Machine tool model correction assisted by dynamic evolution sequence
Abstract
In
the
simulation
analysis
of
large-scale
industrial
instruments
such
as
machine
tools,
in
order
to
ensure
accuracy,
model
parameter
correction
is
necessary.
This
research
presents
a
tool
method
assisted
by
dynamic
evolution
sequence
(DES).
The
first
introduces
generate
uniformly
distributed
sequence,
replacing
traditional
used
Kriging
surrogate
models,
and
constructing
more
accurate
for
tools.
Additionally,
incorporating
instead
random
improves
search
space
coverage
Heterogeneous
Comprehensive
Learning
Particle
Swarm
Optimization
(HCLPSO)
algorithm.
results
numerical
examples
demonstrate
that
finite
element
model,
corrected
using
proposed
method,
accurately
predicts
true
displacement
responses
tool.
offers
new
solution
addressing
static
problems.
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 27, 2024
Язык: Английский