Sustainability,
Journal Year:
2024,
Volume and Issue:
16(3), P. 1009 - 1009
Published: Jan. 24, 2024
From
the
perspective
of
innovation
manufacturing
links,
this
paper
conducted
research
on
current
situation
development
and
relationship
between
regional
economy
digital
transformation,
aiming
to
offer
suggestions
reference
for
relevant
policy
making.
Firstly,
taking
INCOPAT
patent
database
as
data
source,
a
quantitative
analysis
was
five
key
links
in
industry,
which
obtained
characteristics
industry
from
link
innovation.
Then,
based
economic
panel
regions
China,
coupling
coordination
investigate
transformation
coordinated
2017
2021.
The
level
characteristic
relations
31
provinces
or
cities
these
two
systems
were
analyzed.
On
whole,
China
is
steadily
rising
but
varies
among
different
regions,
that
is,
economically
developed
tend
have
better
development.
In
general,
highly
relates
Moreover,
speed
tends
be
stable
with
types
should
formulate
corresponding
policies
accelerate
This
work
aims
to
explore
the
impact
of
Digital
Twins
Technology
on
industrial
manufacturing
in
context
Industry
5.0.
A
computer
is
used
search
Web
Science
database
summarize
First,
background
and
system
architecture
5.0
are
introduced.
Then,
potential
applications
key
modeling
technologies
discussd.
It
found
that
equipment
infrastructure
scenarios,
embedded
intelligent
upgrade
for
a
primary
condition.
At
same
time,
can
provide
automated
real-time
process
analysis
between
connected
machines
data
sources,
speeding
up
error
detection
correction.
In
addition,
bring
obvious
efficiency
improvements
cost
reductions
manufacturing.
reflects
its
application
value
subsequent
through
prospect.
hoped
this
relatively
systematic
overview
technical
reference
development
improvement
entire
business
Industrial
X.0
era.
Materials & Design,
Journal Year:
2024,
Volume and Issue:
244, P. 113086 - 113086
Published: June 25, 2024
Additive
manufacturing
(AM)
has
undergone
significant
development
over
the
past
decades,
resulting
in
vast
amounts
of
data
that
carry
valuable
information.
Numerous
research
studies
have
been
conducted
to
extract
insights
from
AM
and
utilize
it
for
optimizing
various
aspects
such
as
process,
supply
chain,
real-time
monitoring.
Data
integration
into
proposed
digital
twin
frameworks
application
machine
learning
techniques
is
expected
play
pivotal
roles
advancing
future.
In
this
paper,
we
provide
an
overview
twin-assisted
AM.
On
one
hand,
discuss
domain
highlight
machine-learning
methods
utilized
field,
including
material
analysis,
design
optimization,
process
parameter
defect
detection
monitoring,
sustainability.
other
examine
status
current
technical
approach
offer
future
developments
perspectives
area.
This
review
paper
aims
present
convergence
big
data,
learning,
Although
there
are
numerous
papers
on
additive
others
twins
AM,
no
existing
considered
how
these
concepts
intrinsically
connected
interrelated.
Our
first
integrate
three
propose
a
cohesive
framework
they
can
work
together
improve
efficiency,
accuracy,
sustainability
processes.
By
exploring
latest
advancements
applications
within
domains,
our
objective
emphasize
potential
advantages
possibilities
associated
with
technologies
Internet of Things,
Journal Year:
2024,
Volume and Issue:
25, P. 101094 - 101094
Published: Jan. 29, 2024
As
Industry
4.0
enablers,
digital
twins
of
manufacturing
systems
have
led
to
multiple
interaction
levels
among
processes,
systems,
and
workers
across
the
factory.
However,
open
issues
still
exist
when
addressing
cyber–physical
convergence
in
traditional
small
medium-sized
enterprises.
The
problem
for
both
operators
existing
infrastructure
is
how
adapt
knowledge
increasing
business
needs
plants
that
demand
high
efficiency,
while
reducing
production
costs.
In
this
paper,
a
framework
implements
novel
concept
Digital
Twin
Learning
Ecosystem
presented.
objective
facilitate
integration
human-machine
different
industrial
contexts
eliminate
technological
workforce
barriers.
