Journal of Composite Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 12, 2024
The
multi-layered
(fiber/metal)
structure
of
glass
fibre
aluminium
reinforced
epoxy
(GLARE)
makes
it
difficult
to
obtain
acceptable
damage-free
holes
that
meet
aerospace
standards.
This
paper
investigated
the
effects
tool
geometry
and
drilling
parameters
on
reducing
delamination
damage
uncut
fibers
at
hole
exit
surface
in
GLARE.
surfaces
were
examined
by
scanning
electron
microscope
(SEM)
various
magnifications.
In
addition,
deep
neural
network
(DNN)
long-short-term
memory
(LSTM)
machine
learning
models
used
predict
(F
da
),
fiber
(UCF),
thrust
forces
using
experimental
data.
No
positive
contribution
special
was
observed,
while
standard
found
be
ideal
for
conditions.
Analysis
variance
(ANOVA)
revealed
feed
rate
contributed
57.83%
damage,
74.31%
92.33%
force,
respectively.
SEM
analysis
high
deformation
zones
aluminum
layers
fracture
separation
polymer
(GFRP)
layers.
DNN
LSTM
provide
accurate
predictions
with
R
2
values
greater
than
95%
98%,
Polymer Composites,
Год журнала:
2024,
Номер
45(10), С. 9421 - 9439
Опубликована: Апрель 9, 2024
Abstract
This
study
investigates
the
influence
of
NaOH
treatment
on
tribological
behavior
in
hybrid
fiber‐reinforced
composites,
specifically
employing
Banana
fiber
with
Al
2
O
3
filler
an
epoxy
matrix.
Through
design
experiments
(DOE),
disc
speed,
wear
duration,
and
are
analyzed
for
specific
rate
(SWR)
coefficient
friction
(COF).
To
advance
understanding
characteristics,
leverages
advanced
machine
learning,
using
Python‐powered
artificial
neural
networks
(ANN),
is
integrated
innovative
ANN
hyperparameter
optimization.
Optimized
parameters
(1050
rpm,
60
s,
5%
treatment)
significantly
minimize
SWR
(12.38
×
10
−5
mm
/Nm)
COF
(0.2).
Scanning
electron
microscopy
(SEM)
analysis
reveals
improved
interfacial
adhesion
identifies
micro‐cracks
as
primary
mechanism.
work
contributes
to
a
profound
offering
fine‐tuned
predictive
model
optimizing
advancing
material
science
engineering.
Highlights
Reduced
SWR,
composites
via
DOE:
Explored
impact.
Advanced
learning
techniques
enhanced
prediction.
Innovative:
Optimal
Parameters:
1050
treatment.
Polymer Composites,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 14, 2025
Abstract
This
study
examines
how
NaOH
treatment
and
alumina
filler
affect
the
mechanical
properties,
water
absorption,
thermal
degradation,
sliding
wear
of
epoxy
composites
reinforced
with
pineapple
leaf
fiber.
greatly
improved
composites'
tensile,
flexural,
impact
strengths
by
strengthening
bond
between
fiber
matrix.
Furthermore,
incorporation
further
elevated
properties.
The
composite
10%
showed
peak
values
41.4
MPa
in
tensile
strength,
63.8
flexural
37.6
kJ/m
2
strength.
Because
hygroscopic
parts
were
removed
from
treated
composites,
they
absorbed
much
less
water.
15%
had
lowest
absorption
at
18%
after
192
h.
Thermal
degradation
analysis
that
stability,
having
highest
char
residue
(15.3%)
700°C.
Sliding
tests
reinforcement
significantly
reduced
specific
rate
(SWR)
coefficient
friction
(COF).
an
SWR
0.2598
×
10
−5
mm
3
/Nm
a
COF
0.103
when
120
cm/s,
45
N
load
over
1500
m
distance.
A
scanning
electron
microscopy
found
untreated
experienced
severe
abrasive
wear,
while
exhibited
mild
adhesive
wear.
shows
treating
PALF
adding
enhance
their
mechanical,
thermal,
tribological
making
them
suitable
for
high‐performance
industrial
applications.
Highlights
Alumina
(41.4
MPa)
strength
(63.8
MPa).
NaOH‐treated
moisture,
enhancing
durability.
stability
improved,
15.3%
700°C
alumina.
Optimized
achieved
(0.2598
/Nm).
Artificial
neural
network
response
surface
methodology
accurately
predicted
optimized
behavior.
Engineering Reports,
Год журнала:
2025,
Номер
7(4)
Опубликована: Апрель 1, 2025
ABSTRACT
This
study
investigates
the
mechanical
properties
of
hybrid
composites
reinforced
with
jute,
kenaf,
and
glass
fibers,
incorporating
Aluminum
Oxide
(Al
2
O
3
)
as
a
nanoparticle
filler.
The
effects
three
key
parameters—fiber
orientation,
fiber
sequence,
weight
percentage
Al
on—the
tensile
impact
strength
were
examined.
Three
levels
for
each
factor
considered:
orientation
(0°,
45°,
90°),
sequence
(1,
2,
layers),
varying
content
(3%,
4%,
5%).
response
surface
methodology
(RSM)
was
employed
to
optimize
parameters,
providing
insights
into
interactions
between
these
factors
their
influence
on
composite's
performance.
Additionally,
artificial
neural
networks
(ANN)
used
prediction
modeling.
outcome
presented
that
ANN
model
outpaced
RSM
in
terms
accuracy,
higher
correlation
predicted
experimental
values.
optimal
parameters
achieving
highest
determined,
at
90°,
3,
5%.
demonstrates
effectiveness
predicting
laminated
composite
highlights
significant
role
reinforcement
enhancing
