석 사 학 위 논 문

Master’s Thesis

동적 상태 구조물의 손상 감지를 위한

비접촉식 선형 레이저 열화상 시스템 개발

Development of a Line Laser Thermography System

for Structural Damage Detection under Dynamic Conditions

2016

황 순 규 (黃 淳 奎 Hwang, Soonkyu)

한 국 과 학 기 술 원

Korea Advanced Institute of Science and Technology

석 사 학 위 논 문

동적 상태 구조물의 손상 감지를 위한

비접촉식 선형 레이저 열화상 시스템 개발

2016

황 순 규

한 국 과 학 기 술 원

건설및환경공학과

동적 상태 구조물의 손상 감지를 위한

비접촉식 선형 레이저 열화상 시스템 개발

황 순 규

위 논문은 한국과학기술원 석사학위논문으로

학위논문 심사위원회의 심사를 통과하였음

2016년 6월 28일

심사위원장 손 훈 (인 )

심 사 위 원 정 형 조 (인 )

심 사 위 원 안 윤 규 (인 )

Development of a Line Laser Thermography System

for Structural Damage Detection under Dynamic Conditions

Soonkyu Hwang

Advisor: Hoon Sohn

A thesis submitted to the faculty of

Korea Advanced Institute of Science and Technology in

partial fulfillment of the requirements for the degree of

Master of Science in Civil and Environmental Engineering

Daejeon, Korea

June 28, 2016

Approved by

Hoon Sohn, Ph. D

Professor of Civil and Environmental Engineering

The study was conducted in accordance with Code of Research Ethics1).

1) Declaration of Ethical Conduct in Research: I, as a graduate student of Korea Advanced Institute of Science and

Technology, hereby declare that I have not committed any act that may damage the credibility of my research. This

includes, but is not limited to, falsification, thesis written by someone else, distortion of research findings, and plagiarism.

I confirm that my dissertation contains honest conclusions based on my own careful research under the guidance of my

advisor.

초 록

적외선 열화상 기법은 비 접촉, 비 파괴, 비 간섭 및 즉각적인 검사 기법으로 구조물 손상 감지를

위해 다양한 방법으로 사용 될 수 있다. 특히, 적외선 열화상 기법은 손상 감지 능력을

향상시키기 위해 다양한 열 파 생성 기법들이 함께 사용 될 수 있는데, 특히 장거리 및 정밀 열

파 제어가 가능한 광학 기반의 레이저 가진 기법을 적외선 열화상 기법에 적용시켜 이의 사용성을

넓힐 수 있다. 본 논문은 적외선 열화상 기법과 광학 기반의 레이저 가진 기법을 융합한 선형

레이저 열화상 시스템 개발과 이의 실험적 검증을 보여주고자 한다. 해당 기술의 구체적인 목적은

(1) 비 접촉, 비 파괴, 비 간섭 및 즉각적인 구조물 검사, (2) 동적 상태에서의 구조물 검사, (3) 고

정밀 대형 구조물 검사, (4) 원거리 검사, 그리고 (5) 즉각적인 무 기저 기반의 손상 유무 판단

기법의 개발이다. 따라서, 제안 기술을 통해 기존의 구조물 비 파괴 검사 및 모니터링 기법들의

한계점들을 극복 할 수 있을 것으로 본다. 본 논문에서 선형 레이저 열화상 시스템은 (1) 기존

선형 레이저 기법을 발전 및 최적화, (2) 손상 감지 알고리즘의 개발, (3) 정적 상태 구조물들을

이용한 검증 실험, 그리고 (4) 동적 상태 구조물들을 이용한 검증 실험의 단계를 거쳐 제안 된다.

선형 레이저 열화상 시스템은 대상구조물의 거동 상태가 정적인지, 동적 인지에 따라 서로 다른

레이저 열 파 생성 기법이 적용 된다. 선형 레이저가 대상 구조물 표면에 열 파를 생성 하면, 이에

따른 대상 구조물 표면에서의 열 전파 경향이 열화상 카메라에 의해 열화상 이미지로 계측 된다.

여기서, 열 전파 경향은 구조물 표면 및 내부 손상 유무에 따라 정상 영역과는 상이하게 나타나며,

이를 결함 검출 알고리즘을 통해 추출하여 손상의 크기 및 위치를 자동으로 시각화 하며, 무 기저

기반의 손상 우무 판단으로 인해 높은 정확도의 손상 검사가 가능하다. 제안된 선형 레이저

열화상 시스템은 정적 혹은 동적 상태에 있는 탄소섬유 강화 복합재료, 유리섬유 강화 복합재료,

강판, 그리고 반도체 칩을 이용하여 성능이 검증되었다.

핵 심 낱 말 선형 레이저 열화상 기법, 비파괴검사, 층간 박리, 마이크로 균열, 탄소섬유 강화

복합재료, 유리섬유 강화 복합재료, 강판, 반도체 칩

MCE

20143735

황순규. 동적 상태 구조물의 손상 감지를 위한 비접촉식 선

형 레이저 열화상 시스템 개발. 건설및환경공학과. 2016년.

53+VI 쪽. 지도교수: 손훈. (영문 논문)

Soonkyu Hwang. Development of a Line Laser Thermography

System for Structural Damage Detection under Dynamic

Conditions. Department of Civil and Environmental Engineering.

2016. 47+V pages. Advisor: Hoon Sohn. (Text in English)

Abstract

Infrared (IR) thermography technique has a great potential for noncontact, nondestructive, noninvasive and

instantaneous detection of a structural damage. To enhance the detectability of the structural damage, various

thermal wave generating method can be utilized for IR thermography technique. In particular, the optic based laser

excitation technique can be adopted as the thermal wave generating method and broaden the usage of the IR

thermography with its unique properties such as long-distance thermal wave transmission and precise

controllability of excitation position and intensity. This thesis aims to develop and validate a line laser

thermography (LLT) system by integrating the IR thermography technique and optic based laser excitation

technique for structural damage detection in a dynamic condition. The main objectives of the proposed technique

are listed as (1) noncontact, nondestructive and noninvasive damage detection, (2) structural inspection in a

dynamic condition, (3) large scale structural inspection with high spatial resolution, (4) remote inspection, and (5)

instantaneous damage detection and baseline-independent decision making. These objectives contribute to jump

over the limitation of previous nondestructive testing (NDT) and structural health monitoring (SHM) methods. To

achieve them, the LLT system is developed through the following steps: (1) development and optimizing of the

conventional LLT system, (2) development of the damage detection algorithm, (3) experimental validation with

the various structure in the static condition, and (4) experimental validation with the various structure in the

dynamic condition.

The LLT system is developed by introducing different laser thermal wave generating method, depending on

whether the structure is in the static condition or not. When the laser generates thermal waves on the structure

surface, the corresponding thermal waves are recorded as the IR image from the IR camera. Here, the presence of

the structural damages such as surface micro-crack and delamination interrupt the thermal wave propagation and

this results in a surface temperature difference. The surface temperature difference measured in the IR image is

then automatically extracted with visualizing the position and the shape of the damage from the damage detection

algorithm. The damage detection algorithm diagnoses damages using only current-state data, making it possible to

avoid false alarms caused by operational and environmental variations. The proposed LLT system and the damage

detection algorithm is validated from the experiment using a carbon-fiber-reinforced polymer (CFRP), a glass-

fiber-reinforced polymer (GFRP), a steel plate and a semiconductor chip.

Keywords Line laser thermography, Non-destructive testing, delamination, micro-crack, carbon-fiber-reinforced

polymer, glass-fiber-reinforced polymer, steel plate, semiconductor chip

i

Contents

Contents ........................................................................................................................................................................... i

List of Figures ......................................................................................................................................................................................................... iv

Chapter 1. Introduction ....................................................................................................................................................................................... 1

1.1 Research Background .................................................................................................................................................... 1

1.2 Current Damage Detection Techniques ..................................................................................................................... 2

1.3 Research Objectives & Uniqueness............................................................................................................................ 3

1.4 Thesis Organization ........................................................................................................................................................ 4

Chapter 2. Line Laser Thermography System ............................................................................................................................................ 5

2.1 Principle of Laser Generated Thermal Wave ........................................................................................................... 5

2.1.1 Main Characteristics of Laser Beam ............................................................................................................. 5

2.1.2 Absorption of Laser Beam for Thermal Wave Generation ....................................................................... 5

2.1.3 Thermal Wave Propagation .............................................................................................................................. 7

2.2 Principle of Thermal Wave Measurement ................................................................................................................. 8

2.2.1 Fundamental of the Electromagnetic Spectrum .......................................................................................... 8

2.2.2 Thermal Emission by Matter ........................................................................................................................... 9

2.2.3 IR Camera and Radiation Detectors ........................................................................................................... 11

2.3 Hardware Configuration and Working Principle .................................................................................................. 12

Chapter 3. Damage Detection Algorithm .................................................................................................................................................... 15

3.1 Damage Detection under Static Conditions........................................................................................................... 15

3.2 Damage Detection under Dynamic Conditions .................................................................................................... 20

ii

Chapter 4. Experimental Validation under Static Conditions ............................................................................................................. 25

4.1 Experimental Setup ..................................................................................................................................................... 25

4.2 Delamination Detection on the CFRP plate under Static Conditions .............................................................. 26

4.2.1 Description of the CFRP plate ..................................................................................................................... 26

4.2.2 Experimental Results...................................................................................................................................... 26

4.3 Delamination Detection on the Wind Turbine Blade under Static Conditions ............................................. 28

4.3.1 Description of the Target Wind Turbine Blade ........................................................................................ 28

4.3.2 Experimental Results...................................................................................................................................... 29

4.4 Fatigue Crack Detection on the Steel Plate under Static Conditions .............................................................. 31

4.4.1 Description of the Target Steel Plate .......................................................................................................... 31

4.4.2 Experimental Results...................................................................................................................................... 31

Chapter 5. Experimental Validation under Dynamic Conditions ...................................................................................................... 33

5.1 Experimental Setup ..................................................................................................................................................... 33

5.2 Micro-crack Detection on the Semiconductor Chip under Dynamic Conditions ......................................... 34

5.2.1 Description of the Target Semiconductor Chip ........................................................................................ 34

5.2.2 Experimental Results...................................................................................................................................... 34

5.3 Fatigue Crack Detection on the Steel Plate under Dynamic Conditions ........................................................ 36

5.3.1 Description of the Target Steel Plate .......................................................................................................... 36

5.2.2 Experimental Results...................................................................................................................................... 36

5.4 Preliminary Study for Delamination Detection on the CFRP Blades in Rotating Condition .................... 37

Chapter 6. Concluding Remark ...................................................................................................................................................................... 38

6.1 Executive Summary .................................................................................................................................................... 38

6.2 Unique Contributions .................................................................................................................................................. 39

6.3 Path Forward ................................................................................................................................................................. 39

iii

Bibliography ........................................................................................................................................................................................................... 41

Acknowledgments in Korean ........................................................................................................................................................................... 43

Curriculum Vitae ................................................................................................................................................................................................. 44

iv

List of Figures

1.1 Incidents of large scaled structural collapse. (a) Industrial wind turbine blade breakage in

Netherlands, (b) I-35W bridge collapse in United States. ............................................................................ 1

1.2 Necessities of damage diagnosis on dynamic condition. (a) Inspecting in in-situ structure, (b)

Inspecting wide-area structure and the limited inspection area. ................................................................... 2

2.1 Laser beam as a practical usage. (a) optics, (b) astronomy, (c) manufacturing. ............................................ 5

2.2 Thermal wave generation scheme by incident laser beam on the material surface........................................ 6

2.3 Temperature distribution of the material surface due to the laser irradiation. (a) Gaussian laser beam,

(b) uniform laser beam. ................................................................................................................................. 7

2.4 Blackbody spectral radiant emittance according to Plank’s law. ................................................................. 10

2.5 Spectral emissivity of materials. (a) non-metal material and (b) metal material. ........................................ 11

2.6 Spectral response characteristics of various IR detectors. ........................................................................... 12

2.7 Schematic diagram of the line laser thermography (LLT) hardware. .......................................................... 12

2.8 Different target structure scanning methodology depending on the behavior of the target structure.

Scanning methodology for the structure (a) under static conditions and (b) under dynamic conditions.

