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Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2007 Assessment of Triboluminescent Materials for In-Situ Health Monitoring Tarik Jamel Dickens Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected]
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Florida State University Libraries
Electronic Theses, Treatises and Dissertations The Graduate School
2007
Assessment of Triboluminescent Materialsfor In-Situ Health MonitoringTarik Jamel Dickens
Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected]
THE FLORIDA STATE UNIVERSITY
COLLEGE OF ENGINEERING
ASSESSMENT OF TRIBOLUMINESCENT MATERIALS FOR IN-SITU
HEALTH MONITORING
By
TARIK J. DICKENS
A Thesis submitted to the
Department of Industrial Engineering
In partial fulfillment of the
Requirements for the degree of
Master of Science
Degree Awarded:
Spring Semester, 2007
ii
The members of this Committee approve the thesis of Tarik Jamel Dickens defended on
March 26, 2007.
_________________________
Okenwa Okoli
Professor Directing Thesis
_________________________
Zhiyong Liang
Committee Member
_________________________
James Simpson
Committee Member
Approved:
___________________________
Chuck Zhang, Chair, Department of Industrial & Manufacturing Engineering
___________________________
Ching-Jen Chen, Dean, College of Engineering
The Office of Graduate Studies has verified and approved the above named committee
members.
iii
ACKNOWLEDGEMENTS
Humbly, I would like to thank my Mother and Father for their continued support
and undying love for their son in his academic pursuits. I would also like to thank the
members of my committee Dr. Okenwa Okoli, Dr. Richard Liang, and Dr. James
Simpson, whom have guided this research in the right direction; I would also like to
extend my gratitude to Dr. Wu, Dr. Fan, Charlie Liu, and Jerry Han.
iv
TABLE OF CONTENTS
LIST OF TABLES...................................................................................................................................... VI
LIST OF FIGURES.................................................................................................................................. VII
ABSTRACT .............................................................................................................................................VIII
1 INTRODUCTION............................................................................................................................... 1
1.1 PROBLEM STATEMENT ................................................................................................................. 2 1.2 RESEARCH OBJECTIVES................................................................................................................ 3
2 LITERATURE REVIEW................................................................................................................... 5
2.1 LUMINESCENCE............................................................................................................................ 5 2.2 CRYSTALLOGRAPHY & KINETICS OF TRIBOLUMINESCENCE......................................................... 7 2.3 TRIBOLUMINESCENT INTENSITY & SPECTRAL ANALYSIS............................................................. 8 2.4 PROMISING TL SPECIMENS......................................................................................................... 11
2.4.1 Zinc Sulfide: Manganese ...................................................................................................... 11 2.5 NON-DESTRUCTIVE EVALUATION .............................................................................................. 12 2.6 IN-SITU HEALTH MONITORING................................................................................................... 13 2.7 SUMMARY .................................................................................................................................. 14
3 DEVELOPMENT OF MANUFACTURING PROCESS FOR DISPERSION ........................... 15
3.1 MATERIAL SELECTION ............................................................................................................... 15 3.1.1 Reinforcement Material and Resin System ........................................................................... 15
3.2 MANUFACTURING PROCESS ....................................................................................................... 15 3.2.1 Bag-molding Operations ...................................................................................................... 15 3.2.2 Rotational Bag-molding Operations..................................................................................... 16
3.3 DISPERSION ANALYSIS............................................................................................................... 18 3.3.1 Black Light Characterization of Dispersion ......................................................................... 19 3.3.2 Image Processing (IP) Level of Dispersion .......................................................................... 21
3.3.2.1 MATLAB M-file ....................................................................................................................... 23 3.3.3 Combinatorial Metric for Dispersion ................................................................................... 26
4 DESIGN OF EXPERIMENT ANALYSIS...................................................................................... 28
4.1 INVESTIGATION THROUGH DESIGN OF EXPERIMENTS (DOE) ..................................................... 28 4.1.1 Response Selection ............................................................................................................... 28 4.1.2 Choice of Factors ................................................................................................................. 28 4.1.3 Choice of Experiments.......................................................................................................... 29
4.2 DESIGN ANALYSIS ..................................................................................................................... 30 4.2.1 Analysis of Dispersion Metrics ............................................................................................. 31 4.2.2 System Validation and Conclusion ....................................................................................... 38
5 PARASITIC RESPONES ON PRELIMANARY MECHANICAL TESTING........................... 40
5.1 VISCOSITY.................................................................................................................................. 40 5.2 DYNAMIC MECHANICAL ANALYSIS ........................................................................................... 41 5.3 TENSILE TESTING ....................................................................................................................... 43 5.4 DENSITY..................................................................................................................................... 48
6 CONCLUSIONS ............................................................................................................................... 51
6.1 FUTURE WORK........................................................................................................................... 52
v
REFERENCES ........................................................................................................................................... 53
BIOGRAPHICAL SKETCH..................................................................................................................... 56
vi
LIST OF TABLES
TABLE 2.1 LUMINESCENCE TYPES/EXCITATION SOURCE ................................................................................. 6 TABLE 4.1 FACTORS SELECTED FOR DESIGN.................................................................................................. 29 TABLE 5.1 VISCOSITY MEASUREMENTS OF CONCENTRATION LEVELS.......................................................... 40 TABLE 5.2 DMA RESULTS VERSUS PERCENT CONCENTRATION ................................................................... 42 TABLE 5.3 TENSILE TEST RESULTS ............................................................................................................... 45 TABLE 5.4 DENSITY MEASUREMENTS ........................................................................................................... 49 TABLE 5.5 PERCENTAGE OF TL MATERIAL WEIGHT ..................................................................................... 50
vii
LIST OF FIGURES
FIGURE 1.1 SCHEMATIC OF OVERALL TL CONCEPT AND SENSORY MECHANISM ............................................ 3 FIGURE 2.1: EXCITATION TO EMISSION [11].................................................................................................... 6 FIGURE 2.2 A SCHEMATIC OF THE PIEZOELECTRIC THEORY ILLUSTRATING TL PHENOMENA UPON FRACTURE.
THE GAS DISCHARGE, D, ACCUMULATES AS OPPOSITE CHARGES COLLIDE [18] ...................................... 8 FIGURE 2.3 ELECTROMAGNETIC SPECTRUM [25] .......................................................................................... 10 FIGURE 2.4 SPECTRAL CURVE OF ZNS: MN [26] ........................................................................................... 11 FIGURE 3.1 NORMAL MOLD OPERATION ........................................................................................................ 16 FIGURE 3.2THE ROTATIONAL APPARATUS ALLOWING FOR 360 DEGREES OF ROTATIONAL SPIN IN 2
DIMENSIONS......................................................................................................................................... 17 FIGURE 3.3 UV COMPARISON UNDOPED VS. 10% DOPANT ............................................................................ 19 FIGURE 3.4 COMPARISON OF DOPED AND UNDOPED SPECIMEN...................................................................... 20 FIGURE 3.5 UV COMPARISONS TOP VS. BOTTOM SIDES .................................................................................. 21 FIGURE 3.6 (A) REGULAR SEM IMAGE (B) SILICONE (C) ZN TRACE ELEMENTS (D) SULPHIDE TRACE ELEMENTS
............................................................................................................................................................ 22 FIGURE 3.7 (A) RGB IMAGE (B) BLACK AND WHITE CONVERSION................................................................. 23 FIGURE 3.8 (A) BLACK AND WHITE IMAGE (0 AND 1). (B) NUMERICAL VALUE. (C) EUCLIDEAN DISTANCE
TRANSFORMS ....................................................................................................................................... 24 FIGURE 3.9 DISPERSION IN LAMINATE SPACE ............................................................................................... 26 FIGURE 5.1 DMA RESULTS............................................................................................................................ 43 FIGURE 5.2 UNIAXIAL TENSION .................................................................................................................... 44 FIGURE 5.3 PERCENT CONCENTRATION VS. TENSILE STRENGTH................................................................... 46 FIGURE 5.4 DOPED SPECIMENS AFTER TENSILE LOAD UNDER UV LIGHT ...................................................... 46 FIGURE 5.5 PERCENT STRAIN ALONG CONCENTRATIONS .............................................................................. 47 FIGURE 5.6 THICKNESSES VS. STRENGTH ...................................................................................................... 47 FIGURE 5.7 FABRICATION SPEEDS EFFECT ON PART THICKNESS AND TENSILE STRENGTH ........................... 48 FIGURE 5.8 STANDARD RUN ORDER OF FABRICATIONS VS. DENSITY ........................................................... 49
viii
ABSTRACT
Advanced composites which offer robust mechanical properties are being
increasingly used for structural applications in the aerospace, marine, defense and
transportation industries. However, the anisotropic nature of composite materials leaves it
susceptible to problematic failure; the development of means for detecting failure is
imperative. As design and functionality requirements of engineering structures such as
spacecraft, aircraft, naval vessels, buildings, dams, bridges and ground-based vehicles
become more complex; structural health monitoring (SHM) and damage assessment is
becoming more rigorous. Though structures involved have regular costly inspections, the
damage associated with composites in SHM systems can lead to catastrophic and
expensive failures. Industry and research have no single technique used on its own to
provide reliable results. Integrating several nondestructive evaluation (NDE) techniques
could provide a solution for real-time health monitoring. Such studies, utilizing acoustic
emission (AE), A-scans, C-scans, and laser shearography have reported considerable
success. Nevertheless, damage detection has to be reliable and cost effective.
