Reconstruction of residual stresses in additively manufactured Inconel 718 bridge structures using contour method
The International Journal of Advanced Manufacturing Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 25, 2025
Язык: Английский
Improving the strength properties of PLA acetabular liners by optimizing FDM 3D printing: Taguchi approach and finite element analysis validation
The International Journal of Advanced Manufacturing Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 7, 2025
Язык: Английский
Dimensional and geometric deviation modelling for polycarbonate parts fabricated by fused filament fabrication-a machine learning approach
International Journal on Interactive Design and Manufacturing (IJIDeM),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 5, 2025
Язык: Английский
Mechanical behavior of composite pipe structures under compressive force and its prediction using different machine learning algorithms
Materials Testing,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 3, 2024
Abstract
Thanks
to
machine
learning
algorithms,
the
performance
of
composites
with
high
energy
absorption
capacity
can
be
predicted
accuracy
rates
a
small
number
data.
The
aim
this
study
is
experimentally
and
numerically
determine
crushing
performances
glass/epoxy
composite
pipe
structures
under
compressive
force
predict
their
compression
behavior
help
different
algorithms.
In
study,
pipes
(peak
(PF),
peak
displacement
(PFD),
mean
(MCF),
specific
(SEA),
total
inner
(TIE))
were
determined
for
specimen
thicknesses,
lengths,
mesh
sizes,
numbers
integration
points,
diameters
(
D
),
directions
(axial
radial).
Additionally,
maximum
strength
values
estimated
Linear
Regression
(LR),
K-Nearest
Neighbors
(KNN),
Artificial
Neural
Networks
(ANN)
data
taken
from
ANN
algorithm
found
more
reliable
in
estimating
PF
TIE
values,
an
rate
92
%.
When
determining
MCF
value,
it
was
that
obtained
LR
than
other
80
Язык: Английский
Design optimization for inner core of crash box for vehicle based on NPR/PU structure
Mechanics of Advanced Materials and Structures,
Год журнала:
2024,
Номер
unknown, С. 1 - 14
Опубликована: Ноя. 14, 2024
Vehicle
crashworthiness
is
a
critical
aspect
of
the
passive
safety
domain
in
passenger
cars,
and
crash
boxes
play
significant
role
vehicle
collisions.
Currently,
predominantly
utilized
vehicles
are
primarily
simple
thin-walled
structures,
which
exhibit
average
energy-absorbing
capabilities.
To
enhance
collision
safety,
this
article
proposes
an
inner
core
filled
with
negative
Poisson's
ratio
(NPR)
structure
polyurethane
(PU)
material
to
design
box.
Initially,
double-arrow
type
NPR
selected
as
framework,
serving
filling
material.
This
combination
forms
A
analysis
conducted
on
three
types
boxes,
examining
differences
their
performance
indicators
detail
demonstrate
superiority
proposed
design.
Subsequently,
variables
that
significantly
influence
evaluation
metrics
were
identified
through
extreme
value
difference
analysis,
these
designated
parameters
for
subsequent
optimization
process.
Finally,
Neighborhood
Cultivation
Genetic
Algorithm
(NCGA)
Non-dominated
Sorting
Algorithm-II
(NSGA-II)
employed
algorithms
optimal
design,
results
two
determined
separately
using
Normal
Boundary
Intersection
(NBI)
method,
then
compared
determine
overall
solution.
The
simulation
indicate
NSGA-II
optimized
NPR/PU
box
provides
substantial
advantages
performance.
After
optimization,
exhibits
reduced
collapse
displacement
maximum
peak
force
other
along
enhanced
specific
energy
absorption
capacity.
These
findings
designed
improves
vehicle's
event
collision.
offers
valuable
theoretical
insights
support
development
exploration
automotive
boxes.
Язык: Английский
Enhanced crashworthiness performance of auxetic structures using artificial neural network and geyser inspired algorithm
Materials Testing,
Год журнала:
2024,
Номер
67(2), С. 353 - 360
Опубликована: Дек. 18, 2024
Abstract
This
study
focuses
on
the
optimum
design
of
an
auxetic
energy
absorber
intended
for
automobile
applications.
The
material
chosen
this
is
SCGA27D
galvanized
steel.
research
proposes
utilization
artificial
neural
network-assisted
metaheuristic
optimizing
structural
components.
geyser
inspired
algorithm
(GEA),
ship
rescue
algorithm,
and
mountain
gazelle
are
employed
to
optimize
absorber.
objective
problem
obtain
optimal
geometry
while
simultaneously
reducing
mass
meeting
absorption
constraints.
findings
demonstrate
that
both
GEA
steel
exhibit
exceptional
capabilities
in
designing
vehicle
structures.
Язык: Английский
Advanced structural design of engineering components utilizing an artificial neural network and GNDO algorithm
Materials Testing,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 26, 2024
Abstract
In
today’s
competitive
environment,
the
lightweighting
of
vehicle
components
is
under
intense
study.
While
some
these
studies
focus
on
material
modification,
a
very
important
part
focuses
same
material.
The
most
widely
used
techniques
in
light-weight
are
topology,
topography,
size,
shape
optimization,
and
metaheuristic
algorithms.
This
work
introduces
novel
hybrid
generalized
normal
distribution
optimization
(GNDO)
simulated
annealing
algorithm
(GNDO-SA)
adapted
to
optimize
component
made
aluminum
which
aims
minimize
weight
while
ensuring
that
stress
constraints
met.
A
combination
latin
hypercube
sampling
(LHS)
artificial
neural
network
generate
mathematical
equations
governing
for
objective/constraint
optimization.
These
findings
highlight
effectiveness
superiority
GNDO-SA
method
problems.
Язык: Английский