.................................................................................................................................................................... 13

3.1 Overview of the instantaneous damage imaging algorithm in the static condition. (1) Abnormal

response extraction process: The ith laser affected image (Li) is computed from the raw thermal

images acquired using the IR camera, and the ith abnormality image (Ai) is obtained by subtracting

the normal image (N) constructed by averaging Li. (2) Image compression process: Based on the ith

line laser scanning position vector (𝑝𝑖⃗⃗⃗ ), each Ai is relocated and accumulated and the abnormality

image (A) is constructed. (3) Denoising process: the damage is highlighted in the binary image (B)

and the final image (F) after binary imaging and median filtering, respectively. ....................................... 15

3.2 Determination of the laser-affected image. (a) Estimation of the laser-generated temperature profile

after the line laser excitation by fitting an exponential distribution after the maximum temperature

(Tmax), (b) Calculation of the estimated temperature (Testimate) where the cumulative density function

v

(CDF) of the exponential distribution reaches 95 %, (c) Determination of the thermal-affected time

(∆t) based on the cooling time when Tmax goes to Testimate (d) Determination of laser-affected length

(w) for the given ∆t and (e) Determination of Li on the measuring area. .................................................... 17

3.3 Denoising process for eliminating the undesired noise components. .......................................................... 18

3.4 Overview of the time-compressed coordinate trasform (TCT) based micro-crack visualization

algorithm. (1) Computation of a reconstruction image: The infrared image in the time domain (It) is

reconstructed to the reconstructed image in the k domain (Rk) by assigning the data on the kth data

recording line (lk) in It. (2) Construction of the discontinuity image: The kth edge image (Ek) is

constructed by extracting the edge components on Rk and the edge image (E) is constructed by

averaging the Eks. (3) Denoising process for micro-crack visualization: The arbitrary noise

components on E is eliminated and the final image (F) is constructed. ...................................................... 20

3.5 Designation of the thermal wave propagation zone. (a) Line laser tracking, (b) Definition of the

thermal wave generation range. .................................................................................................................. 21

3.6 Construction of the kth reconstructed image (Rk): (a) Image reconstruction based on the data recording

line, (b) Alignment of the target structure based on the pixel tracking. ...................................................... 22

3.7 Edge extraction based on the Sobel filtering: (a) Horizontal and vertical Sobel convolution kernels

(Gx and Gy), (b) Convolution of the kth reconstructed image (Rk) using Gx and Gy, (c) Construction

of the edge image (E). ................................................................................................................................. 23

3.8 Noise elimination based on the binary imaging. .......................................................................................... 34

4.1 Lab-scale test setup of the proposed LLT system for static condition. ........................................................ 25

4.2 CFRP plate used for the validation experiment and the corresponding inspection areas. ........................... 26

4.3 Li, L and Ai images obtained from the inspection areas. (a) I and (b) II of the CFRP plate. ........................ 27

4.4 Delamination visualization using the Ai obtained from the CFRP plate. The A images obatained from

the inspection areas (a) I and (b) II, and the F images from the inspection area (c) I and (d) II. ................ 27

4.5 Wind turbine blades used for the validation experiments. (a) a 10 kW GFRP wind turbine blade and

the corresponding inspection areas and (b) a 10 kW GFRP wind turbine blade and the corresponding

inspection areas. .......................................................................................................................................... 28

vi

4.6 Delamination visualization using the Ai obtained from the 10 kW GFRP wind turbine blade. The A

images from the inspection area (a) I and (b) II, and the F images from the inspection area (c) I and

(d) II. ........................................................................................................................................................... 30

4.7 Delamination visualization using the Ai obtained from the 3 MW GFRP wind turbine blade. The A

images from the inspection area (a) I and (b) II, and the F images from the inspection area (c) I and

(d) II. ........................................................................................................................................................... 30

4.8 Steel plate for the validation experiment and the microscopic images near the fatigue crack. .................... 31

4.9 Fatigue crack visualization using the Ai obtained from the steel plate. The A images from the (a)

Intact area and (b) Damage area, and the F images from the (c) Intact area and (d) Damage area. ........... 32

5.1 Lab-scale test setup of the proposed LLT system for dynamic condition.................................................... 33

5.2 Semiconductor chip for the validation experiment and the microscopic images near the micro-crack.

.................................................................................................................................................................... 34

5.3 Detailed image processing procedure. (a) It in the time domain: (a-1) I180, (a-2) I200 and (a-3) I220, (b)

Rk=70 from lk=70 on It in the time domain and (c) Ek=70 from Rk=70....................................................... 35

5.4 Image processing results of semiconductor chips. (a) E and (b) F. .............................................................. 35

5.5 Steel plate for the target specimen. (a) Steel plate, (b) Microscopic image. ................................................ 36

5.6 Image processing results of semiconductor chips. (a) E and (b) F. ............................................................. 36

5.7 Rotating CFRP structure for the preliminary test. ....................................................................................... 37

5.8 Preliminary image processing result using the part of the proposed damage detection algorithm. (a)

Intact CFRP plate, and (b) Damage CFRP plate ......................................................................................... 37

1

Chapter 1. Introduction

1.1 Research Background

Advances in the structural design technologies have dramatically changed our lives and allowed the

construction of a large scaled structure such as a high rise building, a long span bridge, and a wind turbine blade

with over than 100 m of span. According to the enlarging scale of the structures, the interests in structural

maintenance are also getting the spotlight. However, most inspection methods for the structural maintenance are

still conducted based on the sampling inspection and the visual inspection with the naked eyes, therefore the tiny

structural damages are mostly not detected [1]. For example, a study from the FHWA (Federal Highway

Administration in US) revealed that at least 56 % of the average condition ratings were incorrect with 95 %

probability from the visual inspection [1].

Besides, it has reported that the tiny structural damages, which cannot detect from the visual inspection, can

also influence on a structural failure as shown in Figure 1.1. To be specific, the tiny structural damages, a micro-

sized-defect, is initiated from a damage precursor at unperceivable level when the structure is subjected to

environmental and operational loadings. Then, the precursor can often continue to grow to a critical point at an

alarming rate without sufficient warning, leading to structure failure [2]. So a new concept of structural inspection

system which can automatically detect the tiny structural damage is required.

Also, most structural inspection method is conducted on the static condition after shut off the use of the

structure. Here, the enormous economic loss is involved from the structural shut off. For example, mostly more

than 1 day is required to inspect the wind turbine blade on the static condition with involving typically more than

18 MWh of electric loss [3]. There are many efforts to develop the in-situ structural monitoring methods but there

remain various technical hurdles to overcome such as managing noise problems and a trade-off between inspection

area and detectable damage size (Figure 1.2).

Figure 1.1: Incidents of large scaled structural collapse. (a) Industrial wind turbine blade breakage in

Netherlands, (b) I-35W bridge collapse in United States.

2

Figure 1.2: Necessities of damage diagnosis on dynamic condition. (a) Inspecting in in-situ structure, (b)

Inspecting wide-area structure and the limited inspection area.

1.2 Current Damage Detection Techniques

A number of non-destructive testing (NDT) techniques have been proposed to inspect the structural damage.

One of the most widely used NDT techniques is the ultrasonic technique. Once ultrasonic waves generated by an

ultrasonic transducer installed on the target surface, the ultrasonic waves propagate through a target composite

structure. When the propagating ultrasonic waves encounter a structural damage, they are distorted due to

reflection, refraction and complex scattering at the structural damage boundary. By measuring the corresponding

ultrasonic responses using an ultrasonic sensor, the structural damage can be identified. The ultrasonic technique

has high detectability and is capable of penetrating inside the laminates, but complex signal processing is typically

required. Furthermore, spatial inspection range might be limited because the intensity of the measured ultrasonic

signal is highly attenuated due to the inhomogeneous characteristic of the composite structure [4]. Thus, a large

number of ultrasonic transducers are often needed for in-situ applications [5]. As alternatives, an air-coupled

transducer [6] and laser [7, 8] have been used for ultrasonic generation and sensing without any contact mechanism.

Thanks to their spatial scanning capability, structural damage localization and quantification can be achieved.

However, low signal-to-noise ratio, long scanning time and complex signal processing still hinder their application

to in-situ structures. Other promising NDT techniques for structural damage detection are X-ray radiography and

fiber bragg grating (FBG) sensing techniques. The X-ray radiography has the ability to inspect the internal

condition of the complex shapes and laminated structures with high spatial resolution, but the field application is

often limited due to harmful radiation issue and short working distance [9, 10]. The FBG sensing technique has

been proposed for structural damage detection by introducing multiplexing technique and wide area inspection

capabilities using a single sensor [11-13]. However, it still has technical disadvantages of the contact inspection

mechanism, particularly associated with maintenance issues, and structural damage quantification is difficult to

be accomplished.

3

To overcome the aforementioned technical problems, infrared (IR) thermography techniques have been

proposed as a fully noncontact structural damage imaging method in composite structures. The IR thermography

techniques have advantages of simple, fast, non-invasive, intuitive, and practical structural damage detection [14].

In particular, there are increasing interests on the IR thermography techniques due to the recent advancements in

IR sensor technology. Various heat sources such as optical lamp and eddy current have been utilized for generating

thermal effects. The most commonly used heat source is optical lamps including halogen lamp, xenon lamp and

flash lamp because they can generate thermal waves on large target area with a simple operation system [14, 15].