The answer may lie with the development of SHM systems by the use of
triboluminescent crystals, as well as optical fibers embedded in the composite matrix.
These crystals react to straining or fracture by emitting light of varied luminous intensity.
Thus, a fiber-reinforced plastic (FRP) laminate doped with Triboluminescent (TL) or
Mechanoluminescent (ML) crystals, acting as health sensors to its host material, will give
an indication of crack initiation well ahead of catastrophic failure(s). The development of
an in-situ health monitoring system for safety critical structures is a viable route through
‘Triboluminescence’. Assessing the viability of a proposed structural sensor system
requires cross-linking between key areas in science and engineering.
Initial testing has shown that light can propagate through doped resins alone, as
well as doped FRP laminates. The luminous intensities relation to impact velocity adds
credence to a monitoring system that can characterize impact activity. However,
Triboluminescent crystals have high material density. In response, a two-dimensional
rotational mold was built to counteract massive settling under normal vacuum molding
processes. Micro-structural evaluations using scanning electron microscopy (SEM) and
ix
EDAX imaging have aided in demystifying particulate dispersion of TL fillers through
use of image processing.
.
1
1 INTRODUCTION
Advanced composites are increasingly being used for structural applications in the
aerospace, marine, defense and automotive industries due to their high specific strength
and stiffness [1, 2]. However, since fiber-reinforced composites (FRCs) often lack the
detailed material property data associated with metals, the development of means for
detecting failure is imperative [2]. In particular, the use of composites in safety critical
structures leads to uneasiness since the mechanical response in crash applications is not
well understood.
In composite materials, internal material failure generally initiates long before any
change in its macroscopic appearance or behavior is observed [3]. Fiber reinforced
polymer composites are generally heterogeneous on a macroscopic scale. In addition, the
individual lamina that constitute a laminated continuous fiber reinforced composite are
anisotropic. Thus, unlike metallic materials, composites have no single, similar self-
propagating crack. Metals show visible damage caused by impact mainly on the surface
of structures, while damage is hidden inside composite structures especially when
subjected to low velocity impact such as bird collisions or tool drops [1, 4]. This difficult
to detect damage may cause serious decrease in material strength [5, 6].
The following forms of internal material failure may be observed separately or
jointly in the damage zone, and may result in component failure: matrix micro-cracking,
fiber breakage, fiber separation (debonding), and delamination. Matrix cracking and
delamination are the most common damage mechanisms of low velocity impact, and are
dependent on each other [7]. Initial damage is usually by matrix micro-cracking,
followed by delamination. With delamination, the load transfer mechanism in the
composite is broken, resulting in loss of stiffness and consequential catastrophic failure
[3]. When an aircraft collides with a bird, it can cause potentially catastrophic damage
[5]. Low velocity impact particularly presents a challenge to the utilization of composites,
since at these velocities; impact damage may not be readily visible. Therefore, devising
means of intelligently sensing the onset of failure is imperative.
2
1.1 Problem Statement
Structural health monitoring (SHM) is the acquisition, validation and analysis of
technical data to facilitate life-cycle management decisions [8]. As design and
functionality requirements of engineering structures such as spacecraft, aircraft, naval
vessels, buildings, dams, bridges and ground-based vehicles become more complex;
SHM and damage assessment is becoming more rigorous.
The aerospace industry has one of the highest payoffs for SHM since damage can
lead to catastrophic (and expensive) failures, and the vehicles involved have regular
costly inspections. Currently 27% of an average aircraft's life cycle cost is spent on
inspection and repair [1]; a figure that excludes the opportunity cost associated with the
time the aircraft is terminally grounded. Most of the advances in SHM have concentrated
mainly on active vibration control and on potential aerospace and civil applications [1].
The application of localized damage detection and assessment of composite structures,
especially impact damage, is still in its infancy. Research has also shown that no single
technique used on its own provides reliable results. Integrating several nondestructive
evaluation (NDE) techniques could provide a solution for real-time health monitoring.
Such studies, utilizing acoustic emission (AE) and laser shearography have reported
considerable success. Nonetheless, damage detection has to be reliable and cost
effective.
The answer may lie with the development of damage sensing systems by the use
of mechano-tribo-luminescent crystals embedded in the composite matrix. These crystals
react to straining or fracture by emitting light of varied luminous intensity. Thus, a fiber-
reinforced laminate doped with triboluminescent (TL) or mechanoluminescent (ML)
crystals acting as health sensors to its host material and will give an indication of crack
initiation well ahead of catastrophic failure. This in-situ health monitoring system is
comprised of embedded TL/ML crystals and optical fibers for signal processing. TL/ML
sensors are based on a growing knowledge of various luminescent theories and non-
destructive sensor technology.
3
1.2 Research Objectives
Essentially, a SHM system consists of two major components: the sensory
mechanism or network used to acquire the structural response or measurement
parameters (e.g., TL/ML, optical fiber sensors, piezoelectric-based sensors, electrical
strain gauges, etc.) and data analysis, which includes signal processing, data
interpretation, and damage identification (e.g., using wavelet transform, classical Fourier
transform, genetic algorithm (GA), and neural network) [9]. This work will focus on the
methodology concerning particle dispersion.
Figure 1.1 Schematic of overall TL Concept and Sensory Mechanism
A suitable method for sensor realization is in situ damage detection. By this
method, a matrix of TL materials embedded in composites are placed in such an
orientation that the impact of the projectile will trip photonic sensors at global or local
locations. The tripping of sensors relies heavily on optical fibers receiving light due to
impact and the spread of TL fillers. Composite laminates also need to be doped at certain
concentrations to present sufficient TL and maintain performance characteristics. The
concept visually displayed in Figure 1.1, typically would be a member of any composite
structure. Primary experimentation will consist of assembling a mold that allows for
mechanical agitation of composite laminates. Moreover, the aim is to positively affect the
dispersion of the ZnS:Mn particles that now exist. Initial testing has shown light can
4
propagate through doped resins alone. The procurement of doped composites proves to
be plausible for emitting light upon impact. The luminous intensities relation to impact
velocity adds credence to a monitoring system that can characterize impact activity [10].
The major objectives of this work are:
• Development of a methodology to manufacture glass fiber reinforced composites
doped with well dispersed Triboluminescent (TL) crystals;
• Determination of the effects of TL crystals on the mechanical properties and
curing behavior of the doped laminates.
5
2 LITERATURE REVIEW
2.1 Luminescence
Luminescence takes multifarious forms, and its various types will be discussed in
this review. It has been observed that TL materials spectrally mimic other types of
luminescence. This information is useful insofar as it can aid in ascertaining excitation
mechanism of the specimen, especially since for some instances spectral similarities do
not exist. Several luminescent types are synonymous and have overlapping mechanics. A
cross reference of TL properties in conjunction with other luminescent properties can
reveal accurate truths pertaining to TL excitation.
Luminescence of any form is derived from energy accretion on a molecular
scheme. Light itself is a form of energy made up of photons and the emission of light
from a source of energy is called “luminescence.” According to Bohr's model, electrons
accompany differing energy levels around the nucleus of an atom are given action on
impact. Bohr’s model explains the nature of particles and their subsequent atoms, where
each atom is made up of protons, electrons, and neutrons. In this model the nucleus is the
ground state and it requires certain energies to rise from that ground state to a higher
energy level. Upon impact, particles absorb the necessary energy needed to ascend
energy levels from low to high. The ground state is the closest to the core, and it is the
electrons that can move up to higher energy states. The movement of these electrons
through the energy levels produces the charge required for illumination as shown in the
schematic Figure 2.1.
6
Figure 2.1: Excitation to Emission [11]
Emission of light happens when particles lose energy by descending through
lower energy levels. Described by Figure 2.1, electrons are excited (1) to exceed higher
energy levels and emit light when falling through the energy levels. Types of
luminescence can be distinguished by examining the excitation source as described in
Table 1 below.
Table 2.1 Luminescence types/Excitation source
Luminescent Type Excitation source
Cathodoluminescence Electrons
Photoluminescence (UV) Photons
Chemiluminescence Chemical reaction energy
Electroluminescence Electric field
Triboluminescence Mechanical energy
Mechanoluminescence Mechanical energy
7
2.2 Crystallography & Kinetics of Triboluminescence
The crystallography of TL materials is a conundrum of unexploited events. TL
materials have the propensity to lack a center of symmetry in their crystal structure. This
suggests the validity of a piezoelectric (charge separation) event that is partially
accountable for TL emission [12]. The privation of a mutual excitation model is only
compounded by the plethora of distinct crystal structures. These structures exhibit a range
of either centrosymmetric or asymmetric crystal configurations.
The instances of excepted piezoelectric discharge only describe the means for
asymmetric structures. The asymmetric or non-centrosymmetric crystal structures
accompany impurities within the lattice structure. Piezoelectric incidents cannot
adequately explain the TL activity of centrosymmetric crystals [13, 14]. The lattice
structures constructed with a pure elemental center ideally have no such disorders.
