Polymer Composites,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 20, 2024
Abstract
The
accurate
and
efficient
prediction
of
impact
mechanical
response
is
crucial
for
safety
design
composite
structures.
In
this
work,
high‐fidelity
representative
volume
elements
(RVEs)
with
fiber,
matrix
fiber/matrix
interface
are
established,
in
which
random
fiber
distributions
considered.
A
failure
envelope
under
transverse
loads
proposed
based
on
computational
micromechanical
RVEs,
it
implemented
by
ABAQUS
VUMAT
subroutines
to
predict
the
laminates
loads.
Based
a
dataset
from
macromechanical
finite
element
simulations,
an
artificial
neural
network
model
established
trained.
It
found
that
distribution
introduced
more
obvious
fluctuation
tension/compression
strength
than
shear
strength.
criteria
showed
better
performance
Hashin
Tsai‐Wu
especially
combined
compression
An
ANN
8
hidden
layers
can
achieve
acceptable
coefficient
determination
(R
2
)
0.98
loss
functions
mean
absolute
error
(MAE)
71.
For
certain
loading
conditions,
well
trained
machine
learning
predicted
contact
force
history
within
30
min,
while
FEA
costs
about
75
min
same
computer.
speed
increased
over
60%
conditions.
hence
shown
method
provides
potential
alternative
evaluation
resistance
Highlights
High‐fidelity
micromechanics
analysis
performed
uncover
complex
relationship
between
microstructure
strengths
laminates.
dependent
criterion
shows
high
accuracy
compared
criteria.
multi‐layer
rapid
achieved
0.98,
Heliyon,
Год журнала:
2025,
Номер
11(3), С. e42133 - e42133
Опубликована: Янв. 23, 2025
This
study
investigates
the
utilization
of
waste
iron
slag
(WIS)
as
a
sustainable
alternative
in
concrete
production
to
reduce
environmental
impact
and
preserve
natural
resources.
The
experimental
investigation
WIS-incorporated
focused
on
compressive
tensile
strength
with
machine
learning
(ML)
models
for
prediction.
Among
tested
ML
algorithms,
Decision
Tree
(DT)
XGBoost
showed
highest
accuracy
(R2
=
0.95135)
predicting
properties,
while
like
SVM
Symbolic
Regression
underperformed.
Experimental
results
indicate
that
up
20
%
WIS
replacement
maintains
adequate
strength,
whereas
higher
proportions
structural
integrity.
A
ranking
score
index
cost
analysis
confirmed
technical
economic
feasibility
using
concrete.
Cost
demonstrated
substantial
savings
25
incorporation,
confirming
its
feasibility.
Integrating
data
predictions
highlights
WIS's
potential
applications,
enabling
optimized
mix
designs
reduced
reliance
physical
testing.
Future
work
should
address
limitations,
including
dataset
expansion
exploration
additional
durability
mechanical
properties
validate
practicality
construction
further.
Journal of Adhesion Science and Technology,
Год журнала:
2024,
Номер
unknown, С. 1 - 29
Опубликована: Окт. 11, 2024
Examining
the
mechanical
performance
of
CFRP
and
aluminum
samples
subjected
to
environmental
aging
is
crucial.
Additionally,
it
essential
develop
methods
enhance
their
properties.
This
research
investigates
impact
fullerene
single-walled
carbon
nanotubes
(SWCNT)
on
fatigue
life
static
strength
bonded
bonded/bolted
joints.
The
study
focuses
composite-to-composite
(CTC)
composite-to-aluminum
(CTA)
substrates
under
three-point
bending,
both
before
after
hygrothermal
aging.
were
divided
into
four
categories:
(1)
neat
specimens,
(2)
specimens
with
added
fullerene,
(3)
SWCNT,
(4)
a
combination
50%
SWCNT
fullerene.
experimental
results
indicated
that
optimal
nanoparticle
ratio
for
joints
differs
from
Adding
nanoparticles
adhesive
increased
SLJs,
particularly
in
containing
mixed
particles
SWCNT.
In
some
cases,
amplified
effect
conditions,
enhancing
further.
integration
use
significantly
improved
joint
strength,
techniques
yielding
best
results.
These
modified
offer
promising
alternative
traditional
terms
life.
enhances
understanding
hybrid
joints,
especially
dissimilar
(composite
metal),
provides
insights
behavior
various
conditions.
show
potential
optimizing
composite
structures,
improving
durability,
reducing
likelihood
operational
failures.
Polymer Composites,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 6, 2024
Abstract
This
study
investigates
how
carbon
fiber
reinforced
polymer
(CFRP)‐to‐aluminum
adhesive
joints
behave
under
accelerated
aging
conditions
with
hygrothermal
exposure
and
compares
these
findings
against
naturally
aged
samples
to
evaluate
material
reliability
in
challenging
environments.
The
CFRP‐to‐aluminum
were
manufactured
subjected
natural
for
durations
ranging
from
1
3
years
6‐month
intervals,
as
well
(hygrothermal)
periods
100
1200
h,
intervals
of
50
h.
Subsequently,
the
mechanical
properties
evaluated
using
a
three‐point
bending
test.