The eddy current method can also effectively generate the thermal waves and is used for structural damage

inspection [16]. However, they cannot precisely control the thermal wave excitation position and the intensity of

the thermal waves. Moreover, the working distance between the heat source and the target structure is typically

limited. As an alternative, the laser has been used as the heat source due to its superiorities such as long-distance

heat transmission and precise controllability of excitation position and intensity [17, 18]. But the small size of

laser beam often requires huge time to scan the large area. To tackle the technical limitations of the previous laser

excitation schemes, the authors’ group developed the line laser beam shaping technique and applied to surface

micro-crack inspection on semiconductor wafer chips [19, 20]. Nevertheless, its application is limited to the

surface defect inspection because the thermal wave induced by the laser beam mainly propagates through the

surface direction rather than the through-the-thickness direction [21, 22].

1.3 Research Objectives & Uniqueness

The primary aim of this dissertation is to develop and validate a line laser thermography (LLT) system for

damage detection on the structure not only in the static condition but also in the dynamic condition which can

adopted for the large-scale structure. The study is unique in that it embodies the following objectives in detail: (1)

Development of the line laser thermography hardware, (2) Development of the damage detection algorithm, and

(3) Experimental validations of the proposed techniques.

(1) Development of the line laser thermography hardware

First, the complete noncontact, nondestructive and noninvasive LLT system is developed for inspecting the

structure both on the static condition and the dynamic condition. This system adopts laser as thermal wave

generating device. Due to the characteristics of the laser beam, the LLT system can conduct the remote inspection

under the highly precise control. According to the scale and the operating condition of the target structure, the

laser scanning method is decided with allowing large scaled structure inspection. Also, the laser beam shape is

modified to the line laser beam by introducing a specially designed cylindrical plano-concave lens and reduce the

structure inspection time.

4

(2) Development of the damage detection algorithm

A damage detection algorithm for the thermal images from the LLT system is developed. First, the thermal

images obtained from the LLT system is reconstructed properly according to the condition of the target structure.

Due to the reconstructing thermal images, the proposed algorithm is able to inspect the large scaled structure with

high precision under dynamic conditions. After that, the abnormal thermal propagation phenomena are extracted,

based on the phenomena that the damage affect the material’s thermal propagation in a distinctive manner. Then,

a binary image processing algorithm based on extreme value statistics is applied. Finally, noise removal process

using Median filter is applied. The proposed algorithm offers the diagnosis of damages only using current-state

data. This, in turn, allows the avoidance of false alarms due to operational and environmental variations. The

reference-free dmage evaluation algorithm is able to provide instantaneous and automated diagnostic results,

attributes deemed significant for real-time inspection.

(3) Experimental validations of the proposed techniques

The proposed LLT system and the damage detection algorithm are validated through the experiment. Various

structures are used for the validation, such as CFRP plate, wind turbine blade, steel plate and semiconductor chips.

Here, the structures are inspected both under the static condition and the dynamic condition. Also, for the damage

type, not only surface defect structures but also subsurface defect structures are taken into account. Although the

developed system and algorithm are still in their infancy and thus encompass numerous technical challenges for

real applications, their feasibility verified via experimental results offer possible improvements and path toward

real applications. The associated practical issues and possible solutions are also included in this thesis.

1.4 Thesis Organization

This thesis is organized as follows. In Chapter 2, the overall explanation of the proposed LLT system is

explained. First, the fundamental theory such as principle of light behaviors, principle of laser-induced thermal

wave generation and principle of thermal wave measurement are presented. Based on those fundamental theories,

the LLT system is developed and the hardware configuration and the working principles are described. Chapter 3

explains the developed damage detection algorithm for target structure on the static condition and the dynamic

condition is described. In Chapter 4, the experimental validation under static conditions is reported and the

experimental under dynamic conditions is also reported in the following Chapter 5. Finally, the summary and

conclusion are provided in Chapter 6.

5

Chapter 2. Line Laser Thermography System

2.1 Principle of Laser Generated Thermal Wave

2.1.1 Main Characteristics of Laser Beam

The beam emitted by a laser is considered as electromagnetic radiation. The laser beam has its spectral range

of infrared and ultraviolet as well as the visible spectrum. The laser beam has the following characteristics: (1)

narrow spectral linewidth (quasi-monochromaticity), (2) good collimation for directivity, (3) high spatial and

temporal characteristics, (4) coherence properties, and (5) localized high power intensity and long range energy

delivery. These characteristics enable lasers to be used for many practical applications as shown in Figure 2.1. In

nondestructive testing and structural health monitoring, laser beams can be widely applied for thermal wave

generation, ultrasonic generation, temperature measurement, displace measurement, and so forth thanks to the

aforementioned characteristics. In this dissertation, the laser beam is used as the thermal wave generation source.

The following subchapter describes the material surface absorption of the laser beam for thermal wave generation.

Then, the thermal wave propagation phenomenon is summarized.

Figure 2.1: Laser beam as a practical usage. (a) optics, (b) astronomy, (c) manufacturing.

2.1.2 Absorption of Laser Beam for Thermal Wave Generation

Figure 2.2 shows the schematic diagram of the thermal wave generation by an incident laser beam on the

material surface. The laser beam can be applied for heating, melting and vaporization by controlling the beam

intensity [23]. Those are based on the physical processes that occur during the interaction of the laser with

materials. Knowledge of this process is important for understanding the capabilities and limitations of laser-

induced thermal wave generation.

6

Figure 2.2: Thermal wave generation scheme by incident laser beam on the material surface.

When a pulsed laser beam impinges on a material, the beam energy is partially absorbed depending on the

material characteristics and the wavelength of the laser beam [24]. Absorption is the process by which an incident

laser beam energy is converted to another form of energy, usually heat, leading to rapid localized temperature

increase. If the laser power is kept sufficiently low enough, the material does not melt and ablate. After the energy

from the laser beam begins to be absorbed as heat at the surface, the temperature distribution across the surface is

the same as the energy density distribution in the optical pulse. In case of a uniform laser beam profile on a metallic

plate, the temperature rise in the irradiated material is uniform which can simply be explained by the following

equation [25]:

𝛿𝑇 =𝛿𝐸

𝐶𝜌𝐴𝛿 (2.1)

where, 𝛿𝑇 is the temperature rise in the material surface, 𝛿𝐸 is absorbed energy density, C is the specific

thermal capacity of the material, 𝜌 is the density of material, A is the laser radiated area, and 𝛿 is the skin depth.

The temperature rise in the material surface is proportional to the absorbed energy density. However, most laser

beam profiles can not only be a uniform distribution but also a non-uniform distribution with specific shape. The

beam profile of a pure laser mode is generally Gaussian shape. In the case of the laser with Gaussian distributed

beam profile, the peak laser intensity is much higher compared to that of the laser with uniformly distributed beam

profile (Figure 2.3). Note here that the mean laser power intensity is kept equal. Thus, the surface temperature rise

induced by the laser beam should be considered carefully based on the shape and intensity of the laser beam to

prevent surface ablation and vaporization.

7

Figure 2.3: Temperature distribution of the material surface due to the laser irradiation. (a) Gaussian laser beam,

(b) uniform laser beam.

Once The laser radiation penetrates into the sample, it is progressively attenuated according to a following

equation which is well known exponential form of absorption:

𝐼′(𝑧) = 𝐼′(0)𝑒−𝛾𝑧 (2.2)

where z is a penetration depth of the laser beam and γ is the absorption coefficient. A skin depth, δ, can be defined,

such that the intensity falls to 1/e of its initial value over a distance δ. At longer wavelengths, a classical physics

can be used to calculate the skin depth as shown in following equation [26]:

𝛿 = (𝜋𝜎𝜇𝑟𝜇𝑜𝑣)−12 (2.3)

where σ and μr are the conductivity and relative permeability of the metal, respectively. μo is the permeability of

free space and v is the frequency of the radiation. This formula cannot be used at higher frequencies. From the

above equations, the skin depth is reduced for shorter wavelengths. Thus shorter wavelengths are likely to lead to

more efficient generation of thermal wave by laser since more energy is absorbed into the material.

2.1.3 Thermal Wave Propagation

When an excitation laser beam illuminated onto a specific point of the material, the temperature of the laser

excited region increases tremendously and thermal waves are generated. A material with having a temperature

gradient has shown that there is an energy transfer from their high-temperature region to the low-temperature

region by conduction phenomenon. The relationship between the thermal conductivity and the temperature

gradient induced by the laser beam can be expressed by using Duhamel’s theorem in a semi-infinite metal medium

after the irradiation of a laser beam as follows [27]:

8

𝑇(𝑟, 𝑧, 𝑡) =𝐼𝑚𝑎𝑥𝑟𝑎

2𝑘1/2

𝐾𝜋2∫

𝑝(𝑡 − 𝑡′)𝑒𝑥𝑝(−𝑧2

4𝑘𝑡′ − 𝑟2/(4𝑘𝑡′ + 𝑟𝑎2))

𝑡′12(4𝑘𝑡′ + 𝑟𝑎

2)𝑑𝑡′

𝑡

0

(2.4)

where r and z are cylindrical coordinates with the origin on the surface at the center of the irradiated spot. Imax is

the maximum power density of the laser pulse, p(t) is the normalized temporal profile of the laser pulse at time t,

k is the diffusivity, K is the conductivity, and ra is the laser beam radius.

These theorems can be applied for a nondestructive testing method. If the propagating thermal waves

encounter a surface crack, the thermal waves are blocked due to the thermal conductivity difference at the crack

interface. Due to this difference, thermal wave energy is accumulated at the interface as the majority of the thermal

waves cannot pass through. Then, the surface crack can be successfully visualized. Unlike surface crack, a

subsurface defect can be visualized upon thermal diffusion rate difference. When the test specimen is thermally

stimulated by the laser, the surface temperature increases and the heat energy diffuses toward the thickness

direction of the specimen. Because the presence of a subsurface defect reduces the diffusion rate, a higher

temperature compared to the surrounding area is observed at the subsurface defect location.

2.2 Principle of Thermal Wave Measurement

2.2.1 Fundamental of the Electromagnetic Spectrum

Regardless of the state and the composition of the materials, every material continuously emits

electromagnetic radiation. A radiation can be explained with the phenomenon: (1) the transportation of tiny

particles called photons or quanta, (2) the propagation of electromagnetic waves. A rise in temperature of the

material increase the number of tiny particles which emitted from the material surface. Each tiny particle carries

a particular electrical charge. The amount of energy by a photon, E, can be directly related to the corresponding

wavelength, λ, or frequency, f, of a wave, through the following relationship:

𝐸 = ℎ𝑓 =ℎ𝑐

𝜆 (2.5)

where h is the Planck’s constant and c is the speed of light. From the above equation, the relationship between

frequency and wavelength can be obtained.

𝑓 =𝑐

𝜆 (2.6)

Hence, electromagnetic waves at different λ or f are basically the same kind of physical objects, electromagnetic

radiation, the difference among them is in the amount of photon energy, E, related to each particular λ and f,

9

respectively.

The wavelength of an emitted tiny particle varies inversely with the amount of energy. In the IR region, the

wavelengths are long so that the radiation energy is low. Furthermore, certain substances emit particles that have

discrete range of wavelengths. Therefore, it is difficult to detect a clear IR signal without selecting a proper IR

detector with the target material [28].

Whenever there is a thermal difference between two objects, radiation will be exchanged in the form of heat.

