Computer Science Integrations with Laser Processing for Advanced Solutions
Photonics,
Journal Year:
2024,
Volume and Issue:
11(11), P. 1082 - 1082
Published: Nov. 18, 2024
This
article
examines
the
role
of
computer
science
in
enhancing
laser
processing
techniques,
emphasizing
transformative
potential
their
integration
into
manufacturing.
It
discusses
key
areas
where
computational
methods
enhance
precision,
adaptability,
and
performance
operations.
Through
advanced
modeling
simulation
a
deeper
understanding
material
behavior
under
irradiation
was
achieved,
enabling
optimization
parameters
reduction
defects.
The
intelligent
control
systems,
driven
by
machine
learning
artificial
intelligence,
examined,
showcasing
how
real-time
data
analysis
adjustments
lead
to
improved
process
reliability
quality.
utilization
computer-generated
diffractive
optical
elements
(DOEs)
emphasized
as
means
precisely
beam
characteristics,
thus
broadening
application
opportunities
across
various
industries.
Additionally,
significance
predictive
analyses
manufacturing
effectiveness
sustainability
is
discussed.
While
challenges
such
need
for
specialized
expertise
investment
new
technologies
persist,
this
underscores
considerable
advantages
integrating
with
processing.
Future
research
should
aim
address
these
challenges,
further
improving
quality,
processes.
Language: Английский
Development of a learner model tool for predicting strength and embodied carbon for lightweight concrete production
Journal of Building Engineering,
Journal Year:
2024,
Volume and Issue:
95, P. 110330 - 110330
Published: Aug. 3, 2024
The
demand
for
sustainable
concrete
in
meeting
the
net
zero
carbon
target
places
a
burden
optimizing
response
to
structural
strength
that
satisfy
acceptable
embodied
carbon.
In
most
cases,
low
is
deficient
requirement
and
vice
versa.
This
dilemma
informs
need
tool
can
predict
compressive
as
well
using
same
input
data.
Since
use
of
alternative
materials
cement
replacement
enhance
sustainability
emerging
quest
concrete,
an
optimal
material
both
conditions
integrity
still
lacking.
Paucity
data
lightweight
materials,
portends
upheave
bias
prediction
behaviour
concrete.
study
therefore
uses
from
laboratory
experiment
with
their
performance
evaluated
eight
machine
leaning
regression
models.
results
obtained
indicates
XG
boost
model
exhibited
excellent
Mean
Squared
Error
(MSE)
50.15,
absolute
error(MAE)
=
5.26,
percentage
error(MAPE)
11.76
%,
Explained
variance
score
0.97,
Root
mean
square
error(RMSE)
7.08
high
R
squared
value
0.96.
predicted
multiple
output
such
be
limited
yearly
threshold
achieving
2050
target.
developed
when
compared
similar
mix
ingredients
performed
more
than
95
%
predicting
associated
line
inclusion
regulations
buildings
UK
suggested
by
professionals
construction
industry,
learner
has
integrated
initiate
holistic
approach
design
construction,
balancing
performance,
cost,
environmental
impact.
Language: Английский
Investigation of Laser Ablation Quality Based upon Entropy Analysis of Data Science
Chien-Chung Tsai,
No information about this author
Tung-Hon Yiu
No information about this author
Entropy,
Journal Year:
2024,
Volume and Issue:
26(11), P. 909 - 909
Published: Oct. 27, 2024
Laser
ablation
is
a
vital
material
removal
technique,
but
current
methods
lack
data-driven
approach
to
assess
quality.
This
study
proposes
novel
method,
employing
information
entropy,
concept
from
data
science,
evaluate
laser
By
analyzing
the
randomness
associated
with
process
through
distribution
of
probability
value
(reb),
we
quantify
uncertainty
(entropy)
ablation.
Our
research
reveals
that
higher
energy
levels
lead
lower
signifying
more
controlled
and
predictable
process.