To
forecast
times
data,
five
machine
learning
models
utilized:
artificial
neural
network,
support
vector
regression,
linear
polynomial
random
forest
regression.
Hygrothermal
significantly
degraded
matrix,
causing
shift
failure
modes
cohesive
mixed
types
(cohesive,
adhesive,
tear
failures),
leading
notable
decline
strength.
observed
23.13%
strength
reduction
24.33%
decrease
those
1000
h
aging.
regressor
demonstrated
superior
accuracy
predicting
across
different
periods.
Through
application
models,
this
introduces
novel
approach
data
experiments.
method
shows
potential
optimizing
composite
structures,
ultimately
improving
their
durability
minimizing
likelihood
failures
during
operational
use.
Highlights
Studied
effects
on
polymer/Aluminum
(AL)
joints.
Noted
Used
models;
regression
had
highest
accuracy.
Analyzed
correlation
between
dissimilar
Polymer Composites,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 7, 2024
Abstract
Assessing
the
mechanical
properties
of
CFRP
and
aluminum
specimens
exposed
to
hygrothermal
aging
is
vital.
Moreover,
it
important
develop
strategies
improve
these
properties.
This
study
examines
influence
fullerene
Single‐Walled
Carbon
Nanotubes
(SWCNT)
on
fatigue
life
static
strength
bonded
bonded/bolted
joints.
The
research
concentrates
composite‐to‐composite
composite‐to‐aluminum
substrates
under
three‐point
bending
tests,
both
prior
after
aging.
samples
were
classified
into
four
groups:
(1)
neat
specimens,
(2)
with
added
fullerene,
(3)
containing
SWCNT,
(4)
a
blend
50%
SWCNT
fullerene.
findings
indicated
that
optimal
nanoparticle
ratio
for
joints
differs
from
Incorporating
nanoparticles
adhesive
enhanced
single
lap
(SLJs),
particularly
in
mixed
particles
SWCNT.
In
some
instances,
intensified
effects
conditions,
further
increasing
life.
incorporation
use
significantly
joint
strength,
combination
yielding
best
results.
improves
understanding
hybrid
joints,
dissimilar
configurations,
offers
insights
their
performance
various
environmental
conditions.
Highlights
Study
impacts
CTC/CTA
fatigue.
Optimal
ratios
differ
Nanoparticles
reduce
moisture
absorption,
damage,
increase
failure
load.
enhance
life,
varying
by
type,
volume,
load,
joint.
strength.
Journal of Composite Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 17, 2024
This
study
investigates
the
effects
of
adding
fullerene
and
single-walled
carbon
nanotubes
(SWCNT)
on
strength
durability
bonded
bonded/bolted
joints,
specifically
for
composite-to-composite
(CTC)
composite-to-aluminum
(CTA)
substrates
under
three-point
bending,
both
before
after
hygrothermal
aging.
Samples
were
categorized
into
neat
specimens,
specimens
with
added
fullerene,
SWCNT,
a
combination
50%
SWCNT
fullerene.
Results
show
that
optimal
nanoparticle
ratio
differs
versus
joints.
Nanoparticles
significantly
reduced
degradation
from
exposure,
preventing
interfacial
debonding
slowing
loss.
Mixed
formulations
improved
cohesive
shifted
failure
adhesive
interface
to
within
layer,
enhancing
joint
performance
unaged
aged
conditions.
Furthermore,
six
machine
learning
models—ridge
regression,
decision
tree,
random
forest
regressor,
gradient
boosting
support
vector
neural
networks—were
applied
predict
static
The
regression
tree
models
demonstrated
superior
while
ridge
regressor
most
effective
analysis
highlights
type,
substrate,
type
percentage,
environmental
aging
influence
performance.
offers
valuable
insights
dissimilar
providing
framework
enhance
reduce
risk
during
operational
use.
Journal of Composite Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 4, 2024
This
paper
investigates
the
thermal
and
mechanical
properties
of
carbon/high
silica/phenolic
composites
with
varying
reinforcement
ratios.
Five
hybrid
samples
were
fabricated:
100%
carbon,
75%
carbon/25%
silica,
50%
carbon/50%
25%
carbon/75%
silica.
A
three-point
bending
test
evaluated
their
strength,
while
an
ablation
at
3000°C
for
1
minute
measured
backside
temperature,
linear
rate,
mass
rate.
Results
indicated
that
carbon
sample
had
highest
silica
achieved
lowest
rates,
demonstrating
effective
balance
between
fire
retardancy
insulation,
resulting
in
minimal
temperature
during
ablation.
Additionally,
five
machine
learning
models
(Linear
Regression,
Decision
Trees,
Random
Forests,
Gradient
Boosting
Machines,
Neural
Networks)
utilized
to
predict
strength.
Trees
Machines
exhibited
prediction
accuracy,
Linear
Regression
struggled
non-linear
data,
lower
accuracy
rate
predictions.
Notably,
these
also
able
generalize
other
percentages,
showcasing
robustness
versatility
optimizing
material
compositions
beyond
tested
scenarios.
study
highlights
potential
predicting
advanced
composites,
contributing
development
high-temperature
resistant
materials.
