Recent Advances and Challenges in Industrial Robotics: A Systematic Review of Technological Trends and Emerging Applications
Processes,
Год журнала:
2025,
Номер
13(3), С. 832 - 832
Опубликована: Март 12, 2025
Industrial
robotics
has
shifted
from
rigid,
task-specific
tools
to
adaptive,
intelligent
systems
powered
by
artificial
intelligence
(AI),
machine
learning
(ML),
and
sensor
integration,
revolutionizing
efficiency
human–robot
collaboration
across
manufacturing,
healthcare,
logistics,
agriculture.
Collaborative
robots
(cobots)
slash
assembly
times
30%
boost
quality
15%,
while
reinforcement
enhances
autonomy,
cutting
errors
energy
use
20%.
Yet,
this
review
transcends
descriptive
summaries,
critically
synthesizing
these
trends
expose
unresolved
tensions
in
scalability,
cost,
societal
impact.
High
implementation
costs
legacy
system
incompatibilities
hinder
adoption,
particularly
for
SMEs,
interoperability
gaps—despite
frameworks,
like
OPC
UA—stifle
multi-vendor
ecosystems.
Ethical
challenges,
including
workforce
displacement
cybersecurity
risks,
further
complicate
progress,
underscoring
a
fragmented
field
where
innovation
outpaces
practical
integration.
Drawing
on
systematic
of
high-impact
literature,
study
uniquely
bridges
technological
advancements
with
interdisciplinary
applications,
revealing
disparities
economic
feasibility
equitable
access.
It
critiques
the
literature’s
isolation
trends—cobots’
safety,
ML’s
perception’s
precision—proposing
following
cohesive
research
directions:
cost-effective
modularity,
standardized
protocols,
ethical
frameworks.
By
prioritizing
interoperability,
sustainability,
paper
charts
path
evolve
inclusively,
offering
actionable
insights
researchers,
practitioners,
policymakers
navigating
dynamic
landscape.
Язык: Английский
A Review on Integrating IoT, IIoT, and Industry 4.0: A Pathway to Smart Manufacturing and Digital Transformation
IET Information Security,
Год журнала:
2025,
Номер
2025(1)
Опубликована: Янв. 1, 2025
The
industrial
Internet
of
Things
(IIoT)
has
become
an
innovative
technology
that
brought
many
benefits
to
industries
and
organizations.
This
review
presents
a
comprehensive
analysis
IIoT’s
applications,
highlighting
its
ability
optimize
operations
through
advanced
connectivity,
real‐time
data
exchange,
automation,
importance
in
the
context
Industry
4.0.
Emphasizing
distinction
between
IIoT
traditional
IoT,
paper
explores
how
focuses
on
enhancing
ecosystems
integrating
cyber‐physical
systems
(CPSs).
article
explains
establish
highly
linked
infrastructure
support
cutting‐edge
services
ensure
greater
flexibility
efficiency.
It
emphasizes
role
CPS
automation
control
(IACSs)
realizing
potential
IIoT.
Security
concerns,
important
part
IIoT,
are
addressed
conversations
protecting
networked
systems,
assuring
operational
reliability,
emphasizing
need
for
strong
security
measures
prevent
threats
vulnerabilities.
Furthermore,
critical
technologies
such
as
machine
learning
(ML),
artificial
intelligence
(AI),
various
communication
protocols,
including
fifth
generation
(5G)
message
queuing
telemetry
transport
(MQTT),
investigated
their
improve
system
performance
decision‐making
processes.
In
addition,
also
discusses
safety
precautions
challenges
using
Finally,
addressing
issues
promoting
successful
adoption
achieving
expected
benefits.
study
offers
valuable
resources
researchers,
academics,
decision‐makers
implement
environments.
Язык: Английский
A Short Review: Tribology in Machining to Understand Conventional and Latest Modeling Methods with Machine Learning
Machines,
Год журнала:
2025,
Номер
13(2), С. 81 - 81
Опубликована: Янв. 23, 2025
Tribology
plays
a
critical
role
in
machining
technologies.
