm/m)

CTE X R1

(μm/m*°C)

CTE Y R2

(μm/m*°C) 0 -- -- -- -- -- -- -- --

9.93 235 236 23.76636 23.66566 204 260 26.18328 20.54381 20.14 457 448 22.24429 22.69116 451 438 21.74777 22.39325 29.91 674 692 23.13607 22.53427 682 675 22.5677 22.80174 40.5 934 914 22.5679 23.06173 921 936 23.11111 22.74074

50.06 1174 1151 22.99241 23.45186 1176 1146 22.89253 23.49181 60 1435 1424 23.73333 23.91667 1434 1376 22.93333 23.9 70 1737 1724 24.62857 24.81429 1650 1660 23.71429 23.57143 80 1983 2027 25.3375 24.7875 2069 2114 26.425 25.8625

Mean CTE: 23.5508

23.61539

23.69688

23.16316

This experiment has shown the viability of using IRIC as technique to measure accurately

values of CTE for an isotropic material. A more precise methodology including a more

accurately cut specimen, accurate measurement of emissivity, calibration improvements

for DIC measurement and a slower heating method would enhance the measurement

values obtained. Emissivity of the paint was used as 0.95 for the specimen and while it

may not be exact value of the stochastic speckle pattern used, it is close enough where

small discrepancy in the emissivity value has negligible effect in calculations. The

primary goal was to show that IRIC is a feasible technique to measure CTE, beyond that

this was shown to be done without the use of a thermal chamber.

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Chapter 5: Application of IRIC as Damage Identification Chapter 4 demonstrated that with IRIC is a feasible tool to measure the CTE of materials.

It was then shown it is possible to separate the specimen into local regions and obtain

CTE values in each of them, which for an undamaged specimen are expected to be

comparable with the global value for the entire tested specimen. Chapter 5 proposes the

use of such local/global CTE measurements as a means to detect and quantify the extent

of damage. Previous chapters covered the use of IRT as a means for damage detection.

However the identified damage by using solely the IRT technique typically was not

correlated to loss of materials properties other than ad hoc comparison with overall

mechanical behavior changes. This chapter will test the hypothesis that local CTE

measurements made possible by the combined use of IRT and DIC can be used to

quantify material property degradation after progressive damage as occurred. To

accomplish this goal, the first step is to measure the CTE of the entire specimen, called

global CTE, and then measure local CTE values in regions containing defects such as

notches by partitioning the specimen into smaller sections. The comparison between local

and global CTE values could provide estimates of the severity of occurring damage.

5.1 Heating Damaged AL2024 Specimen Following the same procedure outlined in chapter 4, a Damaged Specimen (D1) was

heated using a hot plate. Specimen D1 had two holes drilled into the back surface, one

shallow and one almost half the specimen’s depth as seen in Figure 43. The specimen

was heated until it reached equilibrium at 30 °C before thermal and strain values were

measured.

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Figure 43: (Left) Undamaged Sample from Chapter 4 (Right) Damaged Sample

Similar stochastic pattern was applied to D1 to accomplish DIC measurements explained

in chapter 4. Average strain values were computed post mortem the average strain was

entire specimen, which was then partitioned into two regions into two regions, called top

and bottom. The next steps in this procedure was to measure global strains, as well as

around the partitioned areas where the top half corresponds to the damaged region and

the bottom half to the undamaged one as shown in Figure 44.

Figure 44: Damaged sample 1 with two partitions A) Thermal image B) Strain map of specimen

The heating of D1 can be considered an active technique, similar to SH, because the

temperature measured is increasing due to an external thermal excitation, consequence of

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heat conduction. During the heating process the manufactured defects actually become

visible at higher temperatures as seen in Figure 45. The CTE values in x-y were

calculated for the whole specimen and the two partitioned regions; the results can be seen

in Table 10.

Figure 45: (Top left) initial Heating Stage, (Top Right) Formation of defects (Bottom) Clearly visible Defects

A close look at the values obtained shows that in the undamaged region consistent values

of CTE in both x and y directions were in accordance to previously measures valued. In

Table 10: CTE Values of Damaged Specimen ΔT (°C)

CTE X Bottom

(μm/m*C)

CTE Y Bottom

(μm/m*C)

CTE X Top

(μm/m*C)

CTE Y Top

(μm/m*C)

CTE X Global

(μm/m*C)

CTE Y Global

(μm/m*C) 9.966 24.3829 24.08188 21.07164 8.42866 17.25868 21.07164 23.5286 23.80082 24.69335 22.14326 17.2556 20.95322 22.14326 35.9972 23.94631 24.86304 22.30729 20.0849 22.36285 22.30729 45.9385 24.22804 24.9464 25.20761 20.28799 22.48659 25.20761 54.0835 24.33274 23.6856 24.18483 19.91365 22.2064 24.18483 60.5885 24.65814 23.88242 23.68436 20.76302 23.09019 23.68436 68.989 24.23575 24.81555 23.22109 20.8584 22.74276 23.22109 74.1371 24.26585 23.15979 22.90351 23.18677 23.71282 22.90351 Mean: 24.23132 24.266 22.45848 18.84737 21.85169 23.09045

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the top region, a comparison between the CTE values obtained in the damaged zone and

entire specimen shows differences. This change in behavior in thermal expansion

indicates at least qualitatively that the specimen has been altered compared with its

undamaged state, as it had been characterized in Table 10. The CTE values measured

after the first procedure correspond to relatively low thermal loads which again make the

use for proof-of-concept arguments. Corresponding strain profile and temperature

profiles can be seen in figure 45. The Strain profiles obtained in the top region show

higher discrepancy compared to the lower region. Figure 46 shows the strain in the y

direction of D1, the shallower hole appears to be well mapped and shows strain values in

tension and compression around it. The noise level obtained in this measurement was 50

microns. As the thermal load increases CTE values measured globally and in the

damaged regions tend to be lower than the bottom region until about 70 degree increase

in temperature.

Figure 46: A) Strain vs. Time of Top and Bottom Regions B) Temperature Profile of Specimen

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Figure 47: Average Strain in Y during Thermal Loading of D1

This damaged sample was further divided into 16 more localized regions. Each

subsection a measurement for the CTE was calculated and plotted. Figure 48, shows how

the specimen was divided into 16 sections while the plot shows how in the top 8 sections

where damage has occurred shows the greater number of statistical outliers compared to

the bottom 8 sections.

Figure 48: Local regions and corresponding CTE values

1

16

9 10 11 12

151413

5 6 7 8

2 3 4

0 2 4 6 8 10 12 14 16 1814

16

18

20

22

24

26

CTE

Valu

e (µ

m/m

)

Section Number

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The defects placed were clearly visible during thermal loading, consequently the larger

depth hole actually created a slight surface defect on the opposite side. This specimen

was heated to see if the IR camera can be used to detect this surface deformation. The

back side of D1 was coated with high emissivity black paint, to obtain accurate

temperature values, and heated to up to 90 degrees Celsius. In Figure 48A we can see a

possible surface anomaly, which is circled. This spot corresponds to the area where a hole

was drilled in from the other side, but to confirm it is a surface defect and not a visual

anomaly that can occur from noise or the paint applied, temperature gradients of the field

were taken.

Figure 49: A) D1 Temperature Field B) D1 Temperature Gradient X Direction C) Temperature Gradient Y

Figure 48B and C show the temperature gradients in the x and y directions respectively

and show anomalous behavior in the same spot as that the temperature field presented

some hot spots. The temperature gradients were calculated by using an in house code

created in Matlab, can be seen in Appendix A. Figure 48A shows temperature values in

Celsius, Figures 48B and C shows units’ degrees Celsius per millimeter. Using

temperature gradients in this manner showed the potential to detect and track the

progression of damage in a material.

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5.2 Damage Progression in GFR Composite The experiments previously described validated the use of IRIC as a feasible technique to

measure CTE values with sufficient accuracy. In addition, the CTE as well as the

combined thermal and deformation field measurements in a partitioned specimen were

shown to be good indicators of damage. The next to step in the presented analysis in this

thesis was to evaluate the capabilities of the IRIC method in detecting subsurface

damage. To this aim a Glass Fiber Reinforced (GFR) epoxy laminate was used in tensile

tests. A rectangular specimen of approximately 90 mm x 25 mm x 3 mm was used. Tabs

were placed on the specimen ends using a strong adhesive to assist the specimen grip

during testing as shown in Figure 49. Two tests were performed; the first test was used to

track damage progression using the IRIC method until failure. The goal of this section is

to compare the progression of damage identified by strain fields and temperature

gradients. The bulk properties of GFR can be seen in Table 11. The second goal of this

section is to provide a CTE measurement of the GFR of a damaged and undamaged

specimen using IRIC to identify the change in thermal properties induced by damage.