This
adaptive
approach
particularly
important
meeting
requirements
help
enterprises
build
their
own
interconnected
Ecosystem.
contribution
work
lies
single
twin
learning
scenarios
can
from
scratch
using
light
infrastructure,
reusing
common
condition-based
methods
well-known
by
skilled
rapidly
flexibly
integrate
legacy
resources
non-intrusive
manner.
solution
was
tested
real
data
milling
machine
currently
operating
induction
furnace
with
maximum
power
12
MW
foundry
plant.
cases,
proposed
proved
its
benefits:
first,
providing
augmented
maintenance
operations
on
second,
improving
efficiency
approximately
9
percent.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 71113 - 71126
Published: Jan. 1, 2023
Additive
manufacturing
is
a
promising
process
with
diverse
applications,
but
ensuring
the
quality
and
reliability
of
manufactured
products
key
challenge.
The
digital
twin
has
emerged
as
technology
solution
to
address
this
challenge,
allowing
real-time
monitoring
control
process.
This
paper
proposes
system
framework
for
additive
that
integrates
machine
learning
models,
employing
Unity,
OctoPrint,
Raspberry
Pi
monitoring.
Particularly,
utilizes
models
defect
detection,
achieving
an
Average
Precision
(AP)
score
92%,
specific
performance
metrics
91%
defected
objects
94%
non-defected
objects,
demonstrating
high
efficiency.
Unity
client
user
interface
also
developed
visualization,
facilitating
easy
research
article
presents
detailed
description
proposed
its
workflow
implementation,
interface.
It
demonstrates
effectiveness
integrated
through
case
studies
experimental
results.
main
findings
show
met
functional
requirements
effectively
detects
defects
provides
contributes
growing
field
manufacturing,
providing
enhancing
in
manufacturing.
Information and Software Technology,
Journal Year:
2024,
Volume and Issue:
169, P. 107424 - 107424
Published: Feb. 14, 2024
Digital
twin
(DT)
ecosystems
are
rapidly
evolving,
connecting
many
stakeholders,
such
as
manufacturers,
customers,
and
application
platform
providers.
These
require
collaboration
interaction
between
diverse
actors
to
create
value.
This
study
delves
into
the
of
stakeholders
within
DT-focused
ecosystems.
research
aims
understand
stakeholder
DT
ecosystems,
identify
potential
challenges,
provide
insights
for
managing
these
stakeholders.
It
also
seeks
define
ecosystem
its
implications
both
practice.
A
systematic
literature
review
was
conducted,
supplemented
by
empirical
evidence
gathered
from
interviews
with
experts
who
were
knowledgeable
about
ecosystem.
The
analyzed
systems,
roles,
challenges
ecosystem-focused
development.
identified
various
their
roles
in
adding
value
a
highlighted
benefits
collaboration,
knowledge
gain
during
system
revealed
technical
non-technical
encountered
DTs,
emphasizing
importance
standardization
solution.
new
definition
proposed,
data-driven
nature,
interconnected
creation,
technology
enablement.
Stakeholder
is
pivotal
each
actor
playing
distinct
role.
Addressing
especially
through
(OPC
UA
ISO
23247),
can
lead
more
efficient
coherent
provided
this
guide
industries
designing,
developing,
maintaining
ensuring
creation
satisfaction.
Future
avenues
that
emphasize
understanding
involved
deploy
appropriate
solutions
suggested.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(4), P. 646 - 646
Published: Feb. 7, 2025
Digital
twins
(DTs)
represent
a
transformative
technology
in
manufacturing,
facilitating
significant
advancements
monitoring,
simulation,
and
optimization.
This
paper
offers
an
extensive
bibliographic
review
of
AI-Based
DT
applications,
categorized
into
three
principal
dimensions:
operator,
process,
product.
The
operator
dimension
focuses
on
enhancing
safety
ergonomics
through
intelligent
assistance,
utilizing
real-time
monitoring
artificial
intelligence,
notably
human–robot
collaboration
contexts.
process
application
concerns
itself
with
optimizing
production
flows,
identifying
bottlenecks,
dynamically
reconfiguring
systems
predictive
models
simulations.
Lastly,
the
product
emphasizes
applications
focused
improvements
design
quality,
employing
lifecycle
historical
data
to
satisfy
evolving
market
requirements.
categorization
provides
structured
framework
for
analyzing
specific
capabilities
trends
DTs,
while
also
knowledge
gaps
contemporary
research.
highlights
key
challenges
technological
interoperability,
integration,
high
implementation
costs
emphasizing
how
digital
twins,
supported
by
AI,
can
drive
transition
toward
sustainable,
human-centered
manufacturing
line
Industry
5.0.
findings
provide
valuable
insights
advancing
state
art
exploring
future
opportunities
twin
applications.
