Numerical calculation of high frequency induction heating for complex hull plate considering deflection
Thin-Walled Structures,
Год журнала:
2025,
Номер
unknown, С. 113158 - 113158
Опубликована: Март 1, 2025
Язык: Английский
DNN-Based Inverse Design of Line Heating Patterns for Automated Plate Forming in Shipbuilding Using Multi-Start Convex Optimization
Extreme Mechanics Letters,
Год журнала:
2025,
Номер
unknown, С. 102313 - 102313
Опубликована: Март 1, 2025
Язык: Английский
Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm
Journal of Marine Science and Engineering,
Год журнала:
2024,
Номер
12(2), С. 255 - 255
Опубликована: Янв. 31, 2024
The
application
of
titanium
alloy
in
shipbuilding
can
reduce
ship
weight
and
carbon
emissions.
To
solve
the
problem
forming,
deformation
prediction
line
heating
based
on
a
backpropagation
(BP)
neural
network
sparrow
search
algorithm
(SSA)
was
researched.
Based
thermal–elastic–plastic
finite
element
method,
numerical
calculation
model
TA5
overlapping
forming
established.
feasibility
verified
by
comparing
it
with
experiment
low-carbon
steel.
Considering
characteristics
alloy-forming
process,
73
groups
schemes
were
obtained
Latin
hypercube
sampling
method.
data
samples
using
forming.
methods
BP,
genetic
algorithm–backpropagation
(GA-BP),
SSA-BP
proposed.
accuracy
different
models
analyzed.
mean
absolute
percentage
errors
(MAPEs)
GA-BP,
shrinkage
7.45%,
4.08%,
2.96%,
respectively.
MAPEs
deflection
8.44%,
4.73%,
2.64%,
goodness
fit
(R2)
is
closest
to
1
among
three
models.
results
show
that
better
than
BP
GA-BP
predicting
alloy.
maximum
error
4.95%,
which
within
allowable
range
engineering
error.
suitable
for
rapid
accurate
intelligent
provides
support
decisions
Язык: Английский
Towards the Automation of Plate Forming Process for Shipbuilding: A Dnn-Based Multi-Start Convex Optimization Framework for the Prompt Inverse Design of Line Heating Patterns
Опубликована: Янв. 1, 2024
Line
heating
is
a
plate
forming
method,
predominantly
used
in
shipbuilding
industry,
to
bend
and
twist
steel
into
desired
shape
by
the
along
appropriate
line
paths.
The
design
of
patterns
current
industry
generally
performed
manually
heuristically
determined
paths,
due
highly
non-linear
relationship
between
pattern
resultant
deformation.
This
process
relies
heavily
on
expertise
experience
skilled
workers,
which
significantly
deteriorates
both
productivity
quality
end
products.
Consequently,
significant
efforts
are
being
made
develop
systematic
way
determine
optimal
solution
for
deformations.
Nevertheless,
compared
other
inverse
approaches,
development
framework
this
task
proves
far
more
challenging,
as
can
be
industrially
practical
only
if
it
'promptly'.
In
article,
we
propose
data-driven
inverse-design
that
swiftly
identifies
achieving
specific
deformation,
given
initial
geometry.
To
model
complex
geometry,
pattern,
trained
surrogate
based
Deep
Neural
Network
(DNN)
with
dataset
generated
from
finite
element
method
(FEM).
could
instantaneously
predict
deformed
geometry
any
configuration
within
our
space.
Utilizing
forward
prediction
model,
perform
multi-start
convex
optimization
allow
us
deform
plates
final
shape.
proposed
easily
adapted
various
engineering
problems
require
finding
constrained
timeframe.
Язык: Английский
Precision detection method for ship shell plate molding based on neural radiance field
Ocean Engineering,
Год журнала:
2024,
Номер
309, С. 118459 - 118459
Опубликована: Июнь 17, 2024
Язык: Английский
Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight
Materials,
Год журнала:
2023,
Номер
16(17), С. 6028 - 6028
Опубликована: Сен. 1, 2023
The
application
of
high-strength
steel
plates
can
reduce
ship
weight,
and
the
saddle
plate
is
one
most
common
types
double-curved
hull
plates.
