A Technical–Economic Study on Optimizing FDM Parameters to Manufacture Pieces Using Recycled PETG and ASA Materials in the Context of the Circular Economy Transition
Polymers,
Journal Year:
2025,
Volume and Issue:
17(1), P. 122 - 122
Published: Jan. 6, 2025
This
paper
presents
the
results
of
research
on
technical–economic
optimization
FDM
parameters
(Lh—layer
height
and
Id—infill
density
percentage)
for
manufacture
tensile
compression
samples
from
recycled
materials
(r)
PETG
(polyethylene
terephthalate
glycol)
ASA
(acrylonitrile
styrene
acrylate)
in
context
transition
to
a
circular
economy.
To
carry
out
our
study,
fundamental
principle
value
analysis
was
used,
which
consists
maximizing
ratio
between
Vi
Cp,
where
represents
mechanical
characteristic
(tensile
strength
or
compressive
strength)
Cp
production
cost.
The
this
study
showed
that,
case
manufactured
by
(rPETG),
parameter
that
significantly
influences
Vi/Cp
ratios
is
Lh
(the
layer),
while
additively
(rASA),
decisively
Id
infill
percentage).
In
(rPETG)
signified
Following
parameters,
using
multiple-response
optimization,
we
identified
optimal
parts
rPETG
rASA:
=
0.20
mm
100%.
demonstrated
use
plastics
lends
itself
consumption
model
based
Language: Английский
Investigations on Thermal Transitions in PDPP4T/PCPDTBT/AuNPs Composite Films Using Variable Temperature Ellipsometry
Polymers,
Journal Year:
2025,
Volume and Issue:
17(5), P. 704 - 704
Published: March 6, 2025
Herein,
we
report
a
comprehensive
investigation
on
the
thermal
transitions
of
thin
films
poly
[2,5-bis(2-octyldodecyl)pyrrolo[3,4-c]pyrrole-1,4(2H,5H)-dione
-3,6-diyl)-alt-(2,2';5',2″;5″,2'″-quaterthiophen-5,5'″-diyl)]PDPP4T,
poly[2,6-(4,4-bis-(2-ethy-lhexyl)-4H-cyclopenta
[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)]
PCPDTBT,
1:1
blend
PDPP4T
and
their
composites
with
gold
nanoparticles
(AuNPs).
The
these
materials
were
studied
using
variable
temperature
spectroscopic
ellipsometry
(VTSE),
differential
scanning
calorimetry
(DSC)
serving
as
reference
method.
Based
obtained
VTSE
results,
for
first
time,
have
determined
phase
diagrams
PDPP4T/PCPDTBT
AuNPs
composites.
measurements
revealed
distinct
in
films,
including
characteristic
temperatures
corresponding
to
pure
phases
PCPDTBT
within
blends.
These
markedly
different
compared
neat
materials,
highlighting
unique
interactions
between
polymer
matrix
AuNPs.
Additionally,
explored
optical
properties,
surface
morphology,
crystallinity
materials.
We
hypothesize
that
observed
variations
transitions,
well
improvement
properties
crystallinity,
are
likely
influenced
by
localized
plasmon
resonance
(LSPR)
passivation
phenomena
induced
composite
films.
findings
could
important
implications
design
optimization
optoelectronic
applications.
Language: Английский
Deformation Characterization of Glass Fiber and Carbon Fiber-Reinforced 3D Printing Filaments Using Digital Image Correlation
Polymers,
Journal Year:
2025,
Volume and Issue:
17(7), P. 934 - 934
Published: March 29, 2025
The
paper
offers
an
in-depth
deformation
study
of
glass
fiber-reinforced
and
carbon
composite
filaments
3D
printers.
During
the
certification,
authors
used
DIC
(Digital
Image
Correlation)
as
a
full-field
strain
measurement
technique
to
explore
key
material
traits
non-contact
optical
method.
insights
captured
through
technology
enabled
better
understand
localized
distributions
during
loading
these
reinforced
filaments.
analyzes
fiber
in
printing
that
are
with
materials
subjected
bending
compressive
loading.
segment
presents
how
affects
performance
when
varying
such
factors
deposition
patterns,
layer
orientation,
other
process
parameters.
Different
types
combinations
reinforcements
variables
were
tested,
resulting
dependencies
mechanical
parameters
failure
modes
established
for
each
case.
Key
conclusions
demonstrate
behavior
both
carbon-
is
strongly
affected
by
parameters,
particularly
infill
density,
pattern,
build
orientation.
application
Digital
Correlation
(DIC)
allowed
precise,
analysis
distribution
behavior,
offering
new
into
structural
printed
composites.
findings
from
provide
guidance
proper
choice
filling
optimal
models
high-performance
indexes
seamless
applications
automotive
industrial
manufacturing
sectors.
Language: Английский
ML-Based Materials Evaluation in 3D Printing
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(10), P. 5523 - 5523
Published: May 15, 2025
Machine
learning
(ML)
is
transforming
the
evaluation
of
3D
printing
materials,
enabling
more
efficient
and
accurate
assessment
material
properties,
including
their
sustainable
life
cycle.
ML
algorithms
can
analyze
vast
amounts
data
from
previous
processes
to
predict
performance
different
materials
(including
those
used
in
multi-material
printing)
under
conditions.
This
predictive
ability
helps
selecting
most
suitable
for
specific
tasks,
optimizing
mechanical,
chemical,
overall
quality
final
product.
Furthermore,
by
integrating
real-time
sensors
during
process,
continuously
monitor
adjust
parameters,
ensuring
optimal
utilization
reducing
waste.
models
identify
correct
defects
printed
recognizing
patterns
associated
with
defects,
thus
improving
reliability
3D-printed
objects.
approach
reduces
need
expensive
time-consuming
physical
tests.
accelerates
pace
development
but
also
increases
precision
selection
processing,
contributing
use
energy
printing.
Language: Английский