The thermal radiation band is enclosed between 0.1 and 1000 µm of the spectrum. The thermal spectrum can be

divided into three spectral bands: the ultraviolet (UV) spectrum, the visible band and the IR. The IR spectrum can

mainly be divided into three large regions, near infrared from 0.73 to 3 µm, middle IR from 3 to 5 µm and far IR

from 8 to 14 µm according to the thermal detectors used to capture it. The IR radiation is invisible to the naked

eye. Hence, energy radiated in the IR band needs to be transformed into a visible image through specialized

imaging equipment using appropriate IR detector with particular measuring spectral bands.

2.2.2 Thermal Emission by Matter

Thermal emission by materials is usually treated in terms of the concept of a blackbody, defined to be an

object capable of totally absorbing all incident radiation, whatever its wavelength is. According to the Kirchhoff’s

laws (1860), such an object is also an emitter of radiation of all wavelengths. It transfers energy to the surroundings

until a state of thermodynamic equilibrium is reached. The emission of radiation by blackbody is able to explain

by Planck’s law, which describes the spectral distribution of the radiation intensity from a blackbody:

𝐸𝜆𝑏 =2𝜋ℎ𝑐2

𝜆5(𝑒ℎ𝑐𝜆𝑘𝑇 − 1)

(2.7)

where 𝐸𝜆𝑏 is the blackbody monochromatic radiation intensity, c is velocity of beam, h is Planck’s constant, k is

Boltzmann’s constant, T is absolute temperature of the blackbody, and λ is the wavelength of the blackbody.

Planck’s formula produces a family of curves when plotted graphically for various temperatures as shown in

Figure 2.4. According to the curves from Planck’s formula, the spectral emittance is zero when 𝜆 = 0 , then

increases rapidly to a maximum at a specific wavelength and after passing it approaches zero again at very long

wavelengths.

The displacement of the maximum wavelength values is then described by Wien’s law, obtained by

differentiating Planck’s law:

10

Figure 2.4: Blackbody spectral radiant emittance according to Plank’s law.

𝜆𝑚𝑎𝑥 =2898

𝑇 𝜇𝑚 (2.8)

Thus, a very hot star such as Sirius (11000 K), emitting bluish-white beam, radiates with the peak spectral radiant

emittance occurring within the invisible ultraviolet spectrum, at wavelength 0.27 μm. The sun (6000 K) emits

yellow beam, peaking at about 0.5 μm in the middle of the visible beam spectrum. At room temperature (300 K)

the peak of radiant emittance lies at 9.7 μm which is in the far IR. By integrating Planck’s formula from λ=0 to

λ=∞, the total radiant emittance of a blackbody can be obtained:

𝐸𝑏 = 𝜎𝑇4 (2.9)

where 𝜎 is the Stefan-Boltzmann constant. This is the Setfan-Boltzmann formula which states that the total

emissive power of a blackbody is proportional to the fourth power of its absolute temperature. For example, if we

wish to determine the radiation generated by the human body at approximately T=300 K, we have:

𝐸𝑏 = 5.7 × 10−12 × (300)4 = 0.05Wc𝑚−2 (2.10)

Therefore, the heat lost by radiation from 2 m2 of skin surface is about 1 kW assuming that the skin is a black

body. So far, only blackbody radiators and blackbody radiation have been discussed. However, real objects almost

never comply with these laws over an extended wavelength region. Thus, the spectral emissivity coefficient, 휀𝜆,

11

is used to estimate the temperature of a real object. A real object generally emits only a part 𝐸𝜆 of the radiation

emitted by a blackbody at the same temperature and at the same wavelength. The corresponding equation is driven

below:

휀𝜆 =𝐸𝜆

𝐸𝜆𝑏

(2.11)

Each material has a specific spectral emissivity value with same condition as shown in Figure 2.5. But the

emissivity is decided based on not only the material composition but layers on the surface, surface roughness and

the angle to surface normal. Therefore, it is not quite absolute way to decide emissivity only from emissivity tables.

Figure 2.5: Spectral emissivity of materials. (a) non-metal material and (b) metal material

2.2.3 IR Camera and Radiation Detectors

Thermography allows us not only to see the invisible IR radiation, but also to detect and to evaluate it. From

IR thermography system, the invisible radiation emitted by different object, which cannot be directly perceived

by the eyes, is thus transformed into recognizable image by optoelectronic detection system. The IR detectors are

used and transform an incident IR signal into an electrical signal or response and then, reconstruct recognizable

2D plane image in time domain. The IR detectors are classified into thermal types and quantum types. Thermal

type detectors use the IR energy as heat and their photo sensitivity is independent of wavelength. Thermal type

detectors do not require cooling, but have disadvantages that response time is slow and detection capability is low.

In contrast, quantum type detectors offer higher detection performance and a faster response speed, although their

photo sensitivity is dependent on wavelength. In general, quantum type detectors must be cooled for accurate

measurement, except for detectors used in the near IR region. Types of the IR detectors and their typical spectral

response characteristics are shown in Figure 2.6. The choice of the IR detector depends on the working conditions

expected of the system. For instance, the working conditions can be the spectral sensitivity range, temperature

sensitivity, temperature range, response speed, and the maximum acceptable noise.

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Figure 2.6: Spectral response characteristics of various IR detectors.

2.3 Hardware Configuration and Working Principle

The line laser thermography (LLT) hardware consists of three units as shown in Figure 2.7: (1) an excitation

unit composed of a continuous wave (CW) laser, a cylindrical lens, a galvano-motorized scanner and an F-theta

lens mounted in front of the galvano-motorized scanner, (2) a sensing unit comprised of an IR camera and a

focusing lens mounted in front of the IR camera and (3) a control unit compromised of a control computer.

Figure 2.7: Schematic diagram of the line laser thermography (LLT) hardware.

13

Figure 2.8: Different target structure scanning methodology depending on the behavior of the target structure.

Scanning methodology for the structure (a) under static conditions and (b) under dynamic conditions.

The working principle of the LLT system is as follows. First, the control computer in the control unit sends

out the control and trigger signals to the CW laser. Then, the CW laser emits a point laser beam, and the cylindrical

lens transforms the point laser beam to the line laser beam with a Gaussian profile. Simultaneously, the control

unit sends out the control signal to the galvano-motorized scanner. Here, depending on whether the target structure

is on the dynamic condition or not, the target structure is scanned by the line laser beam with different scheme as

shown in Figure 2.8. When the target structure is in the static condition, the galvano-motorized scanner scans the

target structure with controlling the position of the line laser beam. Otherwise, when the target structure is in the

dynamic condition, the line laser beam is fixed on the path of the moving target structure. The line laser beam is

focused at the target structure using the F-theta lens and generates thermal waves on the target structure. When

the line laser beam is radiated to the target structure, thermal waves are generated and propagate along the surface

and the subsurface of the target structure. The relationship between the thermal conductivity and the temperature

gradient induced by the line laser beam in a semi-infinite metal medium after irradiation of the single-spot pulse

laser beam can be expressed using Duhamel’s theorem as follows [19]:

𝑇(𝑥, 𝑦, 𝑧, 𝑡) =𝐼𝑚𝑎𝑥𝑟𝑎𝑟𝑏𝜅

1/2

𝑘𝜋1/2∫

𝑝(𝑡 − 𝑡′)exp(−𝑧2

4𝜅𝑡′ −𝑥2

(4𝜅𝑡′ + 𝑟𝑎2)

−𝑦2

(4𝜅𝑡′ + 𝑟𝑏2)

)

𝑡′1/2√4𝜅𝑡′ + 𝑟𝑎2√4𝜅𝑡′ + 𝑟𝑏

2

𝑡

0

d𝑡′ (2.12)

where x, y, and z are Cartesian coordinates with the origin on the surface at the center of the line laser beam in the

target structure surface. Imax is the maximum power density of the laser pulse, p(t) is the normalized temporal

intensity profile of the laser pulse at time t, 𝜅 is the diffusivity, k is the conductivity, ra is the width of the line

laser beam and rb is the height of the line laser beam. Once the thermal waves encounter a damage, which induces

14

a sudden change in the thermal properties with respect to the surrounding area, variation occurs in the patterns of

the thermal wave propagation. Then, the corresponding thermal responses are captured as thermal images in the

time domain by the IR camera in the sensing unit. Here, the inspection area captured by the IR camera is

determined by the working distance and the specification of the focusing lens. The captured IR images are

transmitted to the control computer of the control unit and processed using an instantaneous damage imaging

algorithm in the static condition described in the subsequent section. The image acquisition and processing are

automatically preformed using LabVIEW® and MATLAB® programs installed in the control computer.

Note that thermal wave propagation distance is typically limited in a composite material due to the low

thermal conductivity (k), typically 3.0 Wm-1K-1, which is quite low compared with k =3.0 Wm-1K-1 of a typical

steel material. Thus, the rapid inspection of a large area is difficult to be achieved using a single beam excitation

scheme at a specific spatial area in composites. For this reason, the thermal wave propagation phenomenon is

artificially induced by employing the mechanical scanning scheme, LLT, in this study. The proposed LLT system

enables to achieve the rapid inspection of a large area and to simultaneously inspect surface and subsurface

damage types.

15

Chapter 3. Damage Detection Algorithm

3.1 Damage Detection under Static Conditions

Figure 3.1: Overview of the instantaneous damage imaging algorithm in the static condition. (1) Abnormal

response extraction process: The ith laser affected image (Li) is computed from the raw thermal images acquired

using the IR camera, and the ith abnormality image (Ai) is obtained by subtracting the normal image (N) constructed

by averaging Li. (2) Image compression process: Based on the ith line laser scanning position vector (𝑝𝑖⃗⃗⃗ ), each

Ai is relocated and accumulated and the abnormality image (A) is constructed. (3) Denoising process: the damage

is highlighted in the binary image (B) and the final image (F) after binary imaging and median filtering,

respectively.

16

The instantaneous damage imaging algorithm in the static condition is developed to extract and visualize

only damage information from the measured thermal images by an IR camera. When a target structure surface is

exposed to the continuous line laser beam, thermal waves are generated and propagate along the target structure.

Since subsurface damages in composites is typically formed by the air gap, k of the damaged area (approximately

0.024 Wm-1K-1) is much lower than the sound area (approximately 3.0 Wm-1K-1 for the composite structure and

200.0 Wm-1K-1 for the steel structure). Therefore, the thermal waves are blocked and accumulated at the boundary

of the damaged area. The corresponding temperature to the damaged area becomes much higher than that of the

surrounding sound area based on the following equation [29]:

𝑇(𝑡) = (𝑇𝐿 − 𝑇0)𝑒−𝑘𝑡 + 𝑇0 (3.1)

where, T0 and TL are the initial temperature and the temperature after laser excitation at a specific spatial point,

respectively. t denotes the time. T will be locally changed as a function of t after the line laser beam is exerted

onto the target surface. Thus, the variation of T caused by damaged area can be the promising feature representing

damaged location and shape. Note that the target structures should have a homogeneous conductivity. The

instantaneous damage imaging algorithm in the static condition consists of three processes as shown in Figure 3.1

and shows how damaged information can be extracted and visualized in this study.

