Multiscale Materials Modeling for Metal Additive … of the future/3D printing... · Multiscale...
Transcript of Multiscale Materials Modeling for Metal Additive … of the future/3D printing... · Multiscale...
Anssi Laukkanen, Tom Andersson, Tatu Pinomaa, Matti Lindroos, Merja Sippola, Sami Majaniemi, Tomi Suhonen (et al.++) VTT Materials & Manufacturing, Finland 22.11.2016
Multiscale Materials Modeling for Metal Additive Manufacturing
01/12/2016 2
Brief introduction to multiscale materials modeling & Integrated
Computational Materials Engineering @ VTT
Multiscale materials modeling for metal additive manufacturing
Process-Structure-Properties-Performance analysis of Ti-6Al-4V
selective laser melting:
Process-2-Structure: Phase field modeling of rapid solidification
Structure-2-Properties: Crystal plasticity modeling of engineering
material properties
Properties-2-Performance: Micromechanical modeling of fatigue
Summary & Conclusions
Contents
MULTISCALE MATERIALS
MODELING
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VTT Materials & Manufacturing,
Multiscale Materials Modeling Research Group
Main focus on “mesoscale”
modeling, nano-microstructures &
affiliated phenomena.
VTT ProperTune, in-house
multiscale modeling toolset (2013)
Cleavage fracture research. 1st “real”
multiscale modeling work, circa 1997.
“Merger” with tribology, 2001.
As new area
composites, 2008.
Crystal plasticity analysis of high
strength steel: EBSD map (left),
slip (right)
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Multiscale materials modeling: 1) Structure –
Property Correlations
Computational representation of material microstructure: multiphase composite elements, binders, interfaces, porosity & defects of various kinds, ….
Microstructural analysis of resulting material properties: compressive strength, true stress-strain, viscoplastic strain/strain rate, ….
PROPERTY STRUCTURE
Link material microstructure to material properties
CASES: Compression strength of WC-Co, TiC-Ni & hard material composite & metallic microstructures
TiC-Ni
TiC-Ni
MMC
MMC
WC-Co WC-Co
martensitic
WC-Co
WC-Co
MMC
MMC
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Multiscale materials modeling: 2) Structure –
Property – Performance Correlations
Computational representation of material microstructure: metals, composites, texture, multiphase….
Microstructural analysis of resulting material performance, example: short fatigue crack initiation
PROPERTY
STRUCTURE
Link material microstructure and properties to material performance
Microstructural analysis of resulting material properties: strength, true stress-strain, viscoplastic strain rate….
Microstructure founded analysis of individual defects in material microstructure
PERFORMANCE CASE: Microstructure based fatigue analysis of PH steel for metal additive manufacturing
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Multiscale materials modeling: 3) Process –
Structure – Property – Performance Correlations
a) b)
c) d)
e) f)
g) h) g) h) Link material manufacturing process, thermomechanical history, operational conditions etc. to material structure, permeate to properties & performance
Thermomechanical manufacturing process, operational history etc.
PROCESS
PROCESS
STRUCTURE PROPERTY, PERFORMANCE
Solidification, precipitation, grain growth, …
CASE: Material and microstructure design for rapid solidification processes (especially additive manufacturing, thermal spray)
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Incorporates and integrates a range of multiscale
modeling methods and techniques for materials
related problems:
The main application areas of VTT ProperTune are
1) modeling of nano-microstructures and their
properties at mesoscale:
is a collection of software libraries,
interfaces and modeling packages & tools
enables the rapid development &
deployment of modeling solutions
is not a single software package, but rather
a material modeling toolset
…and 2) performance dominating mechanisms and
processes related to component operating
environments or manufacturing (or both):
VTT ProperTune™ is a computational modeling
assisted material design, tailoring and performance
evaluation methodology and software platform.