Evidence indicates defects do indeed play a role in centrosymmetric crystals exuding TL,
and this suggests that impact might cause creation of impurities in centrosymmetric
structures producing localized charge separation like that clarified by piezoelectric cases
[13, 15]. In actuality, all crystals display some impurities that affect the mechanical and
electrical properties of materials. The majority of the classes of molecular crystal
structure are in fact asymmetrical in geometry. Coincidentally, this is a unique correlation
considering the majority of TL materials are comprised of asymmetric structures [16]. In
addition, impurities can be introduced to a material by doping. This is called adding an
activator, where luminescence is dependent upon a certain optimum activator
concentration [17].
The chemical and physical dynamics involved in TL occurrences are not entirely
explainable by modern science. The brightest assessments declare the presence of
impurities as the main attraction of TL events. For certain, TL emission is excited upon
crystalline fracture on both the micro and macro scale. Bearing in mind the situation in a
collision, it is helpful to revisit the climatic partition on the molecular scale. Although
some materials illuminate during deformation, popular theory proposes that at minuscule
fractures TL events begin to take place.
8
Figure 2.2 A schematic of the piezoelectric theory illustrating TL phenomena upon
fracture. The gas discharge, d, accumulates as opposite charges collide [18]
When the virgin surfaces of crystals are breached, positive and negative charges
propagate along crystalline fractures. These charges shoot across the fracture region
colliding in fractions of a second. Excitation of illumination occurs via sufficient
electrical potential shown schematically in Figure 3. The mechanical stress being applied
to the material generates substantial voltages in the area of breakage. In essence, this is
the piezoelectric event that results from present crystalline impurities. As more and more
cracks start to propagate, the voltage across the surface increases until a discharge of
electromagnetic energy is released photonically. The intensity of the discharge is
dependent upon impact velocity and the rate of creation of newly fractured surfaces [18,
19]. In addition, the most sensitive and luminous TL material would be the optimal
choice for a TL matrix sensor as level of concentration means sensitivity.
2.3 Triboluminescent Intensity & Spectral Analysis
Sage [20] has reported Triboluminescent intensity has a direct linear relationship
with impact velocity. In addition, the impact velocity also shares a linear relationship
with voltage accrued during collision [21]. This is attributed to increasing crack
propagation along the surface walls of the TL specimen where discharge is occurring at a
rapid pace resulting in an abundant amount of visible light [22]. The intensity is
measured in arbitrary. Based on photometry and thermometry, light intensity can be
written as:
I = Io exp {-t/ },
Equation 1
9
where:
I = Fluorescent light intensity (arbitrary units)
Io= Initial fluorescent light intensity
t = time after excitation
τ = prompt fluorescent decay time
Prompt fluorescent decay time is the time it takes for the light intensity to decay
to e-1
(36.8%) after excitation [10]. TL phenomena itself is a time-dependent process,
where light intensity I is proportional to the rate of new surface creation. Surface energy
increases while propagation of recent fractured surfaces accumulates releasing energy in
the form of light. This can be shown by a derivation of this model.
As shown in Figure 2.1, let the excitation phase be a population (N) of particles given
enough mechanical energy to ascend energy levels. The number of excited particles (n)
over time is proportional to the population (N) present given by equation (2). The
derivation is as follows:
Ndt
dn−α Equation 2
kNdt
dN−= Equation 3
∫∫ −= kdtN
dN Equation 4
kt
eNN−= 0 Equation 5
kt
eII−= 0 Equation 6
Finally, light intensity is presented as the accumulation of photons/excited population
released as shown in equation (5) where I and Io are substituted in as the final and
original intensity of the populationequation (6) [Strange 1942]. Indeed light intensity also
can be time dependent. Assuming the rise and fall of light intensity, depends on creation
and cessation of crack propagation [23]. This is represented in equation (6).
10
kt
etI−∝ 2
Equation 7
Likewise, in a wave form light intensity can be addressed as being proportional to the
amplitude squared [24]. A wave form can be observed by measuring its wavelength.
Figure 2.3 Electromagnetic Spectrum [25]
Measuring the spectrum of light emission in contrast to a specimen’s wavelength
expresses in detail how the phenomenon was excited. A table of the electromagnetic
spectrum is displayed in Figure 2.3. It has been reported by several researchers [21, 26]
that Triboluminescent emission fall between ultra-violet and the visible spectrum as
displayed in Figure 2.4. By comparing the spectrum of light intensity to the curve of the
wavelength; other luminescent forms lend credence to the physical mechanism.
11
Figure 2.4 Spectral Curve of ZnS: Mn [26]
Most Triboluminescent materials line up with solid-state Photoluminescent spectra
indicating TL’s apparent piezoelectric influence. Any aberrations from similar spectra
could be attributed to self-absorption of the TL emission [27].
2.4 Promising TL Specimens
TL intensity is the major factor in selection of suitable TL materials. Emphasis is
placed on the amount of light emitted upon excitation and the visibility by the naked eye.
The human eye is sensitive to greenish-yellow light at a wavelength near 555 nm. TL
research to date is exploring the use of thin films as well as bulk materials.
2.4.1 Zinc Sulfide: Manganese
Zinc Sulphide (Zn S) phosphors are promising. The TL intensity of Zn S is visible
to the naked eye, and is considered by researchers to be the best alternative for TL
applications. The light intensity of Zn S can be greatly increased when doped with an
activator such as manganese (Mn) [28]. Experimentation with Zn S:Mn phosphors
produced a piezoelectric discharge of yellow fluorescence [29]. Mechanically, it is
0
100
200
300
400
500
600
700
800
500 550 600 650 700
Lig
ht
Inte
ns
ity
Wavelength (nm)
12
reported that the crystal density is 4.1 (g/ml). To date, this phosphor is said to illuminate
brighter than any other TL material.
2.5 Non-Destructive Evaluation
Composite failure has been known [3, 30] to initiate the formation and propagation
of internal micro-cracking of the matrix. These micro-cracks usually formed when impact
energy is not sufficient to result in catastrophic failure, and are not easily detected by
usual non-destructive inspection. Damage initiations in composite materials are
increasingly perplexed by various failure modes. If the laminate is compromised at any
level, the entire structure will undergo costly routine non-destructive inspections to assess
delamination. Common practice requires ineffective visual inspections, including
expensive ultra sounds, A-scans, and x-ray machines to survey areas of potential interest.
There are several computationally expensive linear and non-linear models to
describe failure in composite panels [31]. Non-destructive inspections involve searching,
repairing, and post searches to ensure compliance of the composite structure. Inspection
cost can often rise to one-third of manufacturing and operating cost [32].
One proposed method of monitoring impact damages is through active sensing
diagnosis (ASD). This requires the use of piezoelectric sensors and actuators to diagnose
waves produced by micro fractures. The waves produced are referred to as acoustic
emission waves. Acoustic emission waves can be intercepted and decoded through use of
wavelet transforms. In turn, wavelet transforms will provide the levels of damage
information needed for critical judgments. Moreover, low velocity impacts cause most
delamination in composite materials; acoustic emission analysis is a conventional method
but is too costly to implement for this application [33].
Other methods such as Lamb waves, scans, or simple visual inspections differ
insofar as they require skill of personnel and specialized equipment to carryout
inspections. In efforts to cheapen inspection costs, routine part zoning is typically applied
in lieu of scanning the entire structure [31]. This is usually at the expense of missing
possible localized damaged areas. These are impractical processes and only allow for few
13
annual inspections in industry (i.e. private and commercial airlines) due to high associate
cost. TL sensors offer the opportunity for inexpensive online inspections [34].
2.6 In-situ Health Monitoring
The development of TL sensing technology will be evaluated on criteria of
frequency of signal during projectile impact that results in fracture. With better
understanding of TL excitation mechanism, implementation of doped composites is a
main concern in the foreseeable future. The successful implementation of damage sensing
through varied luminescent phenomena depends greatly on the maneuverability
techniques employed.
A suitable method for sensor realization is in situ damage detection. By this
method, a matrix of TL materials embedded in composite materials are placed in such an
orientation that the impact of the projectile will trip sensors at any location. Composite
laminates also need to be doped at certain concentrations to present sufficient TL. There
are two fundamental ideas for TL sensor placement. One proposed method is an
interconnected matrix of optical fibers doubling as reinforcement to composites and the
initial photon sensor. The array will be manufactured using a single TL material.
However, it might be difficult to ascertain the actual location of the damage. The other
proposed method requires the use of different TL materials in different sections of the
host entity as a location scheme [20]. Each TL material produces its own color spectrum
and in turn a unique wavelength, which will be assigned to a specific area for detection.
The sensor will be fully capable of deciphering the location by exploitation of a
variegated scheme.
The creation of an optical fiber matrix coupled with a remote detector is the
desired method of choice. Great concern is placed on signal-to-noise (S/N) ratios
bombarding true TL measurement detection. The primary objective is to determine how
efficiently TL activity can be captured and guided to a local detector. One major factor
involving noise is host absorption. Sage and Bourhill [21] report light emissions from
embedded TL crystals can be aided by the procurement of polymeric optical fibers so
light will not be lost to black bodies. In essence, doping the optical fibers themselves with
14
PL materials will reduce the noise created by host absorption. PL materials will absorb
the surrounding ambient light produced by the TL crystals, thus hindering all other noise
influences.
