Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques
Polymers,
Год журнала:
2024,
Номер
16(21), С. 2986 - 2986
Опубликована: Окт. 24, 2024
Additive
manufacturing
(AM)
defects
present
significant
challenges
in
fiber-reinforced
thermoplastic
composites
(FRTPCs),
directly
impacting
both
their
structural
and
non-structural
performance.
In
structures
produced
through
material
extrusion-based
AM,
specifically
fused
filament
fabrication
(FFF),
the
layer-by-layer
deposition
can
introduce
such
as
porosity
(up
to
10-15%
some
cases),
delamination,
voids,
fiber
misalignment,
incomplete
fusion
between
layers.
These
compromise
mechanical
properties,
leading
reduction
of
up
30%
tensile
strength
and,
cases,
20%
fatigue
life,
severely
diminishing
composite's
overall
performance
integrity.
Conventional
non-destructive
testing
(NDT)
techniques
often
struggle
detect
multi-scale
efficiently,
especially
when
resolution,
penetration
depth,
or
heterogeneity
pose
challenges.
This
review
critically
examines
FRTPCs,
classifying
FFF-induced
based
on
morphology,
location,
size.
Advanced
NDT
techniques,
micro-computed
tomography
(micro-CT),
which
is
capable
detecting
voids
smaller
than
10
µm,
health
monitoring
(SHM)
systems
integrated
with
self-sensing
fibers,
are
discussed.
The
role
machine-learning
(ML)
algorithms
enhancing
sensitivity
reliability
methods
also
highlighted,
showing
that
ML
integration
improve
defect
detection
by
25-30%
compared
traditional
techniques.
Finally,
potential
self-reporting
equipped
continuous
fibers
for
real-time
situ
SHM,
investigated.
By
integrating
ML-enhanced
accuracy
efficiency
be
significantly
improved,
fostering
broader
adoption
AM
aerospace
applications
enabling
production
more
reliable,
defect-minimized
FRTPC
components.
Язык: Английский
Prediction of microstructural-dependent mechanical properties, progressive damage, and stress distribution from X-ray computed tomography scans using a deep learning workflow
Computer Methods in Applied Mechanics and Engineering,
Год журнала:
2024,
Номер
424, С. 116878 - 116878
Опубликована: Март 11, 2024
Creating
computationally
efficient
models
that
link
processing
methods,
material
structures,
and
properties
is
essential
for
the
development
of
new
materials.
Translating
microstructural
details
to
macro-level
mechanical
often
proves
be
an
arduous
challenge.
This
paper
introduces
a
novel
deep
learning-based
framework
predict
3D
stress
fields,
behavior,
progressive
damage
in
ceramic
materials
informed
by
features
material.
We
construct
dataset
synthetic
representative
volume
elements
utilizing
X-ray
computed
tomography
scans
employ
automated
finite
element
(FE)
modeling
approach
generate
datasets
alumina
ceramics
with
varying
inclusion
morphologies.
The
learning
model,
U-Net
based
convolutional
neural
network
(CNN),
trained
understand
structure-property
linkages
responses
directly
from
FE-generated
data
without
transforming
them
into
image
format.
CNN's
architecture
optimized
capturing
both
local
global
contextual
information
data,
enabling
accurate
prediction
fields
evolution.
Inclusions
within
are
shown
play
crucial
role
initiation
propagation
damage.
CNN
model
demonstrated
robust
performance
predicting
field,
stress-strain
curve,
training
test
showing
high
consistent
similarity
between
predictions
ground
truth.
Overall,
this
research
offers
generalized
can
adapted
different
structures
toward
creating
digital
replicas
optimizing
real-world
applications.
Язык: Английский
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Amirul Islam Saimon,
Emmanuel Yangue,
Xiaowei Yue
и другие.
IISE Transactions,
Год журнала:
2024,
Номер
unknown, С. 1 - 44
Опубликована: Дек. 19, 2024
This
paper
presents
the
first
comprehensive
literature
review
of
deep
learning
(DL)
applications
in
additive
manufacturing
(AM).
It
addresses
need
for
a
thorough
analysis
this
rapidly
growing
yet
scattered
field,
aiming
to
bring
together
existing
knowledge
and
encourage
further
development.
Our
research
questions
cover
three
major
areas
AM:
(i)
design
AM,
(ii)
AM
modeling,
(iii)
monitoring
control
AM.
We
use
step-by-step
approach
following
Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
(PRISMA)
guidelines
select
papers
from
Scopus
Web
Science
databases,
aligning
with
our
questions.
include
only
those
that
implement
DL
across
seven
categories
-
binder
jetting,
directed
energy
deposition,
material
extrusion,
powder
bed
fusion,
sheet
lamination,
vat
photopolymerization.
reveals
trend
towards
using
generative
models,
such
as
adversarial
networks,
also
highlights
an
increasing
effort
incorporate
process
physics
into
models
improve
modeling
reduce
data
requirements.
Additionally,
there
is
interest
3D
point
cloud
monitoring,
alongside
traditional
1D
2D
formats.
Finally,
summarizes
current
challenges
recommends
some
promising
opportunities
domain
investigation
special
focus
on
generalizing
wide
range
geometry
types,
managing
uncertainties
both
overcoming
limited,
imbalanced,
noisy
issues
by
incorporating
(iv)
unveiling
potential
interpretable
Язык: Английский
Deep-Learning-Based Optimization of Non-Periodic Porous and Composite Materials for Aerospace Structures
AIAA SCITECH 2022 Forum,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 3, 2025
Язык: Английский
A high-throughput framework for predicting three-dimensional structural-mechanical relationships of human cranial bones using a deep learning-based method
Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials,
Год журнала:
2025,
Номер
168, С. 107007 - 107007
Опубликована: Апрель 25, 2025
Язык: Английский
Virtual rapid prototyping of materials with deep learning: spatiotemporal stress fields prediction in ceramics employing convolutional neural networks and transfer learning
Virtual and Physical Prototyping,
Год журнала:
2024,
Номер
19(1)
Опубликована: Сен. 9, 2024
Язык: Английский
Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics
Deleted Journal,
Год журнала:
2024,
Номер
4, С. 100011 - 100011
Опубликована: Авг. 23, 2024
Язык: Английский
Curing simulation and data-driven curing curve prediction of thermoset composites
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Дек. 30, 2024
Molding
has
been
widely
used
to
manufacture
thermoset
composite
structures
in
the
aerospace
and
automotive
industries
owing
its
efficiency
reducing
number
of
parts
manufacturing
cost.
For
such
molded
parts,
degree-of-cure
curve
is
generally
evaluate
solidification
resin.
Nevertheless,
simulation
cure
not
model
itself,
but
rather
knowing
initial
conditions
as
fiber
volume
fraction,
curing
degree,
convective
boundary
etc.
Additionally,
solving
heat
transfer
coupled
with
kinetics
presents
additional
requirements
for
time,
making
artificial
intelligence
tools
promising
these
problems.
This
paper
focuses
on
developing
a
data-driven
approach
predicting
curve.
The
simulated
corresponds
specific
temperature-time
was
verified
by
published
value.
Then,
resulting
degree-of-cure-time
curves
obtained
from
finite
element
simulations
were
created
training
prediction
models
using
machine
learning
approaches
support
vector
regression
(SVR),
back
propagation
(BP)
neural
network
BP
optimized
genetic
algorithm
(GA-BP).
validation
evaluation
indices
illustrate
that
trained
GA-BP
yields
highest
accuracy.
Язык: Английский