Surface Morphology Analysis using Atomic Force Microscopy and
Statistical Method for Glass Fiber Reinforced EpoxyZinc Oxide Nanocomposites
Anupama Hiremath,^{1} Sridhar Thipperudrappa^{1 }and Ritesh Bhat^{1,*}
^{ }
^{1}Department
of Mechanical and Industrial Engineering, Manipal Institute of Technology,
Manipal Academy of Higher Education, Manipal, 576104, India.
*Email: ritesh.bhat@manipal.edu (R. Bhat)
Abstract
Table
of Contents
Keywords: Zinc oxide; Polymer
nanocomposites; Surface roughness; Atomic force microscopy; Analysis of variance.
1. Introduction
Combining two or more raw materials to make a unique
composition that can be used as an intermediate or a final product has been
around for a long time and has a wide range of applications.^{[1]} However, in order to make a formulation
that works well and meets a user's needs, it is important to know about the
material properties and the processing parameters that must be used when mixing
and/or combining different ingredients that are either naturally occurring or
made synthetically, and are often incompatible with each other.^{[2,3]} The final product comprises different
phases that cannot be mixed. Such formulations appear to be homogeneous
macroscopically, but they are very different microscopically. In another type
of formulation, two or more components are mixed to make a new material with
better qualities than the parts that make up the composite. Polymer composites,
because they can be made to have different strengths and weights, are now used
in a wide range of applications, from earth to space. Polymer composites can be
made by adding different types of reinforcements. This is a good way to get
around the limitations of the polymer material.^{[4–6]}
Reinforcements in the form of continuous fibers are thought to be a good way to
transfer the loads and stresses from the weaker polymer matrix to the stiffer
fibers. Glass fibers are used a lot to make polymer composites because they
have good mechanical properties at a good price.^{[7]} It has become more common for nanotechnology to be
used in materials science as it has opened up a wide range of highperformance
materials.^{[8–13]} Polymer
nanocomposites are a new engineering and functional category of materials made
by adding nanoparticles as fillers to different types of polymer matrices.
However, it is important to note that they can achieve these superior
properties only if the materials are processed with extreme care and control.
Also, the final material properties of the polymer nanocomposite largely depend
on how well the chosen nanofillers are spread out in the matrix.^{[14]} Ultrasonication
is the best way to ensure the nanofillers are evenly distributed in the matrix.^{[15–17]} The ultrasonication technique begins
with the addition of nanoparticles to the polymeric resin, followed by the
vigorous stirring of the solution using an ultrasonicvibrating sonicator
probe. The critical parameter that can affect the final material property in
this process is the sonication time. This is because when the sonication period
increases, the resin's temperature and viscosity tend to increase as well.^{[18]} These circumstances may present
difficulties during composite production, particularly if wet processing is
used. As a result, it is critical to carefully set the sonication period to
guarantee that the changed resin can be handled easily during the fabrication
process. A significant disadvantage of polymer matrix composites is their low
heat stability. Thus, it is critical to understand the glass transition
temperature of the polymeric resin since it indicates the point at which the
polymeric matrix loses its stiffness and becomes soft/rubbery. In the case of
thermosets, the resin becomes hard only after the curing process is complete
due to the formation of strong covalent connections.^{[19]} Thus, in
the case of the thermoset matrix, the curing period is a critical factor in
determining the final composite characteristics. Hard ceramic nanoparticles are
added as fillers to compensate for the polymeric resins' lowtemperature
stability. While these inclusions are advantageous in some ways, they often
result in a rough and hard surface on the composite materials. The roughness of
the surface becomes a key factor in a variety of technical applications and
must therefore be properly controlled and maintained within specified limits.
In this study, hard ceramic ZnO nanofiller is added to the epoxy resin in
different amounts to make glass fiber reinforced epoxyzinc oxide
nanocomposites. The atomic force microscopy (AFM) analysis is used to figure
out the different features of the surface topography. Also, analysis of
variance (ANOVA) and regression analysis are conducted to see if there is a
difference in the average values and to create mathematical models that can
predict surface roughness parameters. Fig. 1 represents
the present study pictographically and has been explained in detail in the
further sections of this article.
Fig. 1 Schematic representation detailing the
present study.
2.
Materials and method
2.1
Materials
The matrix material used is a colorless, bisphenolA
type unmodified epoxy resin with a medium viscosity commercially known as
Araldite LY556. A suitable curing agent known as Aradur HY951 is used to cure
the resin. The unidirectional, woven roving mats of Eglass fibers are used as
reinforcement. ZnO nanoparticles with an average particle size of 40 nm are
utilized as nanofillers.