Finally, the effects of the fiber matrix and TL crystals on the mechanical
properties associated with composite laminates have to be monitored closely. Hadzic et
al, [35] explains that caution must be exercised with use of optical fibers embedded in
composites. If the density of fibers is too high, it can affect the desired physical
performance of the laminate itself. The results of Hadzic’s experimentation indicated
medium optical fiber embedment will not degrade the mechanical properties of the host
composite.
2.7 Summary
Composites have additive value for TL implementation because crystals can be
embedded within the host laminate. The ability to incorporate TL crystals directly into
resins makes for applicable composite transfer molding processes. Through luminescent
observations, we can feasibly decipher information to use as a TL sensor. TL materials
will only be useful when stresses and strains are sensitive to the composite structure.
Damage relegated to low velocity impacts may occur unchecked and without any
indication as micro-fractures propagate in the composite laminate. In situ monitoring is a
means for detecting such cases of continual wear and tear. A sensor system of this type
will act as a global and localized detection scheme. The use of sensor detection along side
doped TL crystals will effectively present means for online monitoring of host structures.
A proposal of an in-situ damage sensor could greatly decrease the number of accidents
due to structural failures that impractical non-destructive inspections sometimes costly
overlook. To date, industry does not have adequate and cost effective measuring devices
of this nature.
15
3 DEVELOPMENT OF MANUFACTURING PROCESS
FOR DISPERSION
3.1 Material Selection
3.1.1 Reinforcement Material and Resin System
Glass fibers were used as the reinforcement in this work. Glass fiber was chosen
for the sole purpose of aiding the propagation of light that will be produced by multi-
velocity impacts carried throughout this experiment. The BGF 7781satin weave fabrics
woven in 0 and 90° directions were used.
The resin utilized in fabrication is the IVEX vinyl ester/DCPD blend infusion
resins in monomer. It is a room temperature (77°F, 25°C) thermosetting resin providing
40-60 minute standard gel-times when accelerated by approximately 2.4% MEKP Hi-
point 90 catalyst.
3.2 Manufacturing Process
3.2.1 Bag-molding Operations
Laminates containing vinyl ester resin and satin weave glass fiber are vacuumed
bagged on a flat plexi-glass mold using tacky tape and a flexible sheet (Figure 3.1). The
initial size of the samples is 304 mm long (12 in.), 304 mm (12 in.) wide. Providing a
customary sixty-percent fiber weight fraction, 250 ml of resin is mixed at room
temperature in a 250 (resin): percent concentration (filler):2%(catalyst) ratio by volume
and infused. After infusion, glass fiber reinforced composite (GFRC) laminates were
allowed to completely cure at room temperature.
16
Figure 3.1 Normal mold operation
Doping is the process of incorporating TL phosphors in resin for vacuum
infusion. The appropriate amount of resin is measured in a durable container that is
heated approximately 100°F to reduce viscosity and degas the resin of volatile air
bubbles. Low viscosity makes for relative easy infusion. Following heating, resin is
slowly introduced to the phosphors in low amounts in what is dubbed a “Kool-Aid mix”.
This ensures clumps of phosphors are severed, reducing complications that might occur
during infusion such as susceptible filtration of agglomerated particles. The mixture is
thoroughly agitated and made ready for suction.
3.2.2 Rotational Bag-molding Operations
Due to settling of the TL crystals, it was proposed to rotate the infused resin-TL
crystal-fiber system until resin cure. The rotational agitation was aimed at suspending the
TL crystals, keeping them well dispersed until they are frozen in space by the curing
process. This was implemented by use of centrifugal motion. “Centrifuge”, is a method
whereby dense solid materials are separated from less dense viscous materials. Total
separation can occur at extremely high velocities. If speeds are geared down, particle
elevating becomes more apparent. Instead of achieving total separation of particles and
resin, particles expand out from the centers of the laminates. The rotation of the outer
mold forces particles to move away from the axis of rotation. The inner mold rotating
inside the rotating outer mold elevates particles from the mid-plane out. The rotational
mechanism seen in Figure 3.2 was designed and built for these specifications.
17
Figure 3.2The rotational apparatus allowing for 360 degrees of rotational spin in 2
dimensions
The rotational mechanism was made using angle iron supports. Two Dayton DC
motors were incorporated to yield a two-dimensional 360o rotation. Fabrication processes
are denoted according to inner mold speeds, outer mold speeds, and TL doping
concentration. For example, fabrication ‘40-50-2’, denotes inner mold speed is set to 40
(9 rpm) and outer is 50 (15 rpm) respectively. The doping concentration is 2% by
volume.
The use of the rotational mechanism follows the regular doped resin infusion
methodology. Once the vacuum bagging system has been prepared, it is attached to the
rotational mechanism as seen in Figure 3.2. Doped resin infusion is then carried out.
Immediately after infusion, vacuum and infusion ports are clamped off. Rotation is then
begun and sustained until room temperature resin cure is accomplished. This takes about
two hours. Radiant heat lamps may be used to hasten cure. The laminates are post cured
at 200°C for 2 hours after de-molding.
During bag molding operations a coordinate system is established from the infusion
point. This coordinate system (Figure 3.3) will be used to characterize the dispersion at
different points in the manufactured laminate. This point system is indicative of strips
18
that will be examined by scanning electron microscopy (SEM) in the thru-thickness and
latitudinal directions for assessment of dispersion degree.
Figure 3.3 Dispersion and categorical grid in bag molding process
The preliminary experiments involved the use of natural agitation or regular
operations. Efforts by mechanical agitation were compared to natural results where it was
deemed necessary to evaluate the level of improvement in quantitative measurements.
Thus, characterization and analysis of dispersion in the fabrication process can be known.
Selecting the best agitation process will allow subsequent experimentation that includes
mechanical degradation tests. Uniform dispersion equates to having equivalent properties
throughout the entire laminate, which is the basis for this review.
3.3 Dispersion Analysis
Dispersion is characterized on a multi-level system including black light analysis
and processing of scanning electron microscopy (JSM 7401F-SEM) images throughout
the laminate space. Issues surrounding dispersion involve a multi-step process of
19
composite manufacturing. Mechanical agitation of doped resin, resin infusion, and
curing, make for a tumultuous fabrication process with inclusion of TL particles. Other
parameters include resin rheology, vacuum pressure, mold temperature, fabric thickness,
and rotational speeds. It is crucial that the composite manufacturing process allow for
even dispersion of ZnS:Mn phosphoresce crystals. It is necessary to achieve a good level
of dispersion for mechanical testing of assumable homogenous laminates which should
have equivalent specimen to specimen mechanical properties such as failure strength and
modulus. More importantly, inadequate dispersion will defeat the potential for in-situ
health monitoring.
3.3.1 Black Light Characterization of Dispersion
Black light characterization of the level of dispersion is a visible inspection of the
photoluminescent glow distinguished by observing the laminates surfaces. This is a visual
test that can be used as a metric or solely for the purpose of cross-examining doped
plates.
Figure 3.3 UV comparison undoped vs. 10% dopant
20
Figure 3.4 Comparison of doped and undoped specimen
Adequate dispersion is a relative function of crystal population. This loose
assumption can be observed by comparing the different concentrations in Figure 3.3.
Figure 3.4 clearly shows that inclusion of ZnS:Mn phosphors solely contribute to the
photoluminescent glow. The luminous color produced under UV light is very distinct. At
the lowest doping concentrations, color disparities in the photoluminescent glow are more
prominent than at the highest doping (10%) levels. In lesser concentrations darker less
radiant plates are observed. This is directly related to the population of TL crystals
present at the surfaces and sub-surfaces. The more crystals introduced to the system, the
higher the probability of dispersed ZnS:Mn crystals and crystal density throughout the
laminate.
In order to qualify the type of dispersion present in the composite matrix, a binary
(0/1) metric QT (topside) and QB (bottom-side) are attributed to the black-light testing.
The rating of 0 (fairer complexion) or 1 (more brilliant) is given by observing the
complexion on both faces. If top and bottom metrics are identical or fairly similar, then
an overall Q metric is ‘1’. Otherwise, the overall qualitative metric is ‘0’.
21
Figure 3.5 UV comparisons top vs. bottom sides
When the top sides of each laminate have a spotted and fair complexion, this
indicates lower crystal density at the surface during fabrication as in Figure 3.5. This is
due to the settling of crystals during the infusion stage, because ZnS:Mn phosphors have
in order of 3 times the magnitude in density (reported 4.1 g/cc) than the utilized vinyl
ester resin (experimentally determined 1.08 g/cc). The consistent photoluminescent glow
located on the bottom face signifies massive settling. This is opposed to the preferred
well dispersed case, where no distinction between the black light illuminations of the top
and bottom sides can be observed. The black-light level of dispersion will serve as a
macro view of particulate dispersion. The settling observed necessitated the utilization
of the rotational mechanism discussed in section 3.2.2.
3.3.2 Image Processing (IP) Level of Dispersion
Initial attempts to observe how well dispersed the TL crystals were in the resin
matrix using scanning electron microscopy proved difficult, as the crystals are not easily
distinguished from the matrix. Nonetheless, an elemental tracer known as EDAX Genesis
was used with the SEM apparatus. This system was produced for micro-particle analysis
and elemental tracing. EDS mapping post for a more reliable quantitative metric for
dispersion than black-light testing alone. The elemental images of the Triboluminescent
material ZnS can be traced by the EDS mapping system, colored in light-blue (Zn) and
22
blue (S) in Figure 3.6. The disadvantages produced during mapping are related to particle
traces and the morphology of the SEM sample. For a given SEM image the metrics
statistical power will be based on the quality of the EDS mapping system. Using these
trace images MATLAB’s image processing (IP) toolbox may be utilized to compute a
definite metric for crystal concentration and spacing of the trace elements. Figure 3.6(b)
displays the power of the EDS mapping in tracing silica glass fibers present in the
material. It is clearly shown the accuracy and control in mapping the silica (glass) fibers.