Furthermore,
using
an
interval
time
closer
baseline
improves
consistency.
Additionally,
analysis
suggests
level
has
stronger
correlation
entropy
than
(bit).
The
decreased
by
6.32
12.94
at
0.258
mJ
6.62
0.378
mJ,
while
change
due
bit
was
only
2.12
(from
10.84
bit/2
8.72
bit).
indicates
dominant
factor
for
predicting
Overall,
this
work
demonstrates
feasibility
evaluating
ablation,
paving
way
optimizing
parameters
achieving
precise
Language: Английский
Image-Driven Laser Ablation Optimization
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Feb. 26, 2024
Abstract
This
study
presents
the
development
of
optimal
nanostructures
on
surfaces
post-laser
ablation
processinga
software
solution
aimed
at
enhancing
efficiency
this
process.
Leveraging
image
processing
techniques,
particularly
images
from
Scanning
Electron
Microscopy
(SEM),
optimizes
laser
system
parameters
to
achieve
desired
nanostructure
morphology.
By
integrating
analysis
with
optimization,
approach
offers
a
for
improving
precision
and
efficacy
fabrication.
The
proposed
methodology
holds
significant
promise
advancing
processes
in
material
engineering
nanophotonics.
Language: Английский
Commodity Dynamic Pricing and Replenishment Decision Model Based on Cosine Annealing
Junyi Zhao,
No information about this author
Chenye Xi,
No information about this author
Gong Chen
No information about this author
et al.
Highlights in Science Engineering and Technology,
Journal Year:
2024,
Volume and Issue:
98, P. 270 - 279
Published: May 16, 2024
The
purpose
of
this
paper
is
to
analyze
the
relationship
between
commodity
sales
volume,
types,
time,
etc.,
and
propose
a
decision
model
predict
future
data
through
historical
determine
reasonable
pricing
strategy.
First,
collected
preprocessed.
Through
correlation
analysis,
most
important
influencing
factors
are
obtained.
construction
XGBoost
influence
obtained,
wholesale
prices
chili
products
(yuan/kg)
from
July
1
7,
2023
3.63,
6.68,
6.90,
5.44,
6.62,
respectively.
In
order
further
develop
strategy,
constructs
based
on
cosine
annealing
algorithm
combined
with
dynamic
algorithm.
model,
it
calculated
that
2023,
restocking
quantity
(kg)
234.20,
95.85,
110.04,
179.77,
176.60,
179.77.
Profit
(Yuan)
148.81,
298.96,
347.82,
370.75,
287.54,
356.33,
383.00.
By
comparing
actual
data,
error
small
robustness
high,
which
provides
an
effective
decision-making
for
supermarkets
formulate
Language: Английский
Research on the Impact of Momentum on Game Situations Based on Random Forest and XGBoost Models
Haiyang Qiu
No information about this author
Highlights in Science Engineering and Technology,
Journal Year:
2024,
Volume and Issue:
100, P. 135 - 141
Published: May 22, 2024
The
momentum
in
the
realm
of
sports
is
like
an
intangible
force,
unleashed
by
a
sequence
events.
During
game,
team
or
player
may
feel
they
are
riding
this
wave
as
if
victory
within
reach.
However,
formation
and
its
impact
on
outcome
game
remains
mystery.
To
study
factors
that
influence
explore
their
it,
ultimately
understanding
how
affect
course
This
establishes
Random
Forest
Model
to
calculate
weights
these
independent
variables
which
we
use
define
XGboost
model
detect
combined
with
using
SHAP
Values
analyze
different
feature
quantities
match
fluctuations
learn
about
variable
parameter
changes
results.
Finally,
improve
strategic
decision-making
through
genetic
algorithm
optimization
based
find
optimal
allocation
indicators
maximize
momentum.
research
has
ability
help
us
figure
out
significance
exploration
competitive
scenarios
optimize
Language: Английский