2.2 Fabrication of EGZ nanocomposites
Six square Eglass mats with a length of 300 mm and
a width of 300 mm are cut from the roving in the first phase to create a 3 mm
thick composite laminate. These square cutout mats are weighed using an
electronic balance. The obtained fiber weight is maintained consistently across
all the fabricated composite laminates. ZnO nanoparticles are introduced into
the resin at varying concentrations ranging from 1% to 3%. The weight fraction
of reinforcement and matrix/matrixfiller is maintained as 50/50. As a result,
the resin and filler weights are altered as specified in Table 1. As specified by the vendor, the resin to
curing agent ratio should be maintained at 10:1. The appropriate amount of
resin is placed in a glass beaker, and the required amount of ZnO nanoparticles
is added at the specified weight percent. The ultrasonication process is used
to blend the nanofillers into the epoxy resin,^{[20]}
in which the solution of resin and filler is stirred using ultrasonic sound
waves at a frequency of 20 kHz delivered via a probe. Following sonication, the
needed amount of curing agent is added to the resin/filler solution and
composite laminates. ZnO nanoparticles are introduced into the resin at varying
concentrations ranging from 1% to 3%. The weight fraction of reinforcement and
matrix/matrixfiller is maintained as 50/50. As a result, the resin and filler
weights are altered as specified in Table 1. As
specified by the vendor, the resin to curing agent ratio should be maintained
at 10:1. The appropriate amount of resin is placed in a glass beaker, and the
required amount of ZnO nanoparticles is added at the specified weight percent.
The ultrasonication process is used to blend the nanofillers into the epoxy
resin,^{[20]} in which the solution of
resin and filler is stirred using ultrasonic sound waves at a frequency of 20
kHz delivered via a probe. Following sonication, the needed amount of curing
agent is added to the resin/filler solution and manually mixed using a wooden spatula.
The first square glass fiber mat is placed on a 1 mm
thick Teflon sheet, and the resin/filler/curing agent solution is dispersed
with a brush. The second square fiber mat is positioned on top of the first. As
with the first mat, the resin/filler/curing agent solution is evenly spread
throughout the second mat. This is followed by using a handroller to remove
any excess resin solution between the two mats gently. Similarly, all remaining
mats with a uniform layer of resin/filler/curing agent solution are stacked one
over the other with 0° fiber orientation. On top of the previous stacked mat,
another Teflon sheet with a thickness of 1 mm is applied. The stacked laminates
are compression molded at 100°C for varying lengths of time.
The laminate and the Teflon sheets are placed on the
compression molding machine's mold die and squeezed at 20 bar pressure. The
laminates are removed from the compression molding machine and allowed to cure
at room temperature fully. Table 1 shows how
the various composite laminates manufactured in this work are coded for ease of
identification.
Table 1. Composite
sample coding concerning the composition of glass fiber reinforced epoxyzinc
oxide (EGZ) nanocomposites.
Composite
coding 
Glass fibers (g) 
Epoxy LY556
(g) 
ZnO Nanofiller
(g) 
EGZ1 
253 
250.47 
2.53 
EGZ2 
253 
247.94 
5.06 
EGZ3 
253 
245.41 
7.59 
2.3 Experimental details
It is determined that the polymerfiller matrix has
a significant effect on the roughness properties of the polymer
nanocomposite materials developed with them.^{[21]}
There are numerous ways of nano emulsification available to produce
polymerfiller matrixes, which are broadly classified as high
and lowenergy methods. Ultrasonication, amongst them – a high energy
approach, is widely applied due to its operational advantages.^{[22]} Moreover, it is an
efficient technique for creating nanoemulsions that allow precise control
of the emulsion properties. It can be used to create a nanoemulsion in situ
or to minimize the size of an emulsion that has already been prepared.^{[23]} The present work analyzes two critical
parameters involved in the preparation of EGZ nanocomposites: sonication time,
which is involved in the formation of the polymerfiller matrix, and
compression time, which is involved in the overall formation of the EGZ
laminate and evaluates their effect on the surface roughness of the EGZ
nanocomposites created using the developed polymerfiller matrix. Three
different fixed conditions are considered as given in Table
2. All the composite sample types listed in Table
1 are subjected to all the conditions listed in Table
2.
Table 2. Fixed
conditions of predictor variables for experimental purpose.
Condition
Number 
Sonication
time (minutes) 
Compression
time (minutes) 
1 
10 
5 
2 
15 
10 
3 
20 
15 
2.4 Surface roughness evaluation
Surface roughness assessment is useful for several basic
material properties such as friction, contact deformation, heat and electric
current conduction, contact joint tightness, and positional accuracy. As a
result, surface roughness evaluation has been the focus of theoretical and
empirical studies for many years. The geometry of the real surface is
sufficiently complex that a finite set of parameters cannot offer a complete
description. A more precise description can be obtained by increasing the
number of parameters used for its evaluation. Surface roughness parameters