Figure 3.6 (a) regular SEM image (b) Silicone (c) Zn trace elements (d) Sulphide
trace elements
This work will utilize scanning electron microscopy (SEM) to investigate the degree
of dispersion of the phosphor crystals in the doped composite laminates. The IP toolbox
in MATLAB will be used to measure particulate dispersion. The process for selecting and
analyzing images is as follows:
1. Capture microstructure of surface to be analyzed. This should be done at a
magnification of 30× for thru-thickness measurement.
23
2. Run EDAX software for Zn and S mapping.
3. Import combined EDAX image into MATLAB directory.
4. Run dispersion M-file. M-file computes crystal image concentration, potential
clusters, mean and variation in particle spacing.
5. Repeat for other sample images.
3.3.2.1 MATLAB M-file
The M-file containing the MATLAB programming is based on the following
logic. Dispersion is a function of crystal concentration in the given image, as well as 2D
spacing of crystal centers (Figure 3.9). Assuming the morphology of a specimen under
SEM imaging, is a flat EDS map showcasing individual crystal centers. Given the image
concentration of TL crystals, the ideal spacing assuming similar sizes then becomes a
subset of dispersion.
Figure 3.7 (a) RGB image (b) Black and white conversion
The images produced by the EDAX system are in color format known as RGB
images. Through MATLAB these image can be converted into black and white.
Understanding, images converted to black and white consist of values corresponding to 0
(black) or 1 (white) in an array displayed as pixels (Figure 3.7). The concentration of TL
particles dictates the ideal spacing in the size of the image. For instance, given 25 square
objects in an image with total area of 25 pixels of size 1. The total area of the white
objects is a metric for the concentration in the present image. The concentration of
objects should not be a substitute for dispersion, because it lacks the necessary location
24
information to make an educated conjecture. Dispersing the objects evenly across the flat
plane (area equal to 25) would require 25 pixels equidistance from each other in the XY
plane (in other words an image with a 5×5 matrix). The ideal distances between each
particle in the X and Y directions would be equal to zero. Therefore the ideal X and Y
spacing is dictated by the crystal concentration or the number of crystals in the image.
Spacing can be determined in MATLAB by computing the Euclidean distance.
The Euclidean distance is simply the distance from any non-one white pixels to the
nearest white pixel as displayed in Figure 3.8. For this analysis black pixels will be
deemed as gaps between particles. The metric for the gap is an average distance in the Y
direction computed by the ideal spacing that was determined by the image concentration,
and the variability of the average Euclidean distances in Y direction. The Y direction acts
as a representation of the thru-thickness.
Figure 3.8 (a) Black and White image (0 and 1). (b) Numerical value. (c) Euclidean
distance transforms
The Euclidean distance is computed by the equation:
2
21
2
21 )()( yyxx −+− Equation 8
Where (x1, y1) are the coordinates that correspond to the nearest white non-zero pixel. In
contrast, (x2, y2) represent the coordinates of black (0) pixels. In theory, these black
pixels will be known as gaps with a given distance from the nearest white pixel or TL
particle in any 2D direction. These distances can be utilized to form a metric based on the
mean and variance of each gap. Each individual gap or 0 pixel is taken into consideration
when computing the mean and variance metrics. This is done by using the ‘bwdist’
function provided by MATLAB’s IP toolbox (Step 7). This function converts each image
25
using the Euclidean distance transform. An average and variance is computed for each
distance column signifying the gaps in the thru-thickness direction (Step 8). For example,
the average for gaps in Figure X is 3.33. Meaning the average distance of each column in
the thru-thickness direction is 3.33. Dividing this number by the size of the image in the
X direction computes to a value of 1.11. Meaning, on average there is a particle every
1.11 pixels. Normally, a value close to zero is acceptable given the image size. Likewise
a variance value is calculated to account for the inherent variability. This metric is given
main priority as it can be the most potent assessment of dispersion.
Realistically speaking characterizing potential masses that may form is useful for
detecting potential weak areas. If these foreign substances aggregate and form large
clumps, they could cause massive degradation. Clusters are calculated through use of the
‘bweuler’ function, where clusters are considered 8 interconnected objects (Step 9).
Clusterization will act as a metric for assessing agglomerates.
The methodology concerning the dispersion analysis is supplemented in the M-file
that computes the specific metrics to analyze as a response. The computations and
programming are listed in the steps below.
1. Input image of macro or micro-level of EDS mapping.
%% Open Image
sem_image = input('Put image file name: ','s');
2. Read Image RGB = imread(sem_image);
3. Convert RGB color image into Black-White image. level=.004;
BW = im2bw(RGB,level);
figure(2)
imshow(BW);
4. Compute total area of the images size. [Y,X]=size(BW);
Total_area=X*Y;
5. Compute area of all black pixels within the image. obj=bwarea(BW);
Object_area=Total_area-obj;
Object_rate =Object_area/Total_area;
6. Compute percentage of white pixels. TL_rate_dispersion = 1- Object_rate
26
7. Compute the pixel Euclidean distances. D = bwdist(BW,'euclidean');
8. Compute the average distance between pixels in X and Y directions given as
columns meanX = mean(D)/X;
varX = var(D)/X;
9. Compute the average distance between pixels in X and Y directions. meanD = mean(mean(D))
varD = mean(var(D))
Clusters = bweuler(BW)
3.3.3 Combinatorial Metric for Dispersion
Equating the levels of dispersion is a multi-level response of several
measurements that can be obtained throughout the laminate. Lateral and thru-thickness
dispersion equate to top-side and bottom-side levels of illumination by UV excitation as
shown in Figure 3.9.
Figure 3.9 Dispersion in laminate space
If the goal is to produce particular homogenous laminates, then uniform particle
dispersion is imperative. This dispersion case should be uniform in both lateral and
central measurements. Lateral and central measurements of dispersion represent the
illumination on mutual faces. This study will only account for central thru-thickness
metrics.
Thru-thickness metric (σT)
Top-side (QT -0 or 1)
Bottom-side (QB- 0 or 1)
X
Y
27
The overall metric for dispersion is classified as D. Dispersion is a function of
crystal concentration of a given SEM image, and the two-dimensional crystal spacing.
The metric D is equal to the overall qualitative metric ‘Q’ and the thru-thickness metrics
for dispersion as discussed in sections 5.2.2 and 5.2.3. Moreover, dispersion D is a
function of each individual metric.
Dispersion = D (Q, meanD, varD, C ) Equation 9
where,
Q =Overall qualitative metric where Q is ‘1’ if QT = QB.
QT = Qualitative metric for topside (0 or 1).
QB = Qualitative metric for bottom-side (0 or 1).
meanD = The average gap distance in the thru-thickness direction.
varD = The average variance of the gap distance in the thru-thickness direction.
C = Potential number of clusters in a given image.
An overall qualitative grade of ‘1’ indicates the photoluminescent glow is at its
premier brilliance when comparing faces. On the other hand, a grade ‘0’ indicates top and
bottom faces do not agree on the composite complexion. This indicates both faces show
severe signs of settling at the macro level. An ideal value for the thru-thickness metric is
the case where meanD and varD are equal to or close to 1. For DOE purposes the mean
and variance metric will be scrutinized separately without the inclusion of the above
overall metric.
28
4 DESIGN OF EXPERIMENT ANALYSIS
4.1 Investigation through Design of Experiments (DOE)
The goal of the designed experiment will incorporate several overall objectives.
Through use of a sequential approach, experimentation of the scrutinized fabrication
process of TL doped GFRC laminates will lead towards characterization, discovery, as
well as an attempt at system tuning. The objective is to find improved settings of the
fabrication process of TL doped composites for better particulate dispersion and possible
subsequent monitoring of mechanical properties. In this case the aim is to produce
specimen to specimen conformity and repeatability of manufacturing.
4.1.1 Response Selection
The response that will determine the objectives desired is based on the various
dispersion metrics. The method of fabrication involves a two-dimensional 360 degree
rotating mold to counter act dispersion. This experimental mold operation will be
evaluated on its ability to disperse TL crystals in similar fashion. The multiple responses
include the mean (meanD) and variance (varD) metrics for dispersion. The black-light
dispersion metric Q, will be used purely as a visible means for inspecting macro-
dispersion. For this study loose attention will be given towards other responses that do
not involve dispersion.
4.1.2 Choice of Factors
The factors in this experiment consist of the following quantitative factors: (1)
Inner DC motor speed (0-40/0-13 rpm), (2) Outer DC motor speed (0-50/0-15 rpm), and
(3) Percent by volume concentration (2% or 10%). The cause and effects diagram (Figure
4.1) is used to illustrate potential responses and influential design factors that might be
29
taken into consideration. The controllable design factors selected are set in the design in
Table 4.1 according to their corresponding high and low levels. The factors chosen are a
representation of the variables associated directly with particle dispersion.