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Multiscale materials modeling example:
Deformation of Metallic Materials
Dislocation core
phenomena
Dislocation line
behavior
Single crystal
slip behavior
Polycrystalline
microstructure
deformation
Material
properties, e.g.
strength or
ductility
Material
performances,
e.g. fatigue
resistance
structural
damage, aging
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MULTISCALE MATERIALS MODELING AND
INTEGRATED COMPUTATIONAL MATERIALS
ENGINEERING FOR METAL ADDITIVE
MANUFACTURING
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PRODUCT PERFORMANCE
AND COST
Multiscale modeling for
metal additive manufacturing
Discrete modeling of
powder bed physics
→ Powder bed
thermomechanics, laser
matter interaction
Thermodynamics and
phase fields
→ Solidification
microstructure, surface
phenomena & reactive wetting
Modeling material structure → properties and performance
→ Material structure to
material properties causality
→ Material
performance
Topology optimization
→ Optimized
geometric design
Thermomechanical modeling of
selective laser melting
→ Part specific process design,
residual stress & distortion
minization
SLM process design and optimization
Powder and alloy design
Material property & performance design
Part geometry design
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Modeling for metal additive
manufacturing: Concept
Digital material, digital manufacturing and digital product design for metal additive manufacturing. Enable
complex and coupled (e.g. two-way) optimization workflows.
Adopting ICME
principles
Motivation: to properly design for metal AM, an approach incorporating aspects of material, process and
product modeling and design is required.
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Process-Structure-Properties-Performance
Design Concept in Application of ICME
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PROCESS-2-STRUCTURE: PHASE FIELD
MODELING OF RAPID SOLIDIFICATION
MICROSTRUCTURES
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SLM transient thermal
process model, cubic test
samples
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Epitaxial microstructure (*)
(*): figure
from http://iq-
evolution.com
Mechanical
anisotropy
e.g. prone to
cracking and
non-optimal
strength
properties
β grains
Epitaxial growth of columnar grains
Selective laser melting of Ti-6Al-4V and PF
modeling
Two approaches available
for solving:
• Solver 1: dilute binary
model with multi-order
parameter for
orientations
• Solver 2: grand
canonical formulation
with arbitrary free
energies
The former best suited for
N-order parameter
formulation of high rate
phenomena, the latter e.g.
for coarse graining to
greater timescales
(variational formulation of
the solidification problem).
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Selective laser melting of Ti-6Al-4V and PF
modeling
Epitaxial microstructure
Epitaxial growth of columnar grains
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Inoculated equiaxed growth vs columnar growth
Design of inoculated
metallurgies
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Enables the linking of irreversible non-equilibrium thermodynamics studying metastable structures
to imaging based finite elements
Co-simulation approach between phase field
and finite element modeling of
nano/micro/mesostructures
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STRUCTURE-2-PROPERTIES:
CRYSTAL PLASTICITY MODELING OF
ENGINEERING MATERIAL PROPERTIES
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Structure-Property: Metallic microstructures
Introduction of
secondary features
such as laths, twins
etc. to a primary
structure
Using 3D image processing in manipulating nano- and microstructures, to obtain representable 3D images of structure.
Emphasis in metallic and composite (or plainly multi-phase) structures, but no morphological limitations with respect to
the methods themselves.
Introduction of 2nd
phase structures
(precipitates, carbides
etc) to a primary
structure
Tesselation of synthetic
microstructures
Generation of
microstructures
with texture and
grain flow
generation of BCC
structures (~bainite like)
generation of BCC
structures (~martensite
like)
generation of
steel+composite
microstructures
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Structure-Property: Crystal plasticity
Electron backscatter diffraction (EBSD)
image of a high-strength steel,
martensite like basic microstructure
Example: roughly a
single prior austenite
grain
Imaging based
numerical
model of the
grain region
Finite element
mesh of the
grain region
Single crystal
region with
misorientation
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Structure-Property: Crystal plasticity of martensite like
microstructures
Real microstructure discretized to a
computational FE mesh
Subfeatures in martensitic microstructures
Macroscopic stress-strain behavior
Martensitic structures can include:
• Fine microstructure (e.g., prior
austenite size 20-200 um)
• Subfeatures introduce scale
dependency to the material
• Misorientation exist between grains
and subfeatures (especially
packet/block boundaries)
Slip rate and local hardening:
Crystal plasticity approach: deformation
takes place by shear produced by
dislocations
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Structure-Property: Crystal plasticity
Columnar model 1: phase distribution and
grain orientations from phase field model Equiaxed model 1: phase distribution and
grain orientations from phase field model
Columnar model 2:
model 1 + add a
lamellar structure
Equiaxed model 2:
model 1 + add a
lamellar structure
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Structure-Property: Crystal plasticity,
tensile testing Columnar model 1,
direction 1
Columnar model 1,
direction 2 Columnar model,
lamellar substructure
Equiaxed model, lamellar substructure Equiaxed model
• Columnar models are of low strength
and anisotropic response of the
microstructure is clearly visible.