are often classified into three types based on their functionality. These are
divided into three categories: amplitude parameters, spacing parameters, and
hybrid parameters.^{[24]} Thus, the
current work focuses on determining the surface roughness using three amplitude
parameters: arithmetic average height (R_{a}), root mean square
roughness (R_{q}), and profile maximum height (R_{t}). Atomic
force microscopy (AFM) is a wellknown technique for analyzing the surfaces of
micro/nanostructured coatings.^{[25]}
AFM is an effective technique for characterizing nanoparticles and
nanomaterials because it offers qualitative and quantitative information on
several physical properties such as size, shape, surface texture, and
roughness.^{[26]} Additionally, it is a
technology capable of imaging nearly any surface type, including polymers,
ceramics, composites, glass, and biological materials.^{[27]}
Thus, the surface topographical studies are carried
out in the present study through atomic force microscopy (AFM) to determine the
impact of nanofillers on the surface characteristics of the fabricated EGZ
nanocomposites. The AFM images of the EGZ nanocomposites are obtained, and the
surface roughness amplitude functional parameters R_{a}, R_{q},
and R_{t} are measured using Equations 1,
2, and 3,
respectively.
(1)
(2)
(3)
2.5 ANOVA analysis and regression
modelling
Analysis of variance (ANOVA) is a conceptually straightforward,
effective, and widely used technique for performing statistical testing on
studies involving two or more groups.^{[28]}
Since the present work comprises more than two groups^{[29]} in terms
of conditions mentioned earlier in Table 2, the
ANOVA test is used to investigate the significant differences among the group
averages.^{[30]} Regression analysis is
a popular statistical learning technique used for determining the relationship
between a dependent variable Y and p independent variables X = [X_{1}...X_{p}].
The variables X_{k} (k = 1,...,p) are known as predictors, explanatory
variables, or covariates, while the dependent variable Y is also known as the
response variable or outcome.^{[31]} In
a nutshell, it is the study of how one or more predictors influence a response
variable.^{[32]} In the present work,
the ‘conditions’ as mentioned earlier comprising the combination of sonication
time and compression time is considered as a categorical pooled factor, and
filler weight percentage is taken as the continuous predictor. Using ANOVA, the
significance of filler material addition and the condition is checked for the
amplitude functional factors of surface roughness. Based on the significance
and the contribution of the selected factors, regression analysis is further
employed to develop the predicting mathematical model. The model is later
validated using the additional set of experiments.
3.
Results and discussion
3.1
AFM results
Fig. 2 shows
threedimensional AFM images of EGZ nanocomposites. Equations 1, 2, and 3 are
used to calculate the surface roughness parameters of the fabricated EGZ
nanocomposites. The needed amplitude functional surface roughness values for
manufactured EGZ nanocomposites are listed in Table 3 for
varied sonication and compression times.
Fig. 2 AFM 3D
scans of EGZ nanocomposites: (a) condition 1 – sonication time of 10 min and
compression time of 5 min; (b) condition 2 – sonication time of 15 min and compression
time of 10 min; (c) condition 3 – sonication time of 20 min and compression
time of 15 min.
Table 3. Amplitude functional parameters of surface
roughness measured for EGZ nanocomposites.
ZnO Weight% in the polymerfiller matrix 
Sonication time St (min) 
Compression time Ct (min) 
Arithmetic average roughness, Ra (µm) 
Root mean square, Rq (µm) 
Maximum height of the profile, Rt (µm) 
1 
10 
5 
47.4 
67.1 
874 
2 
10 
5 
92.9 
126 
1517 
3 
10 
5 
119 
134 
1726 
1 
15 
10 
49 
72 
900 
2 
15 
10 
98 
135 
1573 
3 
15 
10 
123 
153 
1811 
1 
20 
15 
45 
82 
822 
2 
20 
15 
90 
150 
1445 
3 
20 
15 
100 
116 
1500 
3.2 Results of ANOVA and regression
analysis
The analysis of variance results for the arithmetic average
roughness, root mean square, and a maximum profile height of EGZ nanocomposites
under all three conditions are shown in Tables 4,
5, and 6. This
analysis was conducted at a 5% level of significance, which corresponds to a
95% confidence level. The fourth column of the tables provides each factor's
percentage contribution (P) to the overall variation, showing its degree of
influence on the outcome.
3.3 Mathematical modeling for R_{a}, R_{q,}
and R_{t }
Equations 4, 5, and 6 in Table 7 provide the mathematical relationship between
the arithmetic average roughness and the nanofiller weight percentage utilized
in the polymerfiller matrix.
Equations 7, 8, and 9 in Table 8 provide
the mathematical relationship between the root mean square and the nanofiller
weight percentage utilized in the polymerfiller matrix:
Equations 10, 11, and 12 in Table 9 provide
the mathematical relationship between the maximum profile height and the
nanofiller weight percentage utilized in the polymerfiller matrix:
Table 4. ANOVA results for arithmetic
average roughness of EGZ nanocomposites using AFM analysis.
Source 
DF 
Seq SS 
Contribution 
Adj SS 
Adj MS 
FValue 
PValue 
Regression 
3 
6485.27 
99.90% 
6485.27 
2161.76 
1626.20 
0.000 
F% 
1 
6246.83 
96.22% 
6246.83 
6246.83 
4699.22 
0.000 
Condition 
2 
238.44 
3.67% 
238.44 
119.22 
89.68 
0.000 
Error 
5 
6.65 
0.10% 
6.65 
1.33 


Total 
8 
6491.92 
100.00% 




Table 5. ANOVA
results for root mean square of EGZ nanocomposites using AFM analysis.
Source 
DF 
Seq SS 
Contribution 
Adj SS 
Adj MS 
FValue 
PValue 
Regression 
3 
9715.00 
99.99% 
9715.00 
3238.33 
26763.10 
0.000 
F% 
1 
8809.00 
90.67% 
8809.00 
8809.00 
72801.67 
0.000 
Condition 
2 
906.00 
9.33% 
906.00 
453.00 
3743.81 
0.000 
Error 
5 
0.61 
0.01% 
0.61 
0.12 


Total 
8 
9715.61 
100.00% 




Table 6. ANOVA
results for maximum profile height of EGZ
nanocomposites using AFM analysis.
Source 
DF 
Seq SS 
Contribution 
Adj SS 
Adj MS 
FValue 
PValue 
Regression 
3 
953522 
99.98% 
953522 
317841 
7163.95 
0.000 
F% 
1 
929053 
97.41% 
929053 
929053 
20940.35 
0.000 
Condition 
2 
24469 
2.57% 
24469 
12234 
275.76 
0.000 
Error 
5 
222 
0.02% 
222 
44 


Total 
8 
953744 
100.00% 




Table 7. Mathematical
model for determining arithmetic average roughness of EGZ nanocomposites for a
given filler weight content at varying sonication and compression time.
Condition 
Mathematical
Model 
Equation
Number 
1 – Sonication
time – 10 min; Compression time – 5 min 