(1) Abnormal response extraction process

When the target structure is mechanically scanned by the line laser beam, thermal waves are generated on

the target structure. The thermal wave generation is spatially different due to the moving laser heat source. At a

specific spatial point, T typically shows exponential decrement behavior after the line laser excitation as a function

of t as shown in Figure 3.2 (a), because the line laser heat source passes through this spatial point. Thus, the

measuring area of interest using the IR camera contains spatially different temperature distribution in the time

domain, which is defined as the laser affected image (Li) as shown in Figure 3.2 (e). Here, h and w are the height

and the width of Li, respectively. The thermal response affected from the line laser excitation changes depending

on the surface and internal condition of the target structure and the damage area can be extracted by analyzing Li.

The detailed procedure of Li calculation is shown in Figure 3.2. Figure 3.2 (a) shows that the thermal response

produced by the line laser excitation undergoes initial heating and the subsequent cooling processes in the time

domain at the middle point in thermal image. According to Equation (3.1), an exponential distribution can be fitted

to the cooling process of the thermal response. Then, to define the temperature where the thermal wave affected

by the line laser excitation is dissipated, the temperature on 95 % confidence interval in the upper-tail of the

estimated exponential distribution and defined as 𝑇𝐸 as shown in Figure 3.2 (b). Then, the thermal affected time

(∆𝑡), which represents the time period required for thermal energy dissipation induced by laser heating, is set to

the time gap between the time when 𝑇𝐿 goes to 𝑇𝐸 as shown in Figure 3.2 (c). The travel distance of the scanning

line laser beam for ∆𝑡 is computed by multiplying the laser scanning speed (s) and ∆𝑡 as shown in Figure 3.2

17

(d). Here, the ith line laser scanning position vector (pi⃗⃗ ) is automatically calculated by pixel tracking the maximum

temperature in the thermal image. Finally, Li is designated from the thermal image by positioning the right end of

the Li to pi⃗⃗ and setting the width and height of the Li to w and h, respectively, the as shown in Figure 3.2 (d).

Figure 3.2: Determination of the laser-affected image. (a) Estimation of the laser-generated temperature

profile after the line laser excitation by fitting an exponential distribution after the maximum temperature (𝑇𝑚𝑎𝑥),

(b) Calculation of the estimated temperature (𝑇𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒 ) where the cumulative density function (CDF) of the

exponential distribution reaches 95 %, (c) Determination of the thermal-affected time (∆𝑡) based on the cooling

time when 𝑇𝑚𝑎𝑥 goes to 𝑇𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒 (d) Determination of laser-affected length (w) for the given ∆𝑡 and (e)

Determination of Li on the measuring area.

The spatial position changes on the designated pixel in Li in the time domain, so the thermal responses from

the sound and damaged area are shown in designated pixel position in different Li. The normal image (N) is, then,

simply calculated by averaging the whole Li in the time domain, and the thermal responses from the damaged area

is slacked. So, N physically shows T after the line laser beam excitation on the sound area. Finally, the damage

area is highlighted on the ith abnormal image (Ai) by subtracting N from Li with eliminating the temperature

response induced by the line laser beam on the sound area.

(2) Image compression process

Once Ai is obtained in the previous process, it is reassigned onto the measuring area image for the image

18

compression. Put the zero-pedding details here. which has an identical image size with the thermal image. Here,

the right end of the Ai is located at pi⃗⃗ . Then, the multiple Ai are compressed in a single abnormality image (A) by

adding the whole Ai. A highlights abnormal thermal wave components in a single image including all the abnormal

components presented in Ai.

(3) Denoising process

Figure 3.3: Denoising process for eliminating the undesired noise components.

The computed A in the previous process includes undesired noise components such as surface patterns,

surface reflection caused by surrounding arbitrary hear sources and measurement noises. Since those undesired

components can be misclassified as damage, the binary imaging and median filtering are used to eliminate them

as shown in Figure 3.3. In here, before constructing a binary image (B), an additional process is conducted for the

micro-crack detection. For the micro-crack detection, an edge image (E) of A is constructed by extracting edge

components using Sobel filter, which consists of a pair of 3 × 3 Sobel convolution operators, Gx and Gy:

𝐺𝑥 = [−1 0 +1−2 0 +2−1 0 +1

], 𝐺𝑦 = [−1 −2 −10 0 0

+1 +2 +1] (3.2)

where Gx 6o separate images, Ex and Ey, visualizing the thermal gradients in the x and y directions, respectively.

Ex and Ey are then combined into the E as follows:

𝐸(𝑥, 𝑦) = √𝐸𝑥2(𝑥, 𝑦) + 𝐸𝑦

2(𝑥, 𝑦) (3.3)

19

Then, B of A and E is constructed by changing the entities of A and E above and below a certain threshold value

to 1 and 0, respectively. The threshold value is estimated by using the Weibull distribution. The Weibull

distribution is a branch of statistics that model the statistical properties of extreme values in either the upper or

lower tails of data [30]. Once a Weibull distribution is fitted to all the pixel values in A and E, a threshold value

corresponding to a one-sided 99 % confidence interval in the upper tail is selected. Because the salt and pepper

noises, which usually have extreme large or small pixel values, still remains in B, a median filter is further applied

to eliminate the salt and pepper noise components by selecting the median value within a 3 × 3 kernal [31].

Finally, the final image (F) is constructed and only damage components are visualized. Here, the size and area

information of damage can be calculated by counting the pixel number whose pixel value is 1 and multiplying the

actual size of each pixel.

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3.2 Damage Detection under Dynamic Conditions

Figure 3.4: Overview of the time-compressed coordinate trasform (TCT) based micro-crack visualization

algorithm. (1) Computation of a reconstruction image: The infrared image in the time domain (It) is reconstructed

to the reconstructed image in the k domain (Rk) by assigning the data on the kth data recording line (lk) in It. (2)

Construction of the discontinuity image: The kth edge image (Ek) is constructed by extracting the edge components

on Rk and the edge image (E) is constructed by averaging the Eks. (3) Denoising process for micro-crack

visualization: The arbitrary noise components on E is eliminated and the final image (F) is constructed.

The time-compressed coordinate transform (TCT) algorithm is developed to visualize the micro-cracks on

the moving target specimens into the single image. The IR images in the time domain (It) show the thermal waves

on the target specimens in moving condition with the limited spatial range. After the coordinate transform of It,

the thermal waves on the whole target specimens are shown in the single image in the static condition as the

21

reconstructed images in the k domain (Rk). At the interface of the micro-cracks on the target structures, typically

air gap is formed and the material properties (including thermal conductivity) are abruptly changes and the thermal

waves on the target specimens are blocked. By extracting the thermal wave blocking boundary, the edge image

(E) is constructed and the micro-crack can be visualized at the final image (F) after denoising process. The

overview of TCT algorithm is illustrated in Figure 3.4 and the details of the proposed image processing procedures

are composed on the following three processes.

(1) Computation of a reconstruction image

Figure 3.5: Designation of the thermal wave propagation zone. (a) Line laser tracking, (b) Definition of the

thermal wave generation range.

When the moving target structure pass through the fixed line laser beam, the IR camera records the thermal

wave on the line laser beam excited area as It. The part of It contains the thermal waves induced from the line laser

beam and defined as the thermal wave propagation zone and used for the further image processes.

The procedures of how thermal wave propagation zone is decided are described in Figure 3.5. First, the direction

and the position of the laser beam is tracked as line laser beam vector (𝑙 ) (Figure 3.5 (a)). To be specific, the upper

bound (yu) and the lower bound (yl) of It is decided (Figure 3.5 (a-1)), then, the horizontal position of the line laser

beam on yu and yl are tracked, as xu and xl respectively, by finding the maximum temperature on yu and yl (Figure

3.5 (a-2)). Then, the line laser beam is tracked as 𝑙 (Figure 3.5 (a-3)):

22

𝑙 = (𝑥𝑢 − 𝑥𝑙 , 𝑦𝑢 − 𝑦𝑙) (3.4)

Next, the thermal wave propagation zone is defined based on the thermal wave generation ranges (rb, ra) (Figure

3.5 (b)). To be specific, the temperature distribution at the middle horizontal line of It (ym) is decided, then, the

temperature distribution before and after the laser excitation point on ym is estimated using the exponential

distribution, 𝑇𝑦𝑚,𝑏′ and 𝑇𝑦𝑚,𝑎

′ respectively (Figure 3.5 (b-1)). Next, rb and ra are decided based on the 95 % of

the cumulative probability of 𝑇𝑦𝑚,𝑏′ and 𝑇𝑦𝑚,𝑎

′ , respectively (Figure 3.5 (b-2)). Finally, the thermal wave

propagation zone is decided based on 𝑙𝑘⃗⃗⃗ (Figure 3.5 (b-3)):

𝑙𝑘⃗⃗⃗ = (𝑥𝑢 − 𝑥𝑙 , 𝑦𝑢 − 𝑦𝑙) + (−𝑟𝑏 + 𝑘, 0) (3.5)

where, k goes 1 to rb + ra.

Figure 3.6: Construction of the kth reconstructed image (Rk): (a) Image reconstruction based on the data

recording line, (b) Alignment of the target structure based on the pixel tracking.

After the thermal wave propagation zone is decided, each data on 𝑙𝑘⃗⃗⃗ is integrated and reconstructed on the

kth reconstructed image (Rk) as shown in Figure 3.6. To construct Rk, x axis in It is transformed to a global

coordinate x*:

𝐼𝑡 (𝑥𝑙𝑘⃗⃗ ⃗, 𝑦𝑙𝑘⃗⃗ ⃗) = 𝑅𝑘′ (𝑡, 𝑦𝑙𝑘⃗⃗ ⃗) (3.6)

First, the data on 𝑙𝑘⃗⃗⃗ in the whole It is assigned to tth column of the temporary kth reconstructed image (𝑅𝑘′ ) (Figure

23

3.6 (a-1, 2)). In this step, when 𝑙𝑘⃗⃗⃗ is inclined, the shape in 𝑅𝑘′ is distorted. To correct the distorted shape in 𝑅𝑘

′ ,

the left-side corner points at upper bound and lower bound (𝑅𝑘′ (𝑥𝑢

∗ , 𝑦𝑢)and 𝑅𝑘′ (𝑥𝑙

∗, 𝑦𝑙)) of the target specimen in

𝑅𝑘′ is tracked. Based on 𝑅𝑘

′ (𝑥𝑢∗ , 𝑦𝑢)and 𝑅𝑘

′ (𝑥𝑙∗, 𝑦𝑙), the inclined angle (𝜃) of the target specimen is calculated

and the distorted shape of 𝑅𝑘′ is corrected in 𝑅𝑘

′′ (Figure 3.6 (a-3)).