Friction
is
an
essential
factor
processes
such
as
composite
material
and
bonding.
This
short
review
highlights
the
recent
advancements
controlling
leveraging
tribological
phenomena
machining.
For
instance,
high-precision
increasingly
relying
on
situ
observation
real-time
measurement
of
tools,
test
specimens,
equipment
for
effective
process
control.
Modern
engineering
materials
often
incorporate
functional
metastable
states,
composites
dissimilar
materials,
rather
than
conventional
stable-phase
materials.
In
these
cases,
effects
during
can
impede
precision.
On
other
hand,
friction
additive
manufacturing
demonstrates
constructive
application
tribology.
Traditionally,
understanding
mitigating
have
involved
developing
physical
chemical
models
individual
factors
using
simulations
to
inform
decisions.
However,
accurately
predicting
system
behavior
has
remained
challenging
due
complex
interactions
between
machine
components
variations
initial
operational
(or
deteriorated)
states.
Recent
innovations
introduced
data-driven
approaches
that
predict
without
need
detailed
models.
By
integrating
advanced
monitoring
technologies
learning,
methods
enable
predictions
within
controllable
parameters
live
data.
shift
opens
new
possibilities
achieving
more
precise
adaptive
Язык: Английский
CYBER INFRASTRUCTURE GUIDE: IT/OT INTEGRATION
Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi,
Год журнала:
2025,
Номер
18(1), С. 378 - 391
Опубликована: Янв. 30, 2025
Innovations
in
information
and
communication
technologies
have
led
to
complex
dangerous
security
problems.
Industries
face
financial
reputational
losses
due
inadequate
applications
the
field
of
cybersecurity.
This
situation
increases
importance
industrial
cybersecurity,
which
tries
protect
systems
from
cyber
threats
attacks.
Industrial
cybersecurity
is
responsible
for
protection
operational
control
used
various
branches
industry,
focusing
on
continuity
business
processes.
It
ensures
uninterrupted
secure
operation
infrastructure
processes
by
minimizing
risks
that
may
harm
Continuously
evolving
types
cyberattacks
pose
serious
Traditional
methods
are
not
sufficient
reduce
these
risks.
Instead,
industries
should
develop
existing
measures
integrate
with
technologies.
In
this
way,
initiative-taking
can
be
taken
before
a
cyberattack
occurs,
operations
ensured.
addition,
data
obtained
handled
up-to-date
approaches.
context,
study
aims
serve
as
guide
integration
against
rapidly
raise
awareness
about
necessary
qualifications
standards
they
need
maintain.
Thanks
roadmap,
sectors
will
able
predict
advance
create
an
model.
Язык: Английский
Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection
Waluyo Nugroho,
Rifdah Zahabiyah,
Afianto
и другие.
Jurnal E-Komtek (Elektro-Komputer-Teknik),
Год журнала:
2024,
Номер
8(2), С. 428 - 437
Опубликована: Дек. 31, 2024
The
use
of
Personal
Protective
Equipment
(PPE)
is
a
critical
step
in
ensuring
worker
safety
various
sectors,
including
industry,
construction,
and
health.
However,
violations
using
PPE
often
occur,
which
can
increase
the
risk
work
accidents.
This
study
aims
to
develop
deep
learning-based
detection
system
YOLOv8
algorithm.
method
was
chosen
because
its
superior
ability
detect
objects
real
time
with
high
accuracy.
training
data
consists
images
workers
different
environments,
label
recognize
types
such
as
helmets,
masks,
vests.
developed
tested
on
test
dataset
evaluate
model
performance
based
metrics
confusion
matrix,
inference
speed,
error
rate.
experimental
results
show
that
an
accuracy
level
up
95%.
implementation
this
expected
be
effective
solution
increasing
compliance
preventing
Язык: Английский