Advanced Materials,
Journal Year:
2023,
Volume and Issue:
35(45)
Published: June 19, 2023
Abstract
Mechanical
metamaterials
are
meticulously
designed
structures
with
exceptional
mechanical
properties
determined
by
their
microstructures
and
constituent
materials.
Tailoring
material
geometric
distribution
unlocks
the
potential
to
achieve
unprecedented
bulk
functions.
However,
current
metamaterial
design
considerably
relies
on
experienced
designers'
inspiration
through
trial
error,
while
investigating
responses
entails
time‐consuming
testing
or
computationally
expensive
simulations.
Nevertheless,
recent
advancements
in
deep
learning
have
revolutionized
process
of
metamaterials,
enabling
property
prediction
geometry
generation
without
prior
knowledge.
Furthermore,
generative
models
can
transform
conventional
forward
into
inverse
design.
Many
studies
implementation
highly
specialized,
pros
cons
may
not
be
immediately
evident.
This
critical
review
provides
a
comprehensive
overview
capabilities
prediction,
generation,
metamaterials.
Additionally,
this
highlights
leveraging
create
universally
applicable
datasets,
intelligently
intelligence.
article
is
expected
valuable
only
researchers
working
but
also
those
field
materials
informatics.
Virtual and Physical Prototyping,
Journal Year:
2023,
Volume and Issue:
18(1)
Published: March 9, 2023
Metal
additive
manufacturing
is
gaining
immense
research
attention.
Some
of
these
efforts
are
associated
with
physics,
statistical,
or
artificial
intelligence-driven
process
modelling
and
optimisation,
structure–property
characterisation,
structural
design
equipment
enhancements
for
cost
reduction
faster
throughputs.
In
this
review,
the
focus
drawn
on
utilisation
topology
optimisation
in
metal
manufacturing.
First,
symbiotic
relationship
between
aerospace,
medical,
automotive,
other
industries
investigated.
Second,
support
structure
by
thermal-based
powder-bed
processes
discussed.
Third,
introduction
capabilities
to
limit
constraints
generate
porous
features
examined.
Fourth,
emerging
adopt
intelligence
models
Finally,
some
open-source
commercial
software
explored.
This
study
considers
challenges
faced
while
providing
perceptions
future
directions.
Advanced Materials,
Journal Year:
2023,
Volume and Issue:
36(8)
Published: Dec. 5, 2023
Abstract
Metamaterials
are
artificial
materials
designed
to
exhibit
effective
material
parameters
that
go
beyond
those
found
in
nature.
Composed
of
unit
cells
with
rich
designability
assembled
into
multiscale
systems,
they
hold
great
promise
for
realizing
next‐generation
devices
exceptional,
often
exotic,
functionalities.
However,
the
vast
design
space
and
intricate
structure–property
relationships
pose
significant
challenges
their
design.
A
compelling
paradigm
could
bring
full
potential
metamaterials
fruition
is
emerging:
data‐driven
This
review
provides
a
holistic
overview
this
rapidly
evolving
field,
emphasizing
general
methodology
instead
specific
domains
deployment
contexts.
Existing
research
organized
modules,
encompassing
data
acquisition,
machine
learning‐based
cell
design,
optimization.
The
approaches
further
categorized
within
each
module
based
on
shared
principles,
analyze
compare
strengths
applicability,
explore
connections
between
different
identify
open
questions
opportunities.
Progress in Materials Science,
Journal Year:
2023,
Volume and Issue:
138, P. 101129 - 101129
Published: April 23, 2023
Additive
manufacturing
(AM)
processes
have
proven
to
be
a
perfect
match
for
topology
optimization
(TO),
as
they
are
able
realize
sophisticated
geometries
in
unique
layer-by-layer
manner.
From
viewpoint,
however,
there
is
significant
likelihood
of
process-related
defects
within
complex
geometrical
features
designed
by
TO.
This
because
TO
seldomly
accounts
process
constraints
and
conditions
typically
perceived
purely
design
tool.
On
the
other
hand,
advanced
AM
simulations
shown
their
potential
reliable
tools
capable
predicting
various
hence
serving
second-to-none
material
tool
achieving
targeted
properties.
Thus
far,
these
two
geometry
been
traditionally
viewed
entirely
separate
paradigms,
whereas
one
must
conceive
them
holistic
computational
instead.
More
specifically,
models
provide
input
physics-based
TO,
where
consequently,
not
only
component
will
function
optimally,
but
also
near-to-minimum
defects.
In
this
regard,
we
aim
at
giving
thorough
overview
concepts
applied
AM.
The
paper
arranged
following
way:
first,
literature
on
performance
reviewed
then
most
recent
developments
techniques
related
covered.
Process
play
pivotal
role
latter
type
serve
additional
top
primary
end-user
objectives.
As
natural
consequence
this,
comprehensive
detailed
review
non-metallic
metallic
additive
performed,
divided
into
micro-scale
deposition-scale
simulations.
Material
multi-scaling
which
central
process-structure-property
relationships,
next
followed
subsection
reduced-order
versions
incorporable
due
lower
requirements.
Finally
concluded
suggestions
further
research
paths
discussed.
Computational Mechanics,
Journal Year:
2024,
Volume and Issue:
74(2), P. 281 - 331
Published: Jan. 13, 2024
Abstract
The
rapid
growth
of
deep
learning
research,
including
within
the
field
computational
mechanics,
has
resulted
in
an
extensive
and
diverse
body
literature.
To
help
researchers
identify
key
concepts
promising
methodologies
this
field,
we
provide
overview
deterministic
mechanics.
Five
main
categories
are
identified
explored:
simulation
substitution,
enhancement,
discretizations
as
neural
networks,
generative
approaches,
reinforcement
learning.
This
review
focuses
on
methods
rather
than
applications
for
thereby
enabling
to
explore
more
effectively.
As
such,
is
not
necessarily
aimed
at
with
knowledge
learning—instead,
primary
audience
verge
entering
or
those
attempting
gain
discussed
are,
therefore,
explained
simple
possible.