Figure 4.4 Cause and Effects diagram
.
Table 4.1 Factors selected for design
Variable Name -1 +1 0
A Inner speed 0 40 20
B Outer speed 0 50 25
C Concentration (%) 2 10 6
4.1.3 Choice of Experiments
The choice of experimental design will involve a full factorial scheme. The
original design calls for 23, 8 treatment combinations (a, b, ab, c, ac, bc, abc). In addition,
three center points will be included to account for potential high variation. This is a
resolution IV design, where 2 factor interactions are aliased with main effects. This will
aide in identifying the main effects that have large influence in this model and factor
screening. The basic design is described in Table 4.2. The plus (+) and minus (-)
notations represent the high and low limits of each factor in the design. The ‘0’ notation
30
symbolizes the necessary insertion of center points. Treatment combinations are the
settings of a particular run. For example, setting all factors to low levels (-) is denoted by
(1) in table 4.2. Consequently, setting all factors at high levels (+) is denoted by ‘abc’.
Table 4.2 Design matrix
Standard Order A B C
1 -1 -1 -1 [1]
2 1 -1 -1 a
3 -1 1 -1 b
4 1 1 -1 ab
5 -1 -1 1 c
6 1 -1 1 ac
7 -1 1 1 bc
8 1 1 1 abc
9 0 0 0 *
10 0 0 0 *
11 0 0 0 *
* Design Centers- 0
4.2 Design Analysis
The main effects can be estimated by the averages of the four replicates produced
at four high levels ( yA+
) minus the average produced at low levels ( yA−
). The main
effects for A are calculated as follows [38]:
A = yA+
- yA−
= [(a + ab + ac + abc) / 4n] - [(1) + b + c + bc) / 4n] Equation 10
In similar fashion all effects can be characterized in a related equation involving
treatment combinations. In turn, these effects facilitate the building of the “Analysis of
Variance” (ANOVA) tables from which the DOE is based upon. The ANOVA tables
provide the F and p-values from which factors can be evaluated for their statistical
significance.
31
Table 4.3 features the full design parameters, including the multiple responses to be
investigated with emphasis on varD. The run order will be randomized in Design-Expert
6, to reduce effects of potential noise variables that have gone unaccounted.
Table 4.3 Full Factorial plus center points
Factors Responses
Run Inner Outer %Conc. meanD varD
1 20 25 6 15.32 82.78
2 0 50 10 15.21 75.85
3 0 50 2 26.95 252.72
4 40 50 10 22.34 149.75
5 40 0 10 12.69 56.64
6 0 0 10 13.03 54.23
7 20 25 6 11.49 45.33
8 20 25 6 12.09 60.24
9 40 0 2 27.47 246.05
10 0 0 2 28.68 247.08
11 40 50 2 16.69 87.93
4.2.1 Analysis of Dispersion Metrics
As mentioned earlier, heavy emphasis will be placed upon the variance metric
(varD) to evaluate dispersion. The metric meanD alone is heavily correlated to percent
concentration; this will undoubtedly skew the analysis. Monitoring the variability of the
spread of the distance (gaps) values between particles allows for a substantial
investigation of particulate dispersion. Variance itself is a metric of dispersion of a data
set. As to say, data sets with low variability provide more consistent values. Values
approaching zero are deemed acceptable concerning variance. As seen in Figure 4.2,
there appears to be a direct linear relationship between the mean and variation of D
metrics. Intuitively, conclusions based by either one of these metrics will behave in
similar fashion with respect to the DOE model.
32
The variation vs. the average gap
0
5
10
15
20
25
30
35
0 50 100 150 200 250 300
varD
2%
6%
10%
.
Figure 4.2 Linear Relationships between Mean and Variance
The half normal plot is a representation of factors to be assessed for relevance.
The inclusion of effects within the prediction model are determined by there distance
from the normal line (in red) depicted in Figure 4.3. Initially, effects C, BC, AC, and
ABC in order of importance are included in the model for further analysis (Figure 4.3 b).
Effects BC and AC are two factor interactions. Effect ABC is a three factor interaction
suggesting all factors interact to produce desirable responses. This requires some leniency
in accepting “model hierarchy”. Model hierarchy which adds model stability is a standard
for accepting factors in a model that are involved in higher order terms. Higher order
terms are essentially factor interactions. However, in order to improve to a realistic model
fit model hierarchy conditions will be relaxed for further analysis.
33
Figure 4.3 Half Normal Probability Plot (a) Hierarchy (b) Effects Included
Table 4.4 ANOVA Table for varD Model Fit
Accepting all factors to estimate the response of varD is not significant as stated
by analyzing the ANOVA table produced in Design-Expert 6. Initial factor screening is
necessary to achieve a model that can fit this particular systems range. Generally,
secondary screening is achieved by rule of ANOVA probabilities of F greater than 0.05.
By this criterion, all remaining estimates are worthy of inclusion (Table 4.4). The
34
statistical significance coincides with the logic involved in the real system. Factors A
(inner speed) and B (outer speed) will improve the response (meanD) when in
conjunction with factor C (concentration). Because the fit of the data has been determined
sufficient, the model can be considered as a representation of the system. The relatively
large significance of “Curvature F-value” of 51.7 indicates that interactions result in
influence of the response. This can be seen by the similar p-values for each factor
interactions which state the significance to the fitted regression model.
The model adequacy of this predicted model is within design limitations. Using
the ‘fat pencil test’, normality assumptions remain true (Figure 4.4 a). Constant variance
is represented by the cone shaped array in Figure 4.4 (b). This pattern indicates some
semblance of constant variance. In fact, these remarks echo what is observed in the R-
squared values which monitor the variability of model fitting. The reported values for R-
squared are 94.82 %, R-adjusted is 90.68 %, and predicted R-squared is 75.7 %. The
proximity of these three values gives statistical reason to support the validity of the
model. Model independency is supported by the scattered of plots in residual vs. run
order plot (Figure 4.4 c). There is no evidence of potential erroneous outliers that can
have adverse effects on model fitting (Figure 4.4 d).
35
Figure 4.4 Model Adequacy
The final model to represent the fitted data using a design of experiment approach is:
Coded units: varD = + 146.28 – 62.16* C+30.27*A*C+33.40*B*C+29.41*A*B*C Equation 10
Actual units: varD = +295.29-24.11*C+0.0107*I*C+0.0399*O*C+0.0147*I*O*C Equation 11
Where,
I = Inner mold speed
O = Outer mold speed
C = Percent Concentration
Some useful information can be learned from characterizing the model fit in coded units.
Examining the magnitudes of the estimates of the fitted regression model reveals higher
36
order terms are relatively twice the magnitude of the coefficient of C. The negative slope
from the regression model indicates the variance of dispersion decreases with increasing
Factor C (Concentration). This suggests factor C has great influence and contribution on
these key interactions. Also, the rather large intercept indicates values at different factor
settings will fall on either side of this particular value. Our predictions will be higher or
lower than our regression model intercept of 146.28.
Figure 4.5 ABC Interactions (a) Low (b) Mid (c) High Concentration
Statistically, factor C has an intermediate affect on the response varD, initial tests
have revealed different rotational behaviors at differing concentrations are not exactly
opposite influences. The interactions of A and B at high levels minimizes the average
distances between particles when factor C is limited in concentration (Figure 4.5 a).
However, at higher concentrations variability is minimized when factor B is high and
factor A is negotiable. When factor B is low, dispersion is unaffected at either
concentration level (Figure 4.5 a, c). At the center level (concentration of 6 percent) no
inference can be observed because curvature is significant (Figure 4.5 b). Further testing
is required to characterize exactly the nature of particle agitation during curing and
rotation. Depending on levels of the BC interactions will determine a given response
varD. By observing the 2 factor interaction graphs, factor C has similar influence on
factors A and B.
37
Figure 4.6 BC Interactions on varD (a) Low Factor A (b) Mid-factor A (c) High
Factor A
Analyzing the influence of factor C on interactions can be seen by the interaction
graphs in Figure 4.6 of interaction BC. The interactions of BC and AC behave in like
manner. Minimization occurs as factor C approaches its low level, and high interaction
between factor B and C ensues with factor A being held a high constant. This trend can
be viewed in Figure 4.6 a-c consecutively as factor A is held constant at low, center, and
high levels. Conversely, as factor C approaches high levels, a reduction in factor B results
in minimizing response varD. In regards to mean and variances of dispersion,
concentration (factor C) exudes a large statistical role. Logically, introducing large
quantities of phosphor crystals in the system increases the rate at which they are
dispersed. Nevertheless, settling might still result as a by product during infusion of TL
crystals.
0
100
200
300
400
500
600
700
800
900
0 50 100 150 200 250 300
varD
Clu
ste
rs 2%
6%
10%
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40
meanD
Clu
ste
rs(8
-co
nn
ecte
d)
2%
6%
10%
Figure 4.7 Similar Relationships with meanD and varD on Clustering
38
Interestingly enough, metrics for dispersion meanD and varD have like stances on 8-
neighbor clusters. At six and ten percent dopants, clusters appear to be on the decline.
Isolating the two-percent doped composites, demonstrates the opposite trend. Clusters
aggregate on an undersized incline. Examining the entirety of these relationships reveals
much variation in particle spacing can be observed while clusters decline as particles are
spaced largely apart. Clusters as a response in the DOE analysis, will be left alone as
varD is the main focal point of clarity in this investigation.