(4) 
2 – Sonication
time – 15 min; Compression time – 10 min 

(5) 
3 – Sonication
time – 20 min; Compression time – 15 min 

(6) 
Table 8. Mathematical model for determining root
mean square value of EGZ nanocomposites for a given filler weight content at
varying sonication and compression time.
Condition 
Mathematical
Model 
Equation Number 
1 – Sonication
time – 10 min; Compression time – 5 min 

(7) 
2 – Sonication
time – 15 min; Compression time – 10 min 

(8) 
3 – Sonication
time – 20 min; Compression time – 15 min 

(9) 
Table 9. Mathematical
model for determining maximum profile height of EGZ nanocomposites for a given
filler weight content at varying sonication and compression time.
Condition 
Mathematical
Model 
Equation
Number 
1 – Sonication
time – 10 min; Compression time – 5 min 

(10) 
2 – Sonication
time – 15 min; Compression time – 10 min 

(11) 
3 – Sonication
time – 20 min; Compression time – 15 min 

(12) 
3.4 Analysis from AFM results
3.4.1 Effect of filler content sonicated
to the matrix
The AFM analysis results in Table
2 indicate that the amplitude functional parameters of surface roughness
are influenced by the nanofiller (ZnO) loading and that these parameters
increase as the ZnO weight percent in the epoxy matrix increases. The 3D AFM
scans in Fig. 2 demonstrate that with ZnO
nanofiller loadings of 1% and 2%, the fillers are more distributed evenly
inside the matrix with less clumping. However, when the nanofiller loading is
increased to 3%, the nanofillers agglomerate significantly, and there is no
uniform dispersion of the nanofiller within the matrix, as illustrated in Fig. 2(c). This can be linked to the nanofiller's
cluster growth mechanism within the matrix.^{[33]}
Nanoparticles cluster or aggregate into an agglomeration due to their large
surface area and strong physical forces of attraction.^{[34]} The formation of agglomeration inside the
matrix reduces the number of strong interfacial areas, which has a significant
impact on the physical properties of polymer composites.^{[35]} As illustrated in Fig.
2(a), there is less possibility of agglomeration at a 1 wt.% loading of
the ZnO nanofiller. In this case, the particles are few, and they have
sufficient area to scatter within the matrix. Due to the small number of particles
spread far apart, the physical force of attraction between them tends to be
negated due to the increased particletoparticle distance. When the ZnO
nanofiller loading is increased to 2%, the particletoparticle distance tends
to decrease, and the particles cannot overcome the strong physical force of
attraction. As a result of this scenario, the particles congregate and form a
cluster, as illustrated in Fig. 2(b). However,
at a loading of 3 wt.% ZnO nanofiller, substantial agglomeration of the particles
occurs, as illustrated in Fig. 2(c), along with
increased unevenness in cluster dispersion within the matrix. As the nanofiller
loading percentage grows from 2 to 3 wt.%, not only particletoparticle
interaction occurs, but also clustertocluster interaction. This occurrence
leads to the formation of a bigger cluster of nanofiller that tends to
concentrate in one area of the matrix, leaving the remainder of the matrix
empty of filler and/or cluster of nanofiller.
3.4.2 Effect of sonication time
According to the surface roughness characteristics
provided in Table 3, sonication time is an
essential parameter that influences the dispersion of the ZnO nanoparticles
inside the matrix and hence changes the surface topography of the glass fiber
reinforced epoxyzinc oxide (EGZ) nanocomposites, as illustrated in Fig. 2.
During sonication, implosions induce higher
temperatures and pressures extending to 5000 K and 1000 atm respectively in a
time span of around 50 ns,^{[36]}
causing shock waves to propagate through the resin/filler solution. By inducing
eddy currents and severe shear stresses, these shock waves tend to dissolve
surrounding filler particles, resulting in the creation of intense turbulence.^{[37]} When the clusters
of ZnO nanoparticles break down, they are evenly spread out in the matrix,
inferring that there are fewer loose hard ZnO nanoparticles that are found on
the surface in this case. Thus, the roughness of the surface tends to go down
as the sonication time goes up. In Fig. 2(a),
for the sonication time of 10 min, the surface of the EGZ composites is made up
of hard clusters of ZnO fillers that are spread out all over the matrix.
Sonication time increases to 15 min, and these hard
