Advanced Intelligent Systems,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
4D
printing
with
carbon
nanotube
(CNT)‐reinforced
polymers
enables
advanced
shape‐changing
materials
but
faces
challenges
in
CNT
dispersion
and
performance.
This
study
addresses
these
limitations
by
functionalizing
CNTs
polyethylene
glycol
(PEG),
significantly
enhancing
interfacial
bonding
within
biocompatible
polyvinyl
chloride
(PVC)‐polycaprolactone
(PCL)
composites.
The
composites,
tailored
for
biomedical
applications
a
glass
transition
temperature
(T
g
)
of
37–41
°C,
exhibit
enhanced
mechanical,
thermal,
shape‐memory
properties.
At
0.5
wt%
CNT,
the
composite
achieves
25%
increase
tensile
strength,
95.78%
shape
fixity,
5‐s
recovery
time,
offering
an
optimal
balance
flexibility,
rapid
recovery.
Higher
concentrations
(5
wt%)
further
improve
thermal
stability,
increasing
decomposition
20
°C
storage
modulus
670
MPa,
although
ductility
is
reduced.
PEG
grafting
prevents
agglomeration,
enabling
high
filler
loading
without
compromising
printability,
as
confirmed
through
uniform
nanoparticle
defect‐free
fused
deposition
modeling
(FDM)‐printed
structures.
These
intelligent
composites
combine
biocompatibility,
durability,
excellent
performance,
making
them
suitable
diverse
structural
applications,
such
adaptive
medical
devices,
ergonomic
shoe
soles,
wearable
biosensors.
novel
material
provides
versatile
platform
high‐performance,
4D‐printed
systems
that
address
current
polymer
nanocomposites
advance
engineering
innovations.
Journal of Composites Science,
Год журнала:
2024,
Номер
8(10), С. 416 - 416
Опубликована: Окт. 10, 2024
This
review
explores
fundamental
analytical
modelling
approaches
using
conventional
composite
theory
and
artificial
intelligence
(AI)
to
predict
mechanical
properties
of
3D
printed
particle-reinforced
resin
composites
via
digital
light
processing
(DLP).
Their
mechanisms,
advancement,
limitations,
validity,
drawbacks
feasibility
are
critically
investigated.
It
has
been
found
that
Halpin-Tsai
model
with
a
percolation
threshold
enables
the
capture
nonlinear
effect
particle
reinforcement
effectively
DLP-based
reinforced
various
particles.
The
paper
further
how
AI
techniques,
such
as
machine
learning
Bayesian
neural
networks
(BNNs),
enhance
prediction
accuracy
by
extracting
patterns
from
extensive
datasets
providing
probabilistic
predictions
confidence
intervals.
aims
advance
better
understanding
material
behaviour
in
additive
manufacturing
(AM).
demonstrates
exciting
potential
for
performance
enhancement
composites,
employing
optimisation
both
selection
parameters.
also
benefit
combining
empirical
models
AI-driven
analytics
optimise
parameters,
thereby
advancing
AM
applications.
Materials Research Express,
Год журнала:
2024,
Номер
12(1), С. 015301 - 015301
Опубликована: Дек. 19, 2024
Abstract
This
study
focused
on
a
modified
Fused
Deposition
Modeling
(FDM)
3D
printing
method,
specifically
the
direct
pellet
of
propylene-based
thermoplastic
elastomer,
Vistamaxx™
6202,
to
address
challenges
like
printability
and
weak
mechanical
properties.
The
main
objective
was
optimizing
parameters
investigating
their
impact
Taguchi
method
used
design
experiments,
reducing
required
experiments
maximize
desired
Three
influential
were
chosen,
each
changing
three
levels.
By
employing
number
decreased
from
27
full
factorials
9.
Regression
models
created
through
analysis
variance
(ANOVA)
verified
by
additional
experiments.
Tensile
tests
performed
according
ASTM
D638
standard.
SEM
imaging
assess
interlayer
adhesion
structural
integrity.
results
demonstrated
satisfactory
integrity
printed
samples.
Notably,
elastomers
achieved
significant
stretchability,
reaching
up
5921.3%.
tensile
strength
5.22
MPa,
with
modulus
1.7
MPa.
effect
parameter
contribution
percentage
strength,
elongation,
elastic
obtained
analysis.
