Applied Sciences,
Journal Year:
2021,
Volume and Issue:
11(24), P. 11949 - 11949
Published: Dec. 15, 2021
In
Laser
Wire
Additive
Manufacturing
(LWAM),
the
final
geometry
is
produced
using
layer-by-layer
deposition
(beads
principle).
To
achieve
good
geometrical
accuracy
in
product,
proper
implementation
of
bead
essential.
For
this
reason,
paper
focuses
on
process
and
proposes
a
layer
(width
height)
prediction
model
to
improve
accuracy.
More
specifically,
machine
learning
regression
algorithm
applied
several
experimental
data
predict
across
layers.
Furthermore,
neural
network-based
approach
was
used
study
influence
different
parameters,
namely
laser
power,
wire-feed
rate
travel
speed
geometry.
validate
effectiveness
proposed
approach,
test
split
validation
strategy
train
models.
The
results
show
particular
evolutionary
trend
confirm
that
parameters
have
direct
geometry,
so,
too,
part.
Several
been
found
obtain
an
accurate
with
low
errors
deposition.
Finally,
indicates
can
efficiently
be
could
help
later
designing
controller
LWAM
process.
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
3(1), P. 200106 - 200106
Published: Jan. 18, 2024
Additive
manufacturing
(AM)
is
a
widely
applied
paradigm
used
for
the
layer-by-layer
fabrication
of
desired
components
and
objects,
especially
those
with
highly
intricate
geometry.
Extrusion-based
AM,
which
subcategory
AM
processing
technologies,
characterized
by
facilitation
controlled
successive
deposition
feedstock
materials
through
nozzles
printer
heads
onto
print
bed.
enables
design
freedom
but
offers
cost
efficiency
process
simplicity
when
compared
to
other
categories
i.e.
liquid-
powder-based
technologies.
The
extrusion-based
has
become
increasingly
widespread
over
last
two
decades
because
expanding
material
options
that
can
be
in
this
technology,
its
capacity
hybridised
addition
multiple
printheads
or
incorporation
into
secondary
system.
Despite
promising
aspects
process,
increasing
demands
customised
printed
products
an
range
create
both
material-
process-related
challenges
limit
suitability
processes
some
specific
applications.
Consequently,
principal
objective
review
paper
conduct
analysis
processes.
follows
discussion
about
assessment
easy-
hard-to-print
materials.
This
paper,
therefore,
provides
comprehensive
each
while
also
providing
ideas
improving
their
current
levels.
findings
ratings
reported
importantly
viewpoints
would
support
better
futuristic
comparisons
between
developed
developing
processes,
as
businesses
look
adopt
right
solutions.
Macromolecular Materials and Engineering,
Journal Year:
2021,
Volume and Issue:
306(11)
Published: Aug. 18, 2021
Abstract
Conductive
polymer
composites
(CPCs)
of
carbon
nanotubes
(CNTs)
and
graphite
nanosheet
(GNP)‐filled
thermoplastic
polyurethane
(TPU)
are
3D‐printed
into
flexible
piezoresistive
sensors
via
fused
filament
fabrication.
The
sensor,
with
a
customized
lever‐cross
structure,
allows
detection
stretching
out‐of‐plane
forces
different
magnitudes
frequencies.
force
direction
is
obtained
by
combing
the
relative
electrical
resistance
change
in
cross
section
sensor
analysis.
75‐CNT/25‐GNP
(CNT‐to‐GNP
mass
ratio
75%‐to‐25%)
demonstrates
excellent
sensing
performance
at
total
nanoparticle
loading
3
wt%.
linearity
0.98,
while
those
100‐CNT
50‐CNT/50‐GNP
0.93
0.86,
respectively.
gauge
factor
52%
higher
than
that
its
strain
range
79%
above
sensor.
Excellent
stability
demonstrated
for
after
1500
(out‐of‐plane
force)
cycles.
synergistic
effect
CNTs
GNPs
on
clearly
shown
this
study.
Applied Sciences,
Journal Year:
2021,
Volume and Issue:
11(24), P. 11949 - 11949
Published: Dec. 15, 2021
In
Laser
Wire
Additive
Manufacturing
(LWAM),
the
final
geometry
is
produced
using
layer-by-layer
deposition
(beads
principle).
To
achieve
good
geometrical
accuracy
in
product,
proper
implementation
of
bead
essential.
For
this
reason,
paper
focuses
on
process
and
proposes
a
layer
(width
height)
prediction
model
to
improve
accuracy.
More
specifically,
machine
learning
regression
algorithm
applied
several
experimental
data
predict
across
layers.
Furthermore,
neural
network-based
approach
was
used
study
influence
different
parameters,
namely
laser
power,
wire-feed
rate
travel
speed
geometry.
validate
effectiveness
proposed
approach,
test
split
validation
strategy
train
models.
The
results
show
particular
evolutionary
trend
confirm
that
parameters
have
direct
geometry,
so,
too,
part.
Several
been
found
obtain
an
accurate
with
low
errors
deposition.
Finally,
indicates
can
efficiently
be
could
help
later
designing
controller
LWAM
process.