clusters are broken down even more, with a little better dispersion than in the
first case, as shown in Fig. 2(b). However,
when the sonication time is increased to 20 min, the ZnO fillers are spread out
more evenly, and there are fewer clumps, evidently depicted in Fig. 2(c).
3.4.3 Effect of compression time
Compression time is also important for highquality
composite laminates with better physical properties.^{[38]} Compression time is important because it
can lead to flaws in the composite laminate and make the composite less
durable.^{[39]} From Fig. 2, the surface roughness tends to go down as the
compression time goes up. Since longer pressure is applied, the clusters of
nanofiller, if any, are broken down, and the resin that flows because of
compression pressure picks up the brokendown particles and moves them along
with the flow. This ensures that the nanofiller is spread out evenly and the
surface roughness goes down. It also gets easier for microvoids to get out of
the interphases as the compression time goes up ^{[40,41]} and results in the formation of good quality
composite laminate. Fig. 2(c) shows that the
surface of the EGZ composites has more evenly distributed ZnO nanofillers.
There are very few clusters of nanofillers on the surface of EGZ composites.
3.5 Analysis from ANOVA and
regression analysis
It can be seen from ANOVA results presented in Table 4, Table 5, and
Table 6 that filler weight percentage is
significant for all the amplitude functional parameters of surface roughness.
The sonication time and compression time pooled together as conditions; a
categorical factor is also found to be significant in all the cases. As a
result, mathematical prediction models developed for all amplitude functional
parameters associated with all three circumstances are valid. R^{2}, R^{2}adjusted,
and R^{2}predicted values for all established equations are greater
than 95%, indicating that the developed linear model possesses a high
degree of fit and does not require additional predictors. Additionally, the
model demonstrates a high degree of prediction accuracy.
The linear regression equation demonstrates that
increasing the ZnO nanofiller content in the polymerfiller matrix by one
weight percent increases the arithmetic average roughness value by 32.267 µm,
the root mean square increases by 38.317 µm, and the maximum profile
height increases by 393.50 µm, regardless of the conditional effect.
Moreover, the conditions substantially affect the amplitude functional
parameters of surface roughness, as indicated by the increasing trend of the
intercept values in all regression models from conditions 1 to 3, as
illustrated in Equations 412. Thus, statistical analysis confirms the AFM
study and establishes that increasing the sonication duration, compression
time, and filler weight percentage results in an increase in the surface
roughness of the nanocomposites.
A Pareto chart is a type of column chart that is
used to prioritize problemsolving steps to find which element has the greatest
impact on the response variable.^{[42]}
Fig. 3 represents the Pareto chart of
standardized effects for the measured values of surface roughness elements.
From the Figs. 3(a), 3(b)
and 3(c) representing the chart for
arithmetic average roughness, root mean square and maximum profile height
respectively, the factor A: filler content proves to have maximum effect. The
residual plots for arithmetic average roughness, root mean square and maximum
profile height are illustrated by Figs. 4, 5 and 6 respectively.
The result indicating an high significant effect of ZnO filler content on surface
roughness morphology agrees with one of the prior study by Ramezanzadeh et
al.^{[43]}
Fig. 3 Pareto
chart of the standardized effects at α = 0.05 concerning: (a) arithmetic
average roughness; (b) root mean square; (c) maximum profile height.
Fig. 4 Residual
plots for arithmetic average roughness (Ra) representing: (a) normal
probability plots; (b) residual versus fits; (c) frequency histogram; (d)
residual versus order.
From the obtained residual plots for all the three
characterizing elements of surface roughness, it is seen that the variation
between experimental and predicted values is very small, on the standard
residual scale. Moreover, the frequency plots of variation in residuals and the
observed order of experimentation and residuals indicated by histogram,
residual versus fit and residual versus order indicate that there exists a