Table 11: GFREL Experimental Bulk Properties

Young’s Modulus, E 41.42 GPa

Poisson’s Ratio ν 0.26

Ultimate Tensile S trength σult 786 Mpa

Maximum Average Strain εmax 2.01%

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Figure 50: GREFL Specimen used during testing

The AE monitoring device utilized during testing is manufactured by Physical Acoustics

Corporation. The acquisition system is Micro-II digital AE System model shown in

Figure 49. It consists of a 4 channel AEWin DiSP system with two piezoelectric sensors

(Pico) and preamplifiers with a uniform gain of 40 dB. The system holds a PCI/DEP-4

board with high speed channels, the PIC bus can obtain AE data transfer speeds up to 132

Megabytes/second. The system includes high speed PCI cards, LAN networking

capabilities, 4 16-bit high speed A/D convertors, 8 parametric outputs, programmable

filtering and real time processing. The piezoelectric sensors used for this experiment were

wide band sensors which allowed for a broader range of frequencies. The piezoelectric

transducers operated between 200-750 kHz with 500 kHz being the peak frequency. Two

Pico receivers were placed on the GFRP specimen, one near the top and one near the

bottom.

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Figure 51: Micro II Digital AE System

The sensors were bonded on the surface of the specimen using super glue. The received

signals were amplified using 2/4/6-AST pre-amplifiers and a threshold of 60 dB. The

threshold used was to minimize the recording of extraneous emissions/noise like the

mechanical vibrations caused by the fatigue motion from the MTS testing load frame

machine. Two sensors were placed 60 mm away from each other, one near the top and

bottom of the specimen. A MTS servohydraulic machine was used to perform the two

tensile tests which as a capacity of a 100 kN, shown in Figure 50. The quasi static

monotonic tension was displacement controlled at a speed of 2 mm/min, ≈ 3.7x10-4 s-1

strain rate. This experiment was used to show how IRT and DIC aided by AE can be used

as damage detection and if this hybrid setup can qualitatively track damage progression.

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Figure 52: MTS Servohydraulic Server used during tensile testing

The experiment was run and AE alerted when the first stages of damage occurred,

approximately 15 seconds after initiation, Figure 53 shows the absolute energy and load

profile of the test. After the onset of damage energy continues to increase, the damage

accumulation in this region can be seen in DIC images but not IR images which means

the damage accumulation occurring from internal damage that has not reached the

surface.

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Figure 53: Load and Absolute Energy of Tensile Test 1

From the temperature profile obtained for the specimen during testing shows that the

specimen actually begins to cool; as it approaches critical failure the temperature shows a

dramatic rise as shown in Figure 51. The temperature initially decreases because

molecular separation occurs from the displacement the specimen undergoes in each

direction, until the onset of damage causes critical failure when energy heated internally

is released.

Onset of Damage

Failure

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Figure 54: Temperature profile of GFREL during tensile test till failure

The images obtained from IRIC were compared with each other to see the progression of

damage. The GFR specimen is anisotropic with more complex failure mechanisms which

allow the strain localization to occur throughout the testing procedure. At different stages

images of obtained from DIC and IR were used and compared to assess damage. Figure

52 was taken after 170 seconds of the test right at the onset of failure and the strain and

thermal images, with the use of temperature gradients provide a validation of damage to

highlight the complex nature of composites. The temperature gradients, °C/mm, obtained

from the overall temperature show that in the x-direction the onset of damage is occurring

on the left side of the specimen; the temperature gradient in y shows the non-uniformity

in damage accumulation, these gradients correlate well with strain maps in the x and y.

Figure 53 shows an actual image of the specimen during the same time, which shows no

visible damage.

0 50 100 150 200

28

30

32

34

36

38

Tem

pera

ture

(C)

Time (s)

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Figure 55: GFREL tensile test after 170 sec A) Strain in X(µm/m) B) Strain in Y(µm/m) C) Temperature profile (°C) D) Temperature gradient in X (°C/mm) E) Temperature gradient

in Y (°C/mm)

Figure 56: Actual specimen during test at 170 seconds

The DIC system relies on tracking the light contrast provided by the speckle pattern,

consequently because of fiber breakage in and out of the plane by the GRFEL this pattern

A) B)

E)C) D)

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can no longer be tracked, IR images can still be obtained. In Figure 54 visible damage on

the specimen can be observed which caused the DIC to stop recording. The circled

regions represent visible fiber breakage and thermal images that followed after, all

temperature readings are given in Celsius. This section combined the use of DIC and IR

to track the progression of damage, the combination of both techniques validated the

occurrence of damage and also better identified the damage occurring in material. DIC

was able to identify surface deformation before IR, but IR was able to track final fracture

longer because surface deformations hampered the ability of DIC to continue its surface

tracking.

