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:
2025,
Номер
unknown
Опубликована: Апрель 9, 2025
Abstract
Fiber‐reinforced
polymer
(FRP)
tendons
are
preferred
in
civil
engineering
for
their
lightweight
properties,
high
strength,
corrosion
resistance,
and
electrical
insulation.
However,
initial
defects
that
arise
during
material
preparation
can
adversely
affect
the
mechanical
performance
service
life
of
structures.
Local
identification
technology
is
inadequate
FRP
products
with
variable
thickness
cross‐sections,
especially
tendons,
resulting
low
detection
efficiency.
This
article
presents
an
innovative
inverse
problem‐solving
framework
aimed
at
simultaneously
identifying
location
severity
through
frequency
change
rates.
A
convolutional
recurrent
neural
network
(CRNN)
model
was
developed
to
establish
mapping
between
rates
associated
damage
information,
including
severity.
The
CRNN
model's
database
generated
from
finite
element
models
(FEM),
which
were
validated
against
Euler
beam
vibration
theory,
demonstrating
absolute
error
less
than
1%.
trained
using
this
optimized
data
matrix
reconstruction,
refinement,
dilated
convolution,
achieving
a
mean
(Mae)
0.115%
predicting
rate.
significantly
surpassed
CNN
(0.318%),
MLP
(0.274%),
LSTM
(0.334%)
models.
served
as
surrogate
problem,
addressed
Slime
Mold
Algorithm
(SMA)
model.
prediction
SMA
under
0.5%,
notably
better
FEM.
Consequently,
identifies
defects'
offering
valuable
insights
applications
various
products.
Highlights
achieved
MAE
rates,
41.6%
MLP.
Optimized
identified
97.8%
accuracy.
Hammering
method
effectively
excited
first
8
frequencies
tendons.
Experimental
theoretical
errors
FEM
analysis
stayed
below
Journal of Composites Science,
Год журнала:
2025,
Номер
9(4), С. 195 - 195
Опубликована: Апрель 21, 2025
Fiber-reinforced
polymer
composites
are
exposed
to
severe
environmental
conditions
throughout
their
intended
lifespan.
It
is
essential
investigate
how
they
age
when
cold
and
hot
water
increase
the
durability
of
fiber-reinforced
composites.
This
work
uses
a
hand
lay-up
process
create
with
different
weight
percentages
glass
fiber,
nanoclay,
epoxy.
ASTM
guidelines
followed
for
performing
tensile
flexural
tests.
The
input
parameters,
varying
wt.%
fiber
continuous,
aging
condition
deemed
categorical
factor.
mechanical
properties
considered
as
response
variables
(output).
optimized
using
Response
Surface
Methodology
(RSM),
while
Artificial
Neural
Networks
(ANNs)
provide
reliable
predictive
model
high
correlation
coefficients.
findings
demonstrate
that
ANNs
outperform
RSM
in
strength
prediction,
whereas
offers
greater
accuracy
modeling.
SEM
analysis
fracture
surfaces
reveals
causes
specimen
failure
under
load,
distinct
differences
between
dry,
cold,
boiling
water-soaked
specimens.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science,
Год журнала:
2024,
Номер
238(19), С. 9577 - 9588
Опубликована: Июль 26, 2024
Eco-conscious
products
are
currently
garnering
significant
attention
due
to
their
abundant
availability
and
versatile
applications
in
various
engineering
contexts.
The
distinctive
properties
of
natural
fibers
make
them
readily
substitutable
for
synthetic
fibers.
A
substantial
one
million
tonnes
fiber
waste,
primarily
composed
paddy
straw
fiber,
is
generated
globally.
research
emphasis
revolves
around
the
adoption
Waste
Wealth
technique,
a
highly
efficient
process
aimed
at
repurposing
waste
materials.
This
approach
plays
pivotal
role
curbing
air
pollution,
specifically
by
preventing
incineration
residual
portion
on
agricultural
lands.
study
involves
collection
from
fields,
with
subsequent
extraction
facilitated
an
extracting
machine.
Employing
chemical
treatment
enhances
adhesion
between
matrix.
Consequently,
comprehensive
tests,
including
single
test,
tenacity,
fineness,
were
meticulously
examined
both
treated
untreated
reinforcements
matrix
taken
weight
percentage
50:50
different
length
(25,
50,
75,
100
mm)
using
compression
moulding
machine
300
mm
×
3
dimensions.
After
making
laminates,
samples
cut
as
per
ASTM
standard.
mechanical
morphological
behavior
hybrid
fiber-reinforced
polyester
composites
evaluated,
water
absorption
property
concerned
laminates
was
studied.
Materials and Manufacturing Processes,
Год журнала:
2024,
Номер
unknown, С. 1 - 10
Опубликована: Сен. 3, 2024
Non-rotational
sliding
ball
burnishing
(NRSBB)
was
developed
as
a
polishing
process.
The
effect
of
NRSBB
parameters
on
longitudinal
and
transverse
roughness
(Ra
||
&
Ra
⊥),
microhardness
(MHV)
AA7075-T651
face-milled
plate
investigated
by
response
surface
methodology.
decreased
increasing
the
diameter.
⊥
increased
with
depth
step
over.
MHV
enhanced
passes.
optimal
obtained
from
multi-objective
optimization
were
diameter
10
mm,
0.18
over
0.062
five
passes,
feed
rate
1050.3
mm/min.
Compared
surface,
optimized
improved
||,
93.7%,
89.3%
37.3%,
respectively.
topographic
Sa,
Sq,
Sp
Sv
89.3
%,
87.5%,
76.4%
88.9%,
distribution
sample
indicated
600
μm
hardened
depth.
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%,