tendency to have run in both positive and negative directions, indicating a
strong correlation between observed and predicted values.
Fig. 5 Residual plots for root mean square
(Rq) representing: (a) normal probability plots; (b) residual versus fits; (c)
frequency histogram; (d) residual versus order.
Fig. 6 Residual
plots for maximum profile height (Rt) representing: (a) normal probability
plots; (b) residual versus fits; (c) frequency histogram; (d) residual versus
order.
3.6
Multivariate analysis
Multivariate analysis is conceptualized by tradition
as the statistical study of experiments in which multiple measurements are made
on each experimental unit and for which the relationship among multivariate
measurements and their structure are important to the experiment's
understanding.[44] In the present study, the surface roughness factors namely arithmetic average roughness,
root mean square and maximum profile height is analyzed for the relationship
between the conditions and filler content weight percentage. Fig. 7 illustrates the consolidated multivariate
analysis chart obtained using MINITAB® 21, wherein the Xaxis represents the
conditions, Yaxis represents the surface roughness values and the colored
legends (circle, square and triangle) represent the different filler content
wt.% (1, 2 and 3%). The results indicate that irrespective of the filler
content in the nanocomposites, the condition 1 with a sonication time – 10 min;
Compression time – 5 min yielded lowest surface roughness and the trend of
increment is seen from condition 1 to 3. Similarly, it is also observed that
irrespective of the pooled effect of the conditions, nanocomposites having 3%
filler content showcased the highest surface roughness values.
Fig. 7 Multivariate plots for: (a)
arithmetic average roughness; (b) root mean square; (c) maximum profile height.
3.7 Validating experiments
A small set of validating experiments were conducted
to check the prediction accuracy of the developed model. The ZnO nanofiller
composition was increased to 4, and 5 wt.% in the R_{qexp,} and R_{texp}.
Table 11, Table 12,
and Table 13 provide the comparison of
experimental values of amplitude functional parameters of surface roughness
with the theoretically calculated value using developed models: R_{ath},
R_{qth,} and R_{tth} through the error percentage. The
validating test experiments show that the error is too low between the
experimental and theoretically calculated values. Thus, the models could be very
well used to predict the surface roughness for any filler weight percentage
within the experimental limitation of sonication and compression time values.
The current study only looks at the amplitude functional parameters under three
different conditions.
Table 10. Amplitude
functional parameters of surface roughness measured for EGZ nanocomposites in
the validating experiments.
ZnO Weight% in the polymerfiller matrix 
Sonication time St (min) 
Compression time Ct (min) 
Raexp
(µm) 
Rqexp
(µm) 
Rtexp
(µm) 
4 
10 
5 
138 
180 
1969 
4 
15 
10 
146 
189 
1976 
4 
20 
15 
150 
203 
2073 
5 
10 
5 
170 
217 
2361 
5 
15 
10 
178 
226 
2365 
5 
20 
15 
182 
242 
2462 
Table 11. Comparison
of experimental and theoretical values for arithmetic average roughness.
ZnO Weight% in
the polymerfiller matrix 
Sonication
time St (min) 
Compression
time Ct (min) 
R_{aexp}
(µm) 
R_{ath}
(µm) 
Error% 
4 
10 
5 
138 
139.538 
1.11% 
4 
15 
10 
146 
147.968 
1.35% 
4 
20 
15 
150 
151.868 
1.25% 
5 
10 
5 
170 
171.805 
1.06% 
5 
15 
10 
178 
180.235 
1.26% 
5 
20 
15 
182 
184.135 
1.17% 
Table 12. Comparison of experimental and theoretical
values for root mean square.
ZnO Weight% in
the polymerfiller matrix 
Sonication
time St (min) 
Compression
time Ct (min) 
R_{qexp}
(µm) 
R_{qth}
(µm) 
Error% 
4 
10 
5 
180 
182.335 
1.30% 
4 
15 
10 
189 
191.301 
1.22% 
4 
20 
15 
203 
206.635 
1.79% 
5 
10 
5 
217 
220.652 
1.68% 
5 
15 
10 
226 
229.618 
1.60% 
5 
20 
15 
242 
244.952 
1.22% 
Table 13. Comparison
of experimental and theoretical values for maximum profile height.
ZnO Weight% in the polymerfiller matrix 
Sonication time St (min) 
Compression time Ct (min) 
R_{texp} (µm) 
R_{tth} (µm) 
Error% 
4 
10 
5 
1969 
1999 
1.52% 
4 
15 
10 
1976 
2005 
1.47% 
4 
20 
15 
2073 
2112.67 
1.91% 
5 
10 
5 
2361 
2392.5 
1.33% 
5 
15 
10 
2365 
2398.5 
1.42% 
5 
20 
15 
2462 
2506.17 
1.79% 
3.8 ZnO nanofiller weight
percentages
In the future, researchers can focus their efforts