To
fill
research
gap
regarding
plates,
two
prediction
models
are
established
here
to
predict
deformation
in
forming.
Deflection
a
key
parameter
reflecting
overall
curved
plate.
Therefore,
first
all,
influencing
factors
line
heating
were
analyzed.
influence
geometric
parameters
forming
on
deflection
was
researched.
Second,
multiple
linear
regression
model
between
established.
Finally,
solve
problem
large
error
multivariate
for
extrapolation,
an
intelligent
program
based
support
vector
machine
(SVM)
developed
using
Python
language.
results
show
that
less
than
5%
data
interpolation.
extrapolation.
This
provide
automatic
marine
Язык: Английский
Numerical Simulation and Microstructure Analysis of 30CrMnMoRe High-Strength Steel Welding
Materials,
Год журнала:
2024,
Номер
17(17), С. 4415 - 4415
Опубликована: Сен. 7, 2024
Welding
experiments
were
conducted
under
different
currents
for
single-pass
butt
welding
of
high-strength
steel
flat
plates.
The
microstructure
welded
joints
was
characterized
using
OM,
SEM,
and
EBSD,
the
process
numerically
simulated
a
finite
element
method.
According
to
grain
size
obtained
by
electron
microscope
characterization
temperature
data
simulation,
mechanical
properties
coarse
fine
areas
heat-affected
zone
predicted
material
property
simulation
software.
Finally,
results
verified
through
testing.
Язык: Английский
A Study on the Effects of Cold Deformation on CMnSi Steel Structures Utilised in the Shipbuilding Industry
Polish Maritime Research,
Год журнала:
2024,
Номер
31(3), С. 135 - 141
Опубликована: Авг. 21, 2024
Abstract
This
article
analyses
the
effects
of
deformation
on
structure
CMnSi
steel
at
various
levels.
After
hot
forging,
comprises
coarse-sized
alpha
and
pearlite
particles.
The
average
grain
size
after
forging
was
100
μm.
rolling,
gradually
decreases,
with
ferrite
grains
measured
as
60
that,
subjected
to
cold
levels
40%,
60%,
80%.
sample
80%
reached
level
7,
corresponding
about
25
For
a
5,
40
μm,
while
60%
produced
35
6.
In
addition,
scanning
electron
microscopy
showed
that
deformation,
smaller
particles
5
μm
appear
inside
parent
Moreover,
energy-dispersive
X-ray
spectroscopy
analysis
revealed
carbide
appearance
in
form
M23C6,
M
being
mixture
Fe
Mn.
These
carbides
have
fine
1–2
contribute
prevention
particle
growth
during
subsequent
heat
treatments.
Язык: Английский
Precision Detection Method for Ship Shell Plate Molding Based on Neural Radiance Field
Опубликована: Янв. 1, 2023
In
this
work,
an
integrated
neural
radiance
field
model
ensemble
measurement
system
was
developed
to
measure
the
ship
shell
plate
molding
accuracy
detection.
This
is
first
work
that
uses
solve
issue
of
The
Instant-NGP
deployed
reconstruct
3D
plate,
which
significantly
reduced
training
time
and
ensured
high
reconstruction
accuracy.
An
image
acquisition
constructed
experiments
were
carried
out
on
three
different
sizes
plates.
accuracy,
efficiency,
completeness
proposed
method
evaluated.
results
show
point
cloud
accomplished
in
about
2.5
minutes,
average
error
less
than
0.2mm.
Based
experimental
results,
optimal
parameters
data
set
for
are
given.
Compared
with
wooden
formwork
method,
active
binocular
vision
based
MVSNet,
our
approach
has
advantages
precision,
low
cost,
proves
flexibility
robustness.
Язык: Английский