𝜃 = 𝑡𝑎𝑛−1 (𝑦𝑢 − 𝑦𝑙

𝑥𝑢∗ − 𝑥𝑙

∗) (3.7)

𝑅𝑘′ (𝑥∗, 𝑦) = 𝑅𝑘

′′ (𝑥∗ −𝑦

tan 𝜃, 𝑦) (3.8)

However, each pixels in 𝑅𝑘′′ are not matched due to the different 𝑙𝑘⃗⃗⃗ . So, the target specimens in 𝑅𝑘

′′ is aligned

by using the pixel tracking algorithm. By plotting the temperature profile at the middle row of the target structure

in 𝑅𝑘′′ (Figure 3.6 (b-1,2)), the left-end position of the target specimen in x* axis is tracked as pk and the kth

reconstructed image (Rk) is constructed after aligning the target samples in k domain.

𝑅𝑘′′(𝑥∗, 𝑦) = 𝑅𝑘(𝑥

∗ − 𝑝𝑘 + 𝑠, 𝑦) (3.9)

(2) Construction of the discontinuity image

Figure 3.7: Edge extraction based on the Sobel filtering: (a) Horizontal and vertical Sobel convolution kernels

(𝐺𝑥 and 𝐺𝑦), (b) Convolution of the kth reconstructed image (Rk) using 𝐺𝑥 and 𝐺𝑦, (c) Construction of the edge

image (E).

After Rk is constructed, the edge components on Rk is extracted by using the Sobel filtering to highlight the

boundary and the micro-crack interface as shown in Figure 3.7. Figure 3.7 (a) shows the Sobel convolution kernels,

𝐺𝑥 and 𝐺𝑦, consisting of a pair of 3 × 3 pixels designed to extract the horizontal and the vertical discontinuities

by calculate derivation of adjacent pixel data. By using 𝐺𝑥 and 𝐺𝑦 on Rk, the kth horizontal edge image (𝐸𝑘𝑥) and

the kth vertical edge image (𝐸𝑘𝑦

) is constructed (Figure 3.7 (b)).

24

𝐸𝑘𝑥 = 𝑅𝑘 ∗ 𝐺𝑥 (3.10)

𝐸𝑘𝑦

= 𝑅𝑘 ∗ 𝐺𝑦 (3.11)

Then the kth edge image (Ek) is constructed by summing 𝐸𝑘𝑥 and 𝐸𝑘

𝑦.

𝐸𝑘 = 𝐸𝑘𝑥 + 𝐸𝑘

𝑦 (3.12)

𝐸 = ∑𝐸𝑘

𝑛

𝑛

𝑘=1

(3.13)

Finally, the edge image (E) is constructed by averaging Eks.

(3) Denoising process for micro-crack visualization

Figure 3.8: Noise elimination based on the binary imaging.

After E is constructed, the denoising process is processed to highlight the boundary of the specimen and the

micro-cracks without the noise components. The probability density function of the pixel value of E is estimated

by fitting the normal distribution only to the non-zero pixel values. A threshold value corresponding to a 5 % of

cumulate density function (CDF) is then calculated (Figure 3.8 (a)). Based on the threshold value, the binary

image processing is conducted and the values on E larger than the threshold value is converted to 1 and otherwise

is converted to 0.

𝐹(𝑥∗, 𝑦) = { 1 if 𝐸(𝑥∗, 𝑦) ≥ Threshold 0 otherwise.

(3.14)

Then the final image (F) is constructed and the boundary of the target specimen and the micro-cracks are

highlighted without the noise components.

25

Chapter 4. Experimental Validation under Static Conditions

4.1 Experimental Setup

Figure 4.1: Lab-scale test setup of the proposed LLT system for static condition.

Figure 4.1 shows the lab-scale test setup of the proposed LLT system. First, the control computer sends out

the trigger and control signals to the CW laser (TMA-532-15T, TMA) and the CW laser emits a point laser beam

with a 4 mm of diameter and a 532 nm of wavelength. The line laser beam intensity is set to 92.31 mW/mm2 and

115.38 mW/mm2 for the CFRP and GFRP specimens, respectively. The point laser beam is transformed into the

line laser beam using the cylindrical lens. The line laser beam is focused at the surface of the target structure with

a 1.3 mm of beam width and a 100 mm of beam length through the F-theta lens. The line laser beam scans the

designated inspection region and generates thermal waves with a constant scanning speed of 20 mm/s. Note that

the laser beam intensity should be decided based on k of the target structure and the scanning speed of the line

laser beam. The corresponding thermal responses are measured in the time domain using the cooled type IR

camera (A6700SC, FLIR) triggered by the control unit. The IR camera acquires thermal images with 0.02 K of a

temperature resolution, 550 µm of a spatial resolution, 50 Hz of a sampling rate and 3 µm to 5 µm of a spectral

range. The galvano-motorized scanner and IR camera are 1000 mm apart from the target structure by considering

the focusing length of the F-theta lens.

26

4.2 Delamination Detection on the CFRP plate under Static Conditions

4.2.1 Description of the CFRP plate

The CFRP plate is used for the validation experiment as shown in Figure 4.1. The CFRP plate shown in

Figure 4.2 has a dimension of 500×500×2 mm3 and consists of 12 plies with a layup of [−45°, +45°]12T and flat-

type surface. Here, a Teflon tape with 25 mm diameter is artificially inserted between 6th and 7th layers to simulate

a hidden delamination. Here, the CFRP plate has k of 3.4 Wm-1K-1 while the Teflon tape has k of 0.25 Wm−1K−1.

Figure 4.2: CFRP plate used for the validation experiment and the corresponding inspection areas.

4.2.2 Experimental Results

The inspection is divided in two parts, i.e. inspection areas I and II. The inspection area I is sound area, and

the inspection area II contains the simulated delamination. Once the thermal images are measured, Li, N and Ai

are processed using the proposed instantaneous damage detection algorithm under static conditions as shown in

Figure 4.1. First, Li is extracted from the thermal image using the previously defined w and h, and N is constructed

by averaging Li. Here, w is defined as 198 pixels and 213 pixels on the intact and damaged cases, respectively,

and h is defined as 267 pixels based on the procedure explained in Figure 4.3. Then, Ai is constructed by

subtracting N from Li. Li, from the inspection area II in Figure4.3 (b), shows that the delamination region has

higher temperature values than the surrounding sound area due to the different k values between the intact and

delamination regions. This temperature contrast is further intensified in Ai, once the high temperature near the

laser source is eliminated by subtracting N from Li. On the other hand, no significant temperature variation is

observed in the intact case in Figure 4.3 (a).

27

Figure 4.3: Li, L and Ai images obtained from the inspection areas. (a) I and (b) II of the CFRP plate.

Figures 4.4 (a) and (b) show A constructed by summing up all the abnormal responses from Ai, while Figures

4.4 (c) and (d) display F obtained by applying the binary imaging and median filter to A. For the intact case, the

surface patterns in Figure 4.4 (a) are successfully eliminated in Figure 4.4 (c). Then, the delamination is clearly

shown in Figure 4.4 (b), and the visualization is further improved with providing the estimated size and the

location of the delamination in Figure 4.4 (d). The estimated area of delamination is 525.62 mm2 and the error is

7.13 %.

Figure 4.4: Delamination visualization using the Ai obtained from the CFRP plate. The A images obatained from

the inspection areas (a) I and (b) II, and the F images from the inspection area (c) I and (d) II.

28

4.3 Delamination Detection on the Wind Turbine Blade under Static Conditions

4.3.1 Description of the Target Wind Turbine Blade

Figure 4.5: Wind turbine blades used for the validation experiments. (a) a 10 kW GFRP wind turbine blade

and the corresponding inspection areas and (b) a 10 kW GFRP wind turbine blade and the corresponding

inspection areas.

Two different test specimens, the 10 kW and 3 MW GFRP wind turbine blade, are used for the validation

experiments as shown in Figure 4.5 (a). The 10 kW GFRP wind turbine blade in Figure 4.5 (a) has a dimension

of 3,500×500×3 mm3 and consists of 6 curved plies with a layup of [−45°, 0°, +45°]s . The Teflon tape of 10

29

mm diameter is inserted between 3rd and 4th layers to simulate the delamination. In addition, surface defects such

as surface scratch and stabbed marks from external impact, is introduced. Also, the 3 MW GFRP wind turbine

blade, which has a dimension of 56,000×2,000×20 mm3 approximately, is used for the target structure as shown

in Figure 4.5 (b). The 3 MW GFRP wind turbine blade is laminated with glass fiber plies, wood plates and resins.

Note that the details of the material compounds and the design parameters are unknown to the authors due to the

confidentiality of the manufacturer. A type I damage is presented in the 3 MW GFRP wind turbine blade as shown

in Figure 4.5 (b). To create the delamination in the 3 MW GFRP wind turbine blade, cyclic loading tests are

conducted as follow: After mounting the 3 MW GFRP wind turbine blade on a stand fixture like a horizontal

cantilever beam, dual-axis resonance fatigue tests were carried out according to international standard IEC 61400-

23: flapwise 510,000 cycles under the equivalent amplitude of 5352 kNm and the mean of 5970 kNm and edgewise

780,000 cycles under the equivalent amplitude of 4454 kNm and the mean of 0 kNm at the end of the blade root

with a frequency ranged from 0.45 to 0.7 Hz [32].

4.3.2 Experimental Results

As for the 10 kW GFRP wind turbine blade inspection, all the test setup and image processing sequences are

identical to the validation experiment for the CFRP plate. The image A shown in Figures 4.6 (a) and (b), noise

components are mainly caused by the non-uniform heating of the curved specimen, but the noise components are

successfully eliminated in Figures 4.6 (c) and (d). As for the inspection area II, delamination is highlighted in

Figure 4.6 (d). Furthermore, some surface damages, which are stabbed and scratched by the external impacts, are

also detected in F. On the other hand, no false indication of any damage is detected for the inspection area I shown

in Figure 4.6 (c). Note that the surface scratch, which are not sorted as a real damage on the industrial field, has

been eliminated in F.

In the case of the validation experiment using the 3 MW GFRP wind turbine blade, identical image

processing sequences with 10 kW GFRP wind turbine blade tests are conducted. Figures 4.7 (a) and (b) show A

and the noise components induced by non-uniform surface condition are observed, but they have successfully

eliminated on F (Figures 4.7 (c) and (d)). As for the damage case, delamination produced along the tension line

(Damage type I) are highlighted in A (Figure 4.7 (b)) and F successfully extracts delamination formation without

any false alarms (Figure 4.7 (d)).

30

Figure 4.6: Delamination visualization using the Ai obtained from the 10 kW GFRP wind turbine blade. The

A images from the inspection area (a) I and (b) II, and the F images from the inspection area (c) I and (d) II.

Figure 4.7: Delamination visualization using the Ai obtained from the 3 MW GFRP wind turbine blade. The A

images from the inspection area (a) I and (b) II, and the F images from the inspection area (c) I and (d) II.