on analyzing and constructing prediction regression models for a range of
surface topologies and formal solutions by augmenting or varying the
different nanofiller materials in the polymerfiller matrix. Moreover,
models could also be focused on in the future, considering more parameters that
might affect the surface roughness. Developing such models is critical
because there is growing interest among researchers and engineers in foreseeing
the surface topological performance of nanocomposites during the early design
stages without relying on highly strong material science expertise. By
anticipating surface roughness during the early conceptual stages, engineers
and researchers can optimize the nanomaterial addition by weight percent for a
particular sonication and compression period and ultimately attain the desired
quality of the final nanocomposite. On the other hand, the primary advantage of
such polynomial regression models is that they may be utilized as inputs to
scientific software used by engineers or researchers to perform longterm
calculations of surface roughness's amplitude functional parameters. For these
reasons, there is always a need to develop such regression models that can
reduce the most sophisticated and detailed numerical models to simple
polynomial functions that can provide the same information in terms of
calculation fidelity while using the least amount of computational memory and
effort.
4.
Conclusion
The influence of ZnO nanofiller, sonication time,
and compression time on the amplitude functional parameters of surface
roughness of glass fiber reinforced epoxyZnO nanocomposites is examined in
this work utilizing atomic force microscopy (AFM) and statistical analysis. The
result indicates that increased ZnO nanofiller content in the polymerfiller
matrix increases the amplitude functional parameters of the EGZ nanocomposites.
Moreover, higher sonication and compression times improved the fineness of the
nanocomposite, but the effect of increased nanofiller content in the
polymerfiller matrix negated and outweighed them. The analysis of variance
(ANOVA) analysis demonstrates that sonication time, compression time, and
filler content all have a significant influence on all amplitude functional
parameters of surface quality. Nonetheless, the contribution of filler content
is substantially more than the combined contribution of sonication and
compression time. As a result, the previous conclusion is statistically
supported. The developed and validated linear regression equation demonstrates
that increasing the ZnO nanofiller content in the polymerfiller matrix by 1
wt.% increases the arithmetic average roughness value by 32.267 µm, the
root mean square value by 38.317 µm, and the maximum profile height by
393.50 µm, regardless of the conditional effect.
Conflict of Interest
There is no conflict of interest.
Supporting Information
Not applicable.
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Author
Information
Dr.
Anupama Hiremath is an Assistant Professor in the
Department of Mechanical and industrial Engineering at Manipal Institute of
Technology, Manipal. She has received her Ph.D. in the field of materials science
from Visvesvaraya Technological University in 2020. She has more than 15 years
of teaching graduate students of Mechanical Engineering and has been involved
in research of composite materials since the days of her graduation. Her
research interests include metal matrix composites, polymer matrix composites,
selfhealing materials and biobased materials. She has published several
research articles in the National and International Journals of repute.
Dr. Sridhar Thipperudrappa has
received his Ph.D. in the field of materials science from Manipal Academy of
Higher Education in 2021. He has more than 10 years of teaching graduate and
postgraduate students of Mechanical and Mechatronics Engineering and has been
involved in research of polymer composite materials. His research interests
include polymer matrix composites and selfhealing materials. He has published
research articles in the National and International Journals of repute.
Mr.
Ritesh Bhat is an Assistant professor (senior scale) in the
Department of Mechanical and Industrial Engineering at Manipal Institute of
Technology, MAHE, Manipal, India. Design of Experiments, Optimization
Techniques, Machining of Materials, Supply Chain Management and Lean
Manufacturing are the areas of his expertise.
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