Figure 57: A) Surface Damage Occurs on GFREL last DIC Image, 182 seconds B) Thermal

Image 182 seconds C) Thermal Image 190 seconds D) Thermal Image 199 seconds.

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Chapter 6: Concluding Remarks and Future Work

6.1 Concluding Remarks The work in this thesis has shown that damage detection and material property

measurement are not mutually exclusive. The novelty of this thesis is to show the

flexibility in using hybrid NDT by combining IRT and DIC to obtain deformation and

thermal fields that will allow measurements of CTE of a specimen and identify damaged

regions by obtaining full field thermal maps and displacement fields. Chapter 4 showed

the use of IRIC as a feasible method to measure values of CTE values by measuring

AL2024 and comparing with reported values and by removing a more conventional

thermal chamber showed it was still possible to obtain accurate measurements. After

confirming the validity of this technique it was shown that now given full field data, the

specimen AL2024 specimen can be partitioned into smaller regions, with these smaller

regions local CTE values can be measured and showed good correlation to measurements

of the whole specimen. A damaged AL2024 specimen was used as a comparison by

measuring CTE of the whole specimen and partitioned regions which compared to an

undamaged sample. Furthermore the IRIC was then shown in chapter 5 as a means of

damage detection using a GFREL undergoing a tensile test till failure.

6.2 Future Work The measurement values recorded for CTE by IRIC can be more accurate with the use of

a better and more repeatable methodology. The use of a thermal chamber can be used to

provide a slower and more accurate temperature increase, better calibration to reduce

noise from DIC and the continual improvement of the speckle pattern for both DIC and

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IR measurements. Preliminary results showed that defects will alter CTE values for a

specimen; it should be further investigated if these changes can be quantified. Next it

would helpful to measure materials with unknown CTE with a variety of techniques to

compare the accuracy of IRIC. Local partitions were made on specimens to measure CTE

values to determine if certain regions were damaged, with the use of image stitching

between deformation and thermal fields could this be localized to the pixel level to

identify even smaller regions of potential defects. Image stitching of these fields will lead

better understanding of the behavior of materials by mechanical loading by having access

to the same field of view of temperature and displacements.

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Appendix A: Matlab Code to Calculate Temperature Gradient

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%Objective : To calculate the temperature of the Hexiom Composite Sample %%from the full field temperature obtained by the infrared camera %% Author : Prashanth Abraham %%Editor: Jefferson Cuadra clc clear close all d = uigetdir; txt = strcat(d, '\*txt'); files = dir(txt); ii=1; %---------------------------------------------------------------------------- %%% Extracting the data for i = 1:length(files) M = importdata((files(i).name)); %% Calculating the temperature data [mm,nn]=size(M); Tval=M; [uu,uuu]=size(Tval); [X,Y]=meshgrid(linspace(0,25,uuu),linspace(0,75,uu)); xI=linspace(0,48.6,20*uuu);yI=linspace(0,39.6,10*uu);%Interpolation Parameter = 10 [XI,YI]=meshgrid(xI,yI); dx=xI(2)-xI(1); dy=yI(2)-yI(1); ZI = interp2(X,Y,Tval,XI,YI); [DTX,DTY]=gradient(ZI,dx,dy); tx(ii)=mean(mean(DTX)); ty(ii)=mean(mean(DTY)); minx=min(min(DTX)); maxx=max(max(DTX)); subplot(1,3,1) surf(XI,YI,ZI,'LineStyle','none') view([0 90]) caxis([28 98]) xlim([-1,30]) ylim([-1,40]) colorbar

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axis equal subplot(1,3,2) % figure(2) surf(XI,YI,DTX,'LineStyle','none') view([0 90]) caxis([-2 2]) xlim([-1,30]) ylim([-1,40]) colorbar axis equal subplot(1,3,3) % figure(3) surf(XI,YI,DTY,'LineStyle','none') shading interp view([0 90]) caxis([-2 2]) xlim([-1,30]) ylim([-1,40]) colorbar axis equal ii=ii+1; end

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