31

4.4 Fatigue Crack Detection on the Steel Plate under Static Conditions

4.4.1 Description of the Target Steel Plate

The steel plate is used for the validation experiment as shown in Figure 4.8. The steel plate shown in Figure

4.8 has a dimension of 300×120×2 mm3 with SS400 material. A fatigue crack is created in a steel plate using a

universal testing machine (UTM). Then a 10 mm-long and 1 mm-wide initial notch is introduced to induce stress

concentration under cyclic loading. The cyclic loading ranges from 2.8 to 28 kN with a loading cycle of 10 Hz.

After 10,000 cycles, a fatigue crack is initiated from the notch tip. Figure 4.8 shows fatigue crack widths estimated

from microscopic images near the notch and crack tips after 26,000 loading cycles. The crack width is

approximately 54.702 μm at the vicinity of the notch tip and 38.667 μm at the crack tip. The crack length is

estimated to 9.5 mm.

Figure 4.8: Steel plate for the validation experiment and the microscopic images near the fatigue crack.

4.4.2 Experimental Results

In the case of the validation experiment using the steel plate, identical image processing sequences with

CFRP plate and wind turbine blade tests are conducted. Figures 4.9 (a) and (b) show A and the noise components

induced by non-uniform surface condition are observed, but they have successfully eliminated on F (Figures 4.9

(c) and (d)). As for the damage case, fatigue crack is highlighted in A (Figure 4.9 (b)) and F successfully extracts

delamination formation without any false alarms (Figure 4.9 (d)).

32

Figure 4.9: Steel plate for the validation experiment and the microscopic images near the fatigue crack.

33

Chapter 5. Experimental Validation under Dynamic Conditions

5.1 Experimental Setup

Figure 5.1: Lab-scale test setup of the proposed LLT system for dynamic condition.

The experimental set-up for the LLT system is shown in Figure 5.1. First, the control computer sends out the

trigger and control signals to the CW laser and the CW laser (TMA-532-15T, TMA) generates a point laser beam

with a wavelength of 532 nm, a beam width of 4 mm. The shape of the point laser beam is modified to the line

laser beam by the line beam generator with a top hat profile. The laser beam controller (hurrySCAN20, SCANLAB)

controls the excitation position of the line laser beam and the f-theta lens attached in front of the laser beam

controller focus the line laser beam at the excitation position with length of 30 mm, width of 1 mm and focal

length of 500 mm. Here, the intensity of the line laser beam is empirically set to 333.33 mW/mm2 for the

semiconductor chip inspection and 466.66 mW/mm2 for the steel plate inspection. Then, the target specimens pass

through the line laser beam focused position with velocity of 2 mm/s from the specimen position control jig. The

corresponding thermal waves of the target structure is recorded by the IR camera (A6700SC, FLIR) as the It. The

IR camera is trigger and controlled by the control computer and acquires It with a temperature resolution of 0.02

K, a sampling rate of 50 Hz, a spectral range of 3 µm to 5 µm and a pixel resolution of 28 μm. The IR camera is

apart 80 mm from the target specimen.

34

5.2 Micro-crack Detection on the Semiconductor Chip under Dynamic Conditions

5.2.1 Description of the Target Semiconductor Chip

The semiconductor chips used for the experimental validation is shown in Figure 5.2 (a). The dimension of

the semiconductor chip is 15 mm × 10 mm × 50 μm . The details of the material compounds and design

parameters are unknown to the authors due to the confidentiality of the producer. The damages, vertical crack and

horizontal crack, are produced during the actual wafer back-grinding process. Figure 5.2 (b) shows the

microscopic images of the micro-cracked semiconductor chip with micro-crack. The width of micro-cracks on

vertical and horizontal micro-cracked semiconductor chip are measured approximately 10 µm to 30 µm. The test

specimens are on the moving left-wise with 20 mm/s of speed attached on the specimen position control jig.

Figure 5.2: Semiconductor chip for the validation experiment and the microscopic images near the micro-crack.

5.2.2 Experimental Results

Its obtained from the IR camera after the line laser beam excitation for moving semiconductor chips are

shown in Figure 5.3 (a). Figure 5.3 (a-1,2 and 3) shows the heat blocking phenomena at the boundary of the

semiconductor chip and vertical micro-crack interface. The thermal wave propagation zone is defined as shown

in Figure 5.3 (a-2) with ra of 75 pixels, rb of 202 pixels and 𝑙 . After defining 𝑙𝑘⃗⃗⃗ , Rk is constructed after integrating

data passing through 𝑙𝑘⃗⃗⃗ and alignment of the semiconductor chips. On the Figure 5.3 (b), three semiconductor

chips are shown in Rk k=70 and two semiconductor chips show the heat blocking phenomena nearby the vertical

and horizontal micro-cracks in Rk=70. After the Sobel filtering to Rk=70, Ek=70 is constructed as shown in Figure 5.3

(c). Ek=70 shows not only the boundary of the three semiconductor chips, vertical and horizontal micro-cracks but

also the noise components.

35

Figure 5.3: Detailed image processing procedure. (a) It in the time domain: (a-1) I180, (a-2) I200 and (a-3) I220,

(b) Rk=70 from lk=70 on It in the time domain and (c) Ek=70 from Rk=70.

Figure 5.4 (a) shows E constructed by averaging the whole Eks, and shows the boundary and micro-crack

interface in the semiconductor chips. Figure 5.4 (b) shows F constructed by binary imaging on E and the micro-

crack components on E is successfully highlighted on F.

Figure 5.4: Image processing results of semiconductor chips. (a) E and (b) F.

Figure 5.4 (a) shows E constructed by averaging the whole Eks, and shows the boundary and micro-crack

interface in the semiconductor chips. Figure 5.4 (b) shows F constructed by binary imaging on E and the micro-

crack components on E is successfully highlighted on F.

36

5.3 Fatigue Crack Detection on the Steel Plate under Dynamic Conditions

5.3.1 Description of the Target Steel Plate

The steel plate used for the experimental validation is shown in Figure 5.5. The dimension of the

semiconductor chip is 300 mm × 320 mm × 2 mm . The steel plate has a fatigue micro-crack which is

generated by the universal testing machine (8801, INSTRON) and the minimum and maximum amplitude is set

to 1.4 kN and 14 kN with frequency of 10 Hz until 135,000 cycles. The fatigue micro-crack in the steel plate has

an average width of 43.8 µm. The Steel plate is moving left-wise with 20 mm/s of speed attached on the specimen

position control jig.

Figure 5.5: Steel plate for the target specimen. (a) Steel plate, (b) Microscopic image.

5.2.2 Experimental Results

Figure 5.6: Image processing results of semiconductor chips. (a) E and (b) F.

Similarily, Figure 5.6 (a) and (b) displays the E and F obtained from the steel plate. The fatigue micro-crack

is successfully highlighted on F.

37

5.4 Preliminary Study for Delamination Detection on the CFRP Blades in

Rotating Condition

The delamination detection is also required for the dynamic condition using the proposed LLT system. The

preliminary study is already done by using the rotating CFRP structure as shown in Figure 5.7. The CFRP structure

is rotating with the 261 mm/s of the tip speed and the delamination is simulated by inserting the Teflon tape (10

mm of diameter) under 0.5 mm from the target structure surface.

Figure 5.7: Rotating CFRP structure for the preliminary test.

The proposed damage detection algorithm is used to visualize damage in the rotating structure and some signal

difference is captured as shown in Figure 5.8. But the further studies are required to enhance the signal difference

between intact and delamination area, remove noise components, and detect delamination placed on deeper layer.

Figure 5.8: Preliminary image processing result using the part of the proposed damage detection algorithm. (a)

Intact CFRP plate, and (b) Damage CFRP plate

38

Chapter 6. Concluding Remark

6.1 Executive Summary

This thesis mainly focusses on the development of a line laser thermography (LLT) system for a damage detection

not only on the static condition but also on the dynamic condition. In particular, the excitation unit of the hardware

on LLT system generates thermal waves on a target structure surface and the corresponding thermal responses are

measured on the sensing unit of the hardware on LLT system. Here, the presence of the damage in the target

structure changes the propagating thermal wave phenomena, and the changes of thermal wave propagating is

extracted through the newly developed damage detection algorithm. By introducing the coordinate conversion

concept, the newly developed damage detection algorithm can also inspect the target structure on the dynamic

condition. This research can be summarized as follows.

(1) Development of the line laser thermography hardware

The complete noncontact, nondestructive, and noninvasive LLT system has been successfully developed.

The LLT system has been developed by integrating an excitation unit consist of a continuous wave (CW) laser

and a galvano-motorized scanner, and a cylindrical lens with a sensing unit consist of an infrared (IR) camera and

a focusing lens. The working principles and details of LLT system have been described in Chapter 2.

(2) Development of the damage detection algorithm

The reference-free damage detection algorithm for LLT system has been developed based on analyzing the

thermal wave propagation phenomena on the target structure. The developed algorithms offer the automated

damage diagnose without false alarm. Also, by developing the time-compressed coordinate transform (TCT)

algorithm, the damage detection for the dynamic condition could be done. The details of the damage detection

algorithms are introduced in Chapter 3.

(3) Experimental validations of the proposed techniques

The applicability of the LLT system and the damage detection algorithm has been validated from the

experiment. First, the delamination and the micro-crack detection in the target structure on the static condition have

been carried out using the carbon-fiber-reinforced polymer (CFRP), the glass-fiber-reinforced polymer (GFRP),

and the steel plate. Then, the micro-crack and fatigue crack detection in the target structure on the dynamic

condition have been carried out using the semiconductor chip and the steel plate. The details of the experimental

validation are introduced in Chapters 4 and 5. Note that the validation experiment for the delamination in the target

structure on the dynamic condition is leaved for the ongoing effort and path forward.

39

6.2 Unique Contributions

The uniqueness of the proposed LLT system in this thesis is described as follows.

(1) Damage detection both for surface and subsurface defect based on the remote inspection

In conventional laser thermography, only the surface crack on the target structure is able to detect. To inspect

the delamination using the thermography method, the lamps are widely used for the excitation source rather than

the laser. But the lamp based thermography method cannot inspect the structure in the remote condition. However,

the proposed LLT system can inspect not only surface crack but subsurface crack in the remote condition. These

achievements significantly enhance the practicality of the system for the real field applications.

(2) Large scaled structural inspection in a dynamic condition

The newly proposed damage detection algorithm includes the image reconstruction algorithm and the time-

compressed coordinate transform (TCT) algorithm. When the thermal images are obtained from the large scaled

structure or the structure in a dynamic condition, the proposed algorithms reconstruct the thermal image and

enables to evaluate the damages in the large scaled structure in the dynamic condition. It is anticipated that the

proposed system can be applicate to the various industrial facilities for online monitoring.

(3) Instantaneous damage detection and baseline-independent decision making

Conventional thermography based damage detection algorithms often require time consuming calculations.

Therefore, it is very hard to applicate the existing thermography based damage detection algorithm to the real

application. The proposed algorithm overcome the previous technical hurdle and broaden the usability.

Furthermore, the propose algorithm reduces the false-alarm for the damage detection by using the current-state

data.

