A knowledge graph-based intelligent planning method for remanufacturing processes of used parts
Journal of Engineering Design,
Год журнала:
2025,
Номер
unknown, С. 1 - 28
Опубликована: Янв. 12, 2025
Intelligent
remanufacturing
process
planning
is
crucial
for
the
efficient
and
high-quality
of
used
parts
with
complex
failure
characteristics.
However,
due
to
varied
characteristics
parts,
diversity
processes,
non-linear
relationships
among
elements,
relying
solely
on
mathematical
programming
or
manual
empirical
difficult
effectively
model
optimise
planning.
To
this
end,
a
knowledge
graph-based
intelligent
method
processes
proposed
enhance
efficiency
quality
by
combining
reuse.
Firstly,
as
decision
nodes,
full-element
ontology
constructed,
linking
characteristics,
corresponding
plans.
The
BERT-BiLSTM-CRF
extracts
entities,
graph
(RPKG)
constructed.
Secondly,
an
decision-making
based
multi-node
path
retrieval
proposed.
Aim
minimise
carbon
emissions,
time,
cost,
feature
similarity
calculations
nearest
neighbour
search
(NNS)
efficiently
retrieve
optimal
plan
each
characteristic.
Then,
plans
are
merged
constraints
create
complete
plan.
Finally,
concrete
case
given
verify
effectiveness
advantages
method.
Язык: Английский
Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station
Processes,
Год журнала:
2024,
Номер
12(7), С. 1412 - 1412
Опубликована: Июль 6, 2024
In
a
hydropower
station,
equipment
needs
maintenance
to
ensure
safe,
stable,
and
efficient
operation.
And
the
essence
of
is
disassembly
sequence
planning
problem.
However,
complexity
arises
from
vast
number
components
in
leading
significant
proliferation
potential
combinations,
which
poses
considerable
challenges
when
devising
optimal
solutions
for
process.
Consequently,
improve
efficiency
decrease
time,
discrete
whale
optimization
algorithm
(DWOA)
proposed
this
paper
achieve
excellent
parallel
(PDSP).
To
begin,
composite
nodes
are
added
into
constraint
relationship
graph
based
on
characteristics
equipment,
time
chosen
as
objective.
Subsequently,
DWOA
solve
PDSP
problem
by
integrating
precedence
preservative
crossover
mechanism,
heuristic
mutation
repetitive
pairwise
exchange
operator.
Meanwhile,
hierarchical
combination
method
used
swiftly
generate
initial
population.
verify
viability
algorithm,
classic
genetic
(GA),
simplified
teaching–learning-based
(STLBO),
self-adaptive
swarm
(SSO)
were
employed
comparison
three
projects.
The
experimental
results
comparative
analysis
revealed
that
with
achieved
reduced
only
19.96
min
Experiment
3.
Additionally,
values
standard
deviation,
average
rate
minimum
0.3282,
20.31,
71%,
respectively,
demonstrating
its
superior
performance
compared
other
algorithms.
Furthermore,
addresses
inefficiencies
dismantling
processes
stations
enhances
visual
representation
training
Unity3D
intelligent
Язык: Английский
Disassembly Plan Representation by Hypergraph
Abboy Verkuilen,
Mirjam Zijderveld,
Niels de Buck
и другие.
Automation,
Год журнала:
2025,
Номер
6(1), С. 10 - 10
Опубликована: Фев. 20, 2025
To
be
successful
in
a
circular
economy,
it
is
important
to
keep
the
cost
of
operationalizing
remanufacturing
processes
low
order
retain
as
much
value
product
possible.
Optimizing
operations
for
disassembly,
key
process
step,
therefore
an
prerequisite
economically
viable
manufacturing.
The
generation
fit-to-resource
disassembly
instructions
labor-intensive
and
challenging
because
(digital)
information
often
lacking
at
End-of-Life.
With
upcoming
EU
regulations
Eco-design
Sustainable
Products
mind,
including
future
use
Digital
Product
Passports,
time
think
about
standardized
methods
capture
products.
First
requirements
from
small
medium-sized
companies
have
been
collected
compared
with
available
frameworks
modeling
topology,
parameters,
(dis)assembly
rationale.
Based
on
this,
hypergraph
presented
concept
recording
‘resource-agnostic
guides’
(machine-readable)
models
determine
required
actions
tools
‘smartly’.
builds
upon
existing
models.
