Advanced Industrial and Engineering Polymer Research,
Journal Year:
2023,
Volume and Issue:
6(4), P. 341 - 355
Published: Jan. 19, 2023
Magnesium
(Mg)
&
its
alloys
are
favourable
for
orthopaedic
cardiovascular
medical
device
fabrication
applications,
but
holds
a
natural
ability
to
degrade
biologically
when
put
with
aqueous
solution
of
the
substances
and/or
water-saturated
tissue
in
context
living
organism.
Mg
nature
corrode
inside
organism
body
is
mainly
attributed
excessive
rates
corrosion
Mg.
Poor
resistance
possessed
by
decreases
mechanical
properties
implants,
and
adds
toxic
effects
on
bone
metabolism.
A
potential
method
increasing
alloy
without
changing
protective
polymeric
deposit
coatings.
Moreover,
impart
better
biocompatible
aspects
based
materials
biopolymers
have
been
used
as
composite
constituent.
This
review
such
constituting
biopolymers.
Their
resulting
osteopromotive
conjunction
biocompatibility
may
help
clinicians
fix
existing
related
issues.
Polymer Composites,
Journal Year:
2023,
Volume and Issue:
44(7), P. 3715 - 3747
Published: April 26, 2023
Abstract
Additive
manufacturing
(AM)
is
being
used
more
widely
because
of
the
simplicity
operating
procedures.
It
decreases
material
consumption
during
part
production
and
eliminates
waste.
Fused
deposition
modeling
(FDM),
a
well‐known
AM
process,
uses
thermoplastic
polymer
as
feedstock
to
manufacture
end
design.
Pure
thermoplastic‐based
products
are
still
utilized
prototypes
in
several
sectors
due
their
lack
strength
durability.
This
problem
has
been
solved
by
strengthening
thermoplastics
adding
reinforcing
element.
Composites
made
polymers
various
constituents
known
components.
article
overviews
natural
reinforced
composites
FDM
process.
Mechanical
Thermal
properties
presented
based
on
filler
percentage,
this
only
considered
composite
filaments
for
Polymer Composites,
Journal Year:
2024,
Volume and Issue:
45(9), P. 7950 - 7966
Published: March 14, 2024
Abstract
This
article
reports
on
development
of
an
adaptive
framework
for
predicting
the
erosion
performance
polymer
composites
using
certain
statistical
and
machine
learning
(ML)
models.
For
this,
ramie‐epoxy
reinforced
with
variations
(0–30
wt%)
sponge
iron
slag
(an
industry
waste)
are
considered.
The
fabricated
then
subjected
to
high
temperature
solid
particle
wear
trials
following
Taguchi's
L
27
orthogonal
array.
effects
different
control
factors
rate
in
interactive
environment
appraised
by
analysis
variance
(ANOVA)
which
reveals
filler
content
as
most
significant
factor
contributing
66.21%,
followed
impact
velocity
(22.86%)
impingement
angle
(2.28%).
A
regression
model
based
input–output
parameters
obtained
from
experimentation
is
constructed
prediction
rate.
Further,
four
predictive
models
algorithms
also
proposed
predict
composites.
feasibility
each
ML
assessed
appropriate
metrics.
Among
all
models,
gradient
boosting
found
be
reliable
exhibiting
highest
accuracy
least
errors.
Highlights
Development
novel
class
slag.
Database
creation
Data‐driven
modeling
rates
learning.
Comparison
identifying
best
one.