6.3 Path Forward

The further research topics are listed as follows.

(1) Delamination detection in the target structure on the dynamic condition

The delamination detection is also required for the dynamic condition using the proposed LLT system. The

preliminary study is already done by using the rotating CFRP structure. Some signal difference is captured on the

delamination area compared with the intact area. But the result shows that the delamination in the deeper part

from the structure surface shows the slighter signal difference. The further studies are required to enhance the

signal difference between intact and delamination area even the delamination is placed in the deeper layer, then,

more clear damage detection results are produced without the false alarms.

40

(2) Damage quantification

The damage quantification is the significant matters in the real industrial field and estimating the damage

size and depth in the ultimate step in damage detection field. In this theses, the damage size can be estimated by

counting the pixel numbers in damaged area, but the accuracy is not verified. Also, the defect depth information

cannot have extracted by the proposed algorithm. Therefore, the tremendous efforts are needed to quantifying the

damage.

41

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43

Acknowledgments in Korean

부푼 꿈을 가지고 KAIST 정문을 들어서던 것이 엊그제 같은데 어느덧 연구실 인턴 및

석사과정을 끝맺음 지었습니다. 많이 부족한 저에게 아낌없는 조언을 주시고, 지치고 힘들 때

위로가 되어 주신 많은 분들께 본지를 빌어 감사의 말씀을 전하고자 합니다.

먼저, 올바른 연구자가 될 수 있도록 저를 지도 해 주신 지도교수님들께 감사의 말씀을

전합니다. 수행하는 모든 일에 있어 평균이 된 것에 안주하지 말고 항상 최고가 되어야 한다는

손훈 교수님의 가르침은 현실과 타협하려는 저를 항상 채찍질 해 주었으며, 덕분에 지난 3 년간

여러 방면으로 저에게 많은 발전이 있을 수 있었습니다. 교수님의 따끔한 충고와 교수님의

생활에서 보여지는 연구에 대한 열정을 보면서 나날이 저를 발전시킬 수 있었습니다. 또한,

저에게 꾸준히 힘을 실어 주시고 연구에 대한 남다른 관점을 제시 해 주신 충남대학교 윤현도

교수님 덕분에 연구에 관한 초석을 다질 수 있었고 보다 즐겁게 연구 생활에 임할 수 있었습니다.

교수님의 가르침에 부응하는 자랑스런 제자가 될 수 있도록 앞으로 더욱 노력 하겠습니다.

다음으로, 지난 3 년간 머물렀던, 그리고 앞으로 4 년간 머물 Smart Structures and Systems (SSS)

가족들에게 감사의 말씀을 전합니다. KAIST 입학 후, SSS 연구실 가족들이 대학원 생활에 있어

많은 힘이 되었습니다. 학업, 연구, 일상생활 그리고 연애 등 모든 일거수일투족을 공유하며, 생활

패턴까지도 비슷한 연구실 가족이기에, 오랜 친구 못지 않은 공감대를 형성 할 수 있었으며 힘들

때마다 많은 힘이 되어 주었습니다. 또한, 연구 및 프로젝트 진행에 있어 누구보다 큰 욕심을

보여 주었던 연구실 구성원들의 열정 덕분에, 저 또한 보다 더 큰 욕심과 포부를 가질 수 있게

된 것 같습니다. 항상 큰 버팀목이 되어 주었던 SSS 연구실 구성원들에게 감사의 마음을

표합니다.

마지막으로 어떠한 일을 하든 저를 믿어주고 지지 해 주신 부모님께 감사의 말씀을 전합니다.

자동차를 만들고 싶다고 하면 자동차를 만드는 아들이 되기를, 건물을 디자인 하고 싶다고 하면

건물을 디자인 하는 아들이 되기를, 어떤 분야에 관심이 있든 저를 응원하는 부모님 덕분에

지금의 제가 있을 수 있었던 것 같습니다. 부모님들의 전공과는 전혀 다른 전공을 선택한

아들임에도 불구하고, 항상 제가하는 연구에 대한 관심과 확신을 보여 주신 부모님 덕분에 저

또한 제 연구에 더욱 확신을 가질 수 있고 보다 더 좋은 성과를 얻을 수 있었던 것 같습니다.

부모님이 자랑스러워하는 아들인 듯이, 다른 사람들의 객관적인 판단 하에서도 자랑스러운

아들이 되도록 하겠습니다.

이제 곧 박사과정 진학이 예정되어 있으며, 연구 생활에 있어 조금 더 앞으로 나아가게

되었습니다. 항상 저를 응원 해 주시는 모든 분들께 다시 한번 감사의 말씀을 전하며, 응원 해

주시는 분들께 자랑스러운 모습으로 다시 인사 할 수 있게끔, 앞으로도 끊임없이 연구하고 노력

하도록 하겠습니다.

44

Curriculum Vitae

Personal Information

Name: Soonkyu Hwang

Place and Date of Birth: Jinju, Gyeong-Nam, South Korea on January 3, 1990

E-mail: soonkay@hotmail.com

Education

2016 August M.S., dept. of Civil and Environmental Engineering, KAIST, Daejeon, Korea

2013 August B.S., Dept. of Architectural Engineering, Chung-Nam National University, Daejeon, Korea

Research & Educational Experience

Research Assistant

- Smart Structures and Systems Laboratory, KAIST, 2013 – Present

- Optical Instrumentation Development Team, Korea Basic Science Institute (KBSI), 2016 – Present

- High Intelligent Concrete Structure Engineering Laboratory, Chung-Nam National University, 2012-2013

Teaching Assistant

- Undergraduate Research Program (URP), KAIST, Spring Semester, 2016

Internship Experiences

- Construction Process Management, Construction Field of Avison Biomedical Research Center (ABMRC),

Hanwha Engineering & Construction corp, Seoul, Korea, June-August, 2012.

Field Test Experiences

- Wind Turbine Blade Monitoring, Korea Institute of Materials Science (KIMS), Buan, Korea, June, 2015

- Triplex Monitoring, Hyundai Heavy Industries Co., Ltd., Ulsan, Korea, 2015-Present

45

Journal Publication

* The corresponding authors are underlined.

1. Soonkyu Hwang, Yun-Kyu An, Hoon Sohn, Jinyeol Yang, “Mirco-crack visualization on Dynamic-state

Structures using Line Laser Thermography”, in Preparation for NDT&E International.

2. Soonkyu Hwang, Jinyeol Yang, Yun-Kyu An, Hoon Sohn, “Visualization of Hidden Delamination in

Composite Structures using Noncontact Continuous Line Laser Scanning Thermography”, in Preparation for

Composite Part B: Engineering.

3. Jinyeol Yang, Jaemook Choi, Soonkyu Hwang, Yun-Kyu An, Hoon Sohn, “Reference-free Micro Defect

Visualization using Pulse Laser Scanning Thermography and Image Processing, Accepted for Measurement

Science and Technology, 2016.

4. Jinyeol Yang, Soonkyu Hwang, Yun-Kyu An, Kyuhang Lee, Hoon Sohn, “Multi-spot Laser Lock-in

Thermography for Real-time Imaging of Cracks in Semiconductor Chips during a Manufacturing Process”,

Journal of Materials Processing Technology, 229, pp 94-101, 2016.

5. Yun-Kyu An, Jinyeol Yang, Soonkyu Hwang, Hoon Sohn, “Line Laser Lock-in Thermography for

Instantaneous Imaging of Cracks in Semiconductor Chips”, Optics and Lasers in Engineering, 73, pp 128-

136, 2015.

Patent & Copyright

1. Hoon Sohn, Irl Kim, Byeongjin Park, Jinyeol Yang, Soonkyu Hwang, Jaemook Choi, Yeonjun Mun,

Kyeongyoon Jang, Changkyu Chung, Sungwoo Choi, “Examining apparatus and examining method there

of”, Korea Patent (10-2015-0045358).

2. Younjo Mun, Hoon Sohn, Sang-Young Kim, Yun-Kyu An, Sung-il Cho, Seung-Weon Ha, Jinyeol Yang,

Soonkyu Hwang, “Surface inspection apparatuses for semiconductor chips and method of inspecting

surfaces of semiconductor chips using same”, Korean Patent (10-2014-0008579), 2014.

46

Award

2015 August 1st Place for the Structure Health Monitoring Competition on the 8th Asia-pacific Summer

School in Smart Structures Technology, Illinois, United States.

Research Projects

2015-Present Development of an Automated Non-destructive Inspection System for Damage Detection in

Triplex Adhesive Layers: Hyundai Heavy Industries Co., Ltd. (Funded: 250,000 USD, for

09/01/2014-02/28/2017).

2014-2015 Development of Micro Crack and Chipping Inspection Equipment for a Cutting Plane of

Display Glass using Noncontact Laser Lock-in Thermography Technique: Ministry of

Science, ICT and Future Planning. (Funded 530,000 USD, for 06/01/2014-05/31/2015).

2014-2014 Development of Damage Inspection Equipment for an Electronic Device Packaging Elements

using Noncontact Laser Lock-in Thermography Technique: Korea Advanced Institute of

Science and Technology (Funded: 220,000 USD, for 06/09/2014-12/08/2014).

2014-2014 Development of Inspection Method for Delamination, Void and Surface Crack using Laser:

Samsung Electronics Co., Ltd. (Funded: 600,000 USD, for 06/09/2014-12/08/2014).

2013-2014 Development of a Noncontact Laser Thermography Technique for Instantaneous Crack

Detection in Semiconductors: Samsung Electronics Co., Ltd. (Funded: 190,000 USD, for

06/01/2013-05/31/2014).

Conference Proceedings

1. Soonkyu Hwang, Yun-Kyu An, Dong-Gun Kim, Jinyeol Yang, Hoon Sohn, “Continuous Line Laser

Thermography for Instantaneous Damage Imaging of Rotating Wind Turbine Blades”, 6th Asia-Pacific

Workshop on Structural Health Monitoring (APWSHM), Hobart, Australia, Dec 7-9, 2016 (In preparation)

47

2. Jaemook Choi, Soonkyu Hwang, Jinyeol Yang, Hoon Sohn, “Baseline-free Automated Fatigue-crack

Evaluation using Laser Scanning Thermography”, 28th KKHTCNN, Bangkok, Thailand, Nov 16-18, 2015.

3. Soonkyu Hwang, Jaemook Choi, Jinyeol Yang, Hoon Sohn, “Multi-spot laser scanning thermography for

delamination inspection in CFRP/GFRP structure”, 6th International Conference on Advances in

Experimental Structural Engineering and 11th International Workshop on Advanced Smart Materials and

Smart Structures Technology, Urbana-Champaign, US, August 1-2, 2015.

4. Soonkyu Hwang, Jaemook Choi, Jinyeol Yang, Hoon Sohn, “Pulsed Thermography using Multi-spot Laser

beams for Hidden Delamination Detection in a CFRP Plate Structure”, 2015 Annual Spring Conference of

Korean Society for Nondestructive Testing, Mokpo, Korea, May 14-15, 2015.