Additionally,
suitable
collection
are
explored,
resulting
preliminary
insights
data
workshops.
Although
approach
promising,
work
needed
expand
both
guidelines
setting
up
ontologies
further
systematic
knowledge
extraction
apply
this
useful
means
rationalize
their
operations.
Язык: Английский
Multi-objective human-robot collaborative disassembly line balancing problem considering components remanufacture demand and hazard characteristics
Computers & Industrial Engineering,
Год журнала:
2024,
Номер
197, С. 110621 - 110621
Опубликована: Окт. 9, 2024
Язык: Английский
Monitoring model for enhancing adaptability in human–robot collaborative mold assembly
International Journal of Computer Integrated Manufacturing,
Год журнала:
2024,
Номер
unknown, С. 1 - 19
Опубликована: Авг. 10, 2024
Molds
are
assembled
manually
due
to
their
inherent
characteristics
of
low-volume
and
high-variety
production.
Given
the
ergonomic
risks
caused
by
heavy-handling
repetitive
tasks
diverse
requirements
in
mold
assembly,
collaborative
robots
offer
adaptability
ease
reconfiguration,
making
them
potential
solutions
these
challenges.
This
study
introduces
a
monitoring
model
for
human–robot
assembly
using
two
cobots.
encompasses
manual
progress
cobot
execution
position-sharing
modules.
Manual
task
actions
detected
You-Only-Look-Once-v8
Nano
model.
Detected
subsequently
classified
into
different
states.
These
identified
states
relayed
cobots,
enabling
early
controlling
cobots'
entry
area.
proposes
approach
prevent
collisions
receiving
coordinates
via
Modbus
between
Most
existing
research
has
developed
separate
models
action
part
recognition,
excluding
utilization
recognition
results
enable
execution.
contributes
novel
subsequent
facilitate
communication
through
position
sharing
during
tasks.
The
show
that
time
risk
cobots
can
be
reduced
Язык: Английский
Solving a Stochastic Multi-Objective Sequence Dependence Disassembly Sequence Planning Problem with an Innovative Bees Algorithm
Automation,
Год журнала:
2024,
Номер
5(3), С. 432 - 449
Опубликована: Авг. 23, 2024
As
the
number
of
end-of-life
products
multiplies,
issue
their
efficient
disassembly
has
become
a
critical
problem
that
urgently
needs
addressing.
The
field
sequence
planning
consequently
attracted
considerable
attention.
In
actual
process,
complex
structures
can
lead
to
significant
delays
due
interference
between
different
tasks.
Overlooking
this
result
in
inefficiencies
and
waste
resources.
Therefore,
it
is
particularly
important
study
sequence-dependent
problem.
Additionally,
activities
are
inherently
fraught
with
uncertainties,
neglecting
these
further
impact
effectiveness
disassembly.
This
first
analyze
an
uncertain
environment.
It
utilizes
stochastic
programming
approach
address
uncertainties.
Furthermore,
mixed-integer
optimization
model
constructed
minimize
time
energy
consumption
simultaneously.
Recognizing
complexity
problem,
introduces
innovative
bees
algorithm,
which
proven
its
by
showing
superior
performance
compared
other
state-of-the-art
algorithms
various
test
cases.
research
offers
solutions
for
holds
implications
advancing
sustainable
development
recycling
Язык: Английский
Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0
International Journal of Production Research,
Год журнала:
2024,
Номер
unknown, С. 1 - 17
Опубликована: Дек. 5, 2024
In
the
context
of
Industry
4.0,
model-based
definition
(MBD)
has
been
an
effective
approach
to
creating
3D
models
contained
all
heterogeneous
information
needed
define
a
product,
which
proposes
new
challenges
for
traditional
subassembly
identification
method
that
only
considers
geometric
product
in
assembly
sequence
planning.
To
bridge
gap,
we
propose
novel
knowledge
graph-driven
enhance
planning
systems
engineering
(MBSE)
paradigm.
Specifically,
graph
is
first
constructed
based
on
shape
and
details
MBD
model.
Next,
ensemble
deep
learning
combines
neural
networks
with
community
detection
algorithm
proposed
effectively
detect
from
Finally,
feasibility
effectiveness
are
demonstrated
through
example
car
suspension
identification,
providing
insight
into
industrial
implementation.
Язык: Английский