4.2.2 System Validation and Conclusion
Fabrication by two-dimensional rotation is assessed for optimality by numerical
optimization in Design-Expert 6. Setting our constraints to minimize varD and factor C
particle concentration allows for an improved fabrication process which aims to produce
adequate dispersion (Table 4.5).
Table 4.5 Optimization with Constraints
Lower Upper Lower Upper
Name Goal Limit Limit Weight Weight Importance
Inner is in range 0 40 1 1 3
Outer is in range 0 50 1 1 3
Conc Minimize 2 10 1 1 3
meanD is in range 11.49 28.69 1 1 3
varD minimize 45.33 252.72 1 1 3
Given these constraints an overall desirability of 81.3 percent aided to select the
improved settings. Inner and outer mold speeds at 40 and 50 are carried out at
approximately 2 percent doping. Numerical optimization with constraints (Table 4.6 #2)
reports values of meanD equal to 17.55 and varD equal to 115.37 (Table 4.7). In case
number 1 of Table 4.6, the doping concentration was limited. This limitation was
incorporated to minimize the parasitic weight. Point prediction estimates report a (12.61,
22.5) prediction interval for meanD. Prediction interval concerning varD is (36.55,
194.19), with ninety-five percent confidence (Table 4.7). Through means of two-
dimensional rotation at these desired settings, repeatable responses are possible.
39
Table 4.6 Selected Solutions
Number Inner Outer Conc meanD varD Desirability
1 40 50 2 17.5584 115.379 0.814
Table 4.7 Point Estimation for Validation
Response Prediction SE Mean 95% CI low
95% CI high SE Pred 95% PI low
95% PI high
meanD 17.55788 1.171203 14.5472 20.56855 1.92174 12.6178854 22.49786
varD 115.3717 18.68714 67.33488 163.4085 30.6623 36.5516652 194.1917
Overall conclusions intimate that at higher concentrations the variability in
particle spacing will decline. This is somewhat reminiscent of our logic in sections 3.3.1.
Dispersion on some level is a function of crystal concentration. The greater concentration
introduced, the higher the probability of dispersing crystals during infusion. However,
composite knowledge aims to keep the particulate concentration at a minimum to resist
parasitic weight. Moreover, statistical knowledge will require concentration to be treated
as a noise variable. This augmentation infers rotational speed as the paramount
contributor to effectively dispersing TL particles. Significantly, rotation of the outer mold
(factor B) has relative influence given a desired concentration, but not without interaction
of the inner mold rotation (factor A). Furthermore, you can achieve relatively low
variability with regards to dispersion when concentration is low and interactions AB are
at high levels. The fitted model can be improved by employing a central composite
design (CCD), which incorporates better estimates for second-order interaction models
with inclusion of axial runs. Total validation was not undertaken because of this issue
concerning model curvature at center design points, although some inferences can be
declared about the real system.
40
5 PARASITIC RESPONES ON PRELIMANARY
MECHANICAL TESTING
A major advantage of composites in structural applications is their high stiffness to
weight ratios. As such, it is essential to maintain the mechanical properties of composite
laminates in safety critical structural applications. Determining the parasitic effects of TL
crystal inclusion are therefore imperative for the viability of full-scale inclusion in
material processes. Mechanical tests on viscosity, glass transition temperature, tensile
strength, and density were undertaken to evaluate any degradation effects resulting from
the doping of TL crystals.
5.1 Viscosity
The impact of TL crystal inclusion on resin viscosity was studied. Using a
Brookfield DV-E viscometer, single point viscosity tests were conducted on TL resin
samples consisting of a range from 0 to 10 percent doping by volume. Viscosity is itself a
measurement of liquid resistance to flow. The internal friction present in the resin is
caused by a molecular attraction making this tendency to oppose the flow of resin.
Measurements were made at room temperature and were recorded in centipoises (1 cP =
1 mPa s) using a standard spindle size #2 at 100rpm. The sample resin was a blended
vinyl ester in monomer solution.
Table 5.1 Viscosity Measurements of Concentration Levels
%Conc. TL(ml) η(cP) % torque
0% 0 163 5.1
2% 13 170 5.3
3% 19.5 170 5.3
4% 26 173 5.3
5% 32.5 174 5.4
6% 39 176 5.5
10% 65 179 5.6
*650 ml blended vinyl ester.
41
Findings from measurements in Table 5.1 indicate that the inclusion of TL crystals
in liquid resin increases viscosity almost in linear fashion. At the first doping level, a 4%
increase in viscosity occurred. Subsequently, an approximate 10% viscosity increase is
observed at the highest experimental doping level. The increase in viscosity may affect
the infusion process during fabrication; possibly increasing the infusion time. Increasing
the resin temperature may reduce the viscosity, allowing for slight increases in resin flow.
The measurements in Table 5.1 agree with those tested for this type of resin system
(Brookfield, 100-200 cps). The effect of temperature on resin viscosity will be
addressed later, as well as characterizing the significance of fiber permeability on the
infusion of doped resin. Overall, the incorporation of TL materials will complicate the
processing window during fabrication.
5.2 Dynamic Mechanical Analysis
Dynamic mechanical analysis (DMA) tests were conducted on TL doped laminates
made of glass fiber and vinyl ester resin. Several experiments were carried out using a
TA instruments DMA 2980 Dynamic Mechanical Analyzer and analyzed by Universal
Analysis 2000 software. Three-point bend testing was chosen as the preferred method of
deformation. The mechanical procedure involves heating the sample over a 20-200 °C
range at a constant rate (5 °C /min) while oscillating the deformation pressure at a
constant strain and frequency (1 Hz). The amplitude is set at 20, applying a preload force
of 10 kN. Samples were cut by a water-jet machine to the specifications of 60mm ×
10mm × 3mm (l × w × h) rectangular laminates. The cumulative graphs are displayed in
Figure 15.
42
Table 5.2 DMA Results Versus Percent Concentration
% Conc. Avg. Tg(°C) E
10 108.27 20565
6 110.56 16182
5 107.99 14628
4 109.56 14447
2 118.22 14222
0 111.19 16820
The analysis gives the impression that incorporation of TL crystals (ZnS:Mn)
does indeed have an impact on the glass transition temperature. The glass transition
temperatures were derived from DMA graphs generated by curve estimators of the
Universal 2000 software package (Figure 5.1). Table 5.2 shows the condensed
measurements obtained from the tan delta and initial storage modulus. The degree of
severity shows mixed results with minimal degradation across concentrations from four
to ten percent by volume. With regards to initial storage modulus no striking conclusions
can be stated because of the specimen to specimen difference. Any conjecture might yield
that the initial modulus changes slightly. Note: Cook Composites and Polymers Co.
reports a Tg equal to 100°C at 1 Hz frequency [35]. In practice, the Tg can be greatly
enhanced during fabrication by applying variant temperatures.
43
Figure 5.1 DMA results
5.3 Tensile Testing
Tensile tests were performed in accordance with ASTM D3039 standards [36].
An MTS 858 material testing equipment was utilized in this experiment. The MTS 858
instrument applied a 2.5 kN force at a constant strain rate of 2.5 mm per minute. The
system is equipped with software which records the stresses at which strain on material is
applied. A schematic of typical tension loading is shown in Figure 5.2.
44
Figure 5.2 Uniaxial Tension
The schematic in Figure 5.2 is a depiction of a composite strip under uniaxial
tension load. This results in a stress tensor ( ) where the only stress applied is in the Y
direction. This stress tensor is illustrated in a matrix in the figure above. The graph
displayed in Figure 5.2, is a typical behavior of ductile composite structural materials.
When increasing stress is applied, the material behaves in linear elastic fashion showing
initial yielding. Beyond this point is when fiber-matrix bonds start to deteriorate. As the
stress in the Y direction continues to climb the material reaches an ultimate stress where
soon after the material fails and fractures. This is how tensile strength or stress is
obtained through mechanical testing.
45
Table 5.3 Tensile Test Results
Fabrication (a.u,a.u.,%) width (mm) t (mm)
Strength (Mpa)
Modulus (GPa) % Strain
40-0-2 25.20 2.25 366.00 16.80 7.09
0-0-2 25.53 2.18 432.00 18.00 8.19
0-50-2 25.25 2.60 254.00 14.80 7.01
40-50-2 25.18 2.32 300.00 17.10 6.54
40-0-10 25.11 2.45 278.50 14.30 6.30
0-0-10 25.03 2.22 351.50 15.65 6.93
0-50-10 25.28 2.56 249.00 15.30 6.52
40-50-10 25.13 2.40 344.50 15.90 9.30
20-25-6-1 25.14 2.45 350.00 16.15 8.10
20-25-6-2 25.20 2.56 313.50 14.85 7.78
20-25-6-3 25.36 2.41 303.50 17.90 6.39
Control 25.23 2.53 368.50 14.95 9.07
Table 5.3 is the results of the tensile tests conducted on the laminates fabricated
for the DOE. Each fabrication process has its own denotation following inner mold
speed, outer mold speed, and percent doping concentration (e.g. 40-50-2). The desire is to
express the trend that might be visible by comparing each different fabrication process.
Inside the DOE there exist nine different fabrication processes to study, as well as the
control sample. Figure 5.3 is a representation of the variation across fabrication processes
that are interlinked to the corresponding doping concentrations. This plot shows evidence
for degradation in tensile strength compared to the control sample, where doping
concentration is null. The fabrication processes convolute any conjectures. The rotational
speeds could have implications not known at this time. With inclusion of TL materials
essentially acting as composite fillers should add some increase in tensile strengths.
Validity from this perspective will be introduced in further research.
46
0
100
200
300
400
500
0 5 10 15
Percent Concentration
Ten
sile S
tren
gth
(M
Pa)
10%
6%
2%
Control
Figure 5.3 Percent Concentration vs. Tensile Strength
The results of MTS tensile testing at levels dictated by the related DOE, infer a
reduction in the Tensile Strength with TL doping (Table 5.3). The increased strength with
doping concentration observed in Figure 5.6 may be due to the interaction of the fiber-
matrix-crystal interface. Typical modes of failure can be observed in Figure 5.4. Most
failure occurred by explosive splitting or breakage perpendicular to loading.
Figure 5.4 Doped specimens after tensile load under UV light
47
0
50
100
150
200
250
300
350
400
450
0 5 10 15
Run Order
Ten
sile S
tren
gth
(M
Pa)
20-25-6
0-50-10
0-50-2
40-50-10
40-0-10
0-0-10
20-25-6
20-25-6
40-0-2
0-0-2
40-50-2
C l
Figure 5.5 Percent Strain along Concentrations
Figure 5.5 is a graph of the DOE fabrication parameter settings versus the
corresponding tensile strengths displayed in uni-axial tension test. The graph shows
modest drop off in tensile strength from the norm depicted as the yellow triangle above.
The average tensile strength at the control level is 368.5 MPa. The minimum and
maximum tensile strengths of doped specimens observed are 388 and 247 MPa
respectively. This is an indication that TL inclusion and its dispersive state has potential
adverse affects on composite mechanical properties.
Thickness vs. Strength
0
50
100
150
200
250
300
350
400
450
500
2 2.2 2.4 2.6 2.8
t ( mm)
2%
6%
10%
Control
Figure 5.6 Thicknesses vs. Strength
48
High variations between fabrication processes that require identical concentration
levels can be deduced from Figures 5.6 and 5.7. These graphs plot the laminate thickness
in comparison to the tensile strength with respect to fabrication process. There exists high
variation in part thickness between fabrications with 2 and 10% dopant. Tensile strength
also varies along this path.
0
100
200
300
400
500
2.1 2.2 2.3 2.4 2.5 2.6 2.7
t (mm)
Ten
sile S
tren
gth
(M
Pa)
40-0
40-50
0-0
0-50
20-25
Control
Figure 5.7 Fabrication Speeds Effect on Part Thickness and Tensile Strength
In each case where fabrication speeds were identical and concentrations varied, a
slight increase in tensile strength at 2% is observed. On one occasion it was observed that
at process at speed of zero and 2% has higher tensile strength. Overall, lesser
concentrations have lower affect on Tensile strength.
5.4 Density
The practicality of in-situ health monitoring using TL sensors is the reduction or
elimination of parasitic weight. As such, it was critical to determine if any parasitic
loading was incurred by doping the composite laminates with the TL crystals. The
densities of the doped composites were measured to determine the extent to which (if
any) the TL crystals added parasitic weight to the laminates.
Density measurements were carried out using the Mettler Toledo XS104 Analytical
Balance. Several samples from each laminate representing different fabrication processes
are compared in Figure 5.8.
49
1.6
1.65
1.7
1.75
1.8
1.85
0 2 4 6 8 10 12
Run Order
Den
sit
y (
g/m
l)
20/25/6
0/50/10
0/50/2
40/50/10
40/0/10
0/0/10
20/25/6
20/25/6
40/0/2
0/0/2
40/50/2
Control
Figure 5.8 Standard Run Order of Fabrications vs. Density
Figure 5.8 is the graph of the fabrication processes versus the density
measurements taken from each laminate. Colored points that differ vertically are an
illustration of the variation in densities of the resulting fabrication. The undoped
controlled composite, fabricated at 40/50/0 shows minute variation ranging from 1.689-
1.695. Other processes demonstrate acceptable densities throughout. These are subtle
indications that the various fabrication processes involving rotational agitation produce
transverse density properties. The control measurement for density is 1.695 and 1.692
(g/ml).
Table 5.4 Density Measurements
The average percentage of TL material weight in all processes is 2.4 percent
parasitic contribution. The range extends from 1 to 4 percent of the composite weight.
The weight fraction of fiber produced is near the standard 60 percent fiber fractions most
Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Run 11 Control
Sample 1 1.697 1.640 1.641 1.661 1.761 1.828 1.720 1.740 1.759 1.791 1.773 1.695
Sample 2 1.638 1.731 1.652 1.659 1.762 1.823 1.719 1.737 1.760 1.791 1.771 1.689
Avg. 1.667 1.686 1.646 1.660 1.762 1.825 1.720 1.739 1.760 1.791 1.772 1.692
50
composite material experience in optimum manufacturing procedures. Here in table 5.5
we can view the parasitic weight contributions and fiber weight fraction.
Table 5.5 Percentage of TL Material Weight
Run Inner Outer %Conc. TLwt. Wf
1 20 25 6 0.025 0.575
2 0 50 10 0.043 0.568
3 0 50 2 0.007 0.568
4 40 50 10 0.035 0.646
5 40 0 10 0.043 0.564
6 0 0 10 0.030 *0.692
7 20 25 6 0.025 0.568
8 20 25 6 0.025 0.567
9 40 0 2 0.008 0.581
10 0 0 2 0.007 0.620
11 40 50 2 0.007 0.614 * Anomaly
51
6 CONCLUSIONS
SHM systems are predicated on costly incorporations of various sensing
components. Health-sensing by means of TL materials has the potential for cost
reduction, and creating the ability for in-situ damage sensing for composite structural
components. The key factors that will introduce TL materials as a viable route is the
interaction of TL particles and mechanical property affect.
The effect on mechanical properties mainly focuses on the composite tensile
strength as the standard for the mechanical performance measure. TL materials are a
heavy earth metals, and can have extreme adverse effects on the amalgamation of the
other constituents that create the unique advantages of composites. Some level of
degradation is expected as TL concentration increases. The percentage of TL material can
be limited in its weight contribution, but a significant rise in density is observed.
An optimal manufacturing methodology has been developed for the glass
fiber/vinyl ester/TL reinforced composites. Fabrication by rotational molding improves
the dispersion of TL particles in glass-fiber composite laminates. The speeds of the inner
and outer molds apply a low-level centrifuge between the high TL material density and
the vinyl ester resin. This mechanical agitation method demonstrates the ability to
produce TL doped composite laminates with adequate dispersion.
Through image processing of SEM-EDS particle analysis images, a level of
particulate dispersion has been explored and developed. Through a DOE investigation we
are able to infer that dispersion of TL particles can be improved by rotational agitation in
a vacuum bag molding processes. Statistically, low concentration levels and interactions
of mold control speeds set at high levels respectively produce adequate dispersive
properties. This meets the initial objective of composite engineering, where foreign
inclusion is minimized reducing parasitic weight. Experimentally, our efforts are focused
on the thru-thickness dispersion of particles throughout the laminate space which is
attained by several metrics. Those metrics include mean and variance estimations of gaps
that exist between particles in an EDS traced image. Investigation through DOE gives
credence to dispersion analysis by means of EDS mapping and Image processing
methodology.
52
6.1 Future Work
The aforementioned study conducted was limited in its capabilities. Future work will
be completed to realize greater research objectives. The objectives involve complete
evaluations of the parasitic properties and interactions of TL materials of the initial multi-
scale composites. Studies that entail tensile and flexural properties will be of use for
material design. These properties will report on the viability of incorporating TL
materials in critical safety structures. Other statistical means will be applied to further
gain information on the integrated system of TL materials.
Improvement of the two-dimensional rotational mold will be carried out this
summer. During this study the ‘vacuum pressure’, a common factor in composite
technology was negated because it was not directly involved in the dispersion process.
Adding th
e ability to infuse while rotating may offer more dispersion benefits.
Knowing dispersion is a multi-level event involving multiple fabrication factors; a
continuation for a more accurate quantitative metric should continually be updated.
Further progress in this area is paramount to fitting the system with precision. EDS
mapping and computational image processing offer wide possibilities that need further
observance.
Optical fiber incorporation during fabrication in an optimal desired orientation in the
resin matrix will be assessed for its parasitic contributions. Finally, experiments
invloving the interception of light emission from multi-velocity impacts will be
intterogated to assess structural health. This research effort is the continuation of building
a database of applicable sensing measures through use of TL resources and optical fiber
embedment. The main ambition is to create a central nervous system for which composite
structures can assess its own health.
53
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56
BIOGRAPHICAL SKETCH
The author was born and raised here in sunny Florida. He is the youngest addition
to the union of Frank and Mary Dickens. Before coming to Tallahassee in 1992, the
author’s father served twenty years in the Navy. During his residency in Tallahassee he
attended the areas public schools. Before entering Florida State University’s
baccalaureate program, he was a student attending Tallahassee Community College
where he received his Associate in Arts degree.
Currently, the author is conducting research to secure his master’s degree in
Industrial Engineering specializing in Composite Engineering.