• Equiaxed structure is an
improvement, although the
microstructure is a bit coarse which
shows.
• Lamellar/lath/multiphase structures
are superior in terms of strength.
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Structure-Property: Crystal
plasticity, tensile testing
Columnar
model 1,
direction 1
Columnar
model 1,
direction 2
Columnar
model,
lamellar
substructure
Equiaxed model,
lamellar substructure
Equiaxed
model
Equiaxed
model, lamellar
substructure
Columnar model,
lamellar
substructure
Equiaxed
model
Columnar
model 1,
direction 1
CUMULATIVE PLASTIC SLIP
EQUIVALENT STRESS CONTOURS
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PROPERTIES-2-PERFORMANCE:
MICROMECHANICAL MODELING OF FATIGUE
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Structure-Property-Performance :
Microstructure informed modeling of fatigue
Plastic slip at the free surface of a microstructural domain (high
strength steel) during fatigue cycling. Plastic slip as the failure
criterion.
Deformed model geometry
Region of interest
with respect to
plastic slip and
crack initiation
Larger size packet
feature in microstructure
Slip systems in larger
spot activate ~ “soft
spot”
Surface “waviness” due to finite strain
crystal plasticity
Slip bands &
intrusions for
FC initiation
Plastic strain contours
during tensile loading of a
microstructure.
Crystal plasticity modeling of surface deformation of martensite microstructure (microstructural model generated on the
basis of HR-SEM imaging)
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Structure-Property-Performance : Microstructure
informed modeling of fatigue
1000 cycles, ampl = 2e-3
Ampl = ~50% of yield strength
Cyclic loading to 1000 cycles
• Small amplitudes also cause
significant plasticity in the fine
lath martensite
• Many plausible fatigue sites
• Fatigue evolution laws must be
evaluated locally to upscale to
macroscopic fatigue life
Interframe cycle rate 50
Strain localization
proceeds over
subfeatures e.g.,
prior austenite
grains and packet
boundaries;
however the
relative resistance
of each may be
evaluated
Cumulative plastic slip
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Structure-Property-Performance : Microstructure
informed modeling of fatigue
Microstructure
scale initial crack,
growth about one
grain size via
cumulative slip
based criterion
Introduce a microstructure scale pore, initial location
selected based on local large misorientation
Strain amplitude 5e-4 Strain amplitude 1e-3 Strain amplitude 5e-3 Strain amplitude 1e-2
Analysis of fatigue in microstructures with pre-existing
defects:
Cumulative plastic slip
Cumulative
plastic slip
Cumulative
plastic slip
1st principal
stress
1st principal
stress
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Structure-Property-Performance :
Microstructure informed modeling of fatigue
“Best” microstructure “as good as the
others” for large amplitudes
Despite very small
increments of slip, no
threshold predicted
Columnar,
coarse
lamellar
defects
Introduction of a short fatigue crack size
sharp defect influences cycles to initiation
by a factor of approx. 5
Effect of pore
approx. 2.5
~100 cycles in
strain control
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SUMMARY & CONCLUSIONS
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Summary
Integrated Computational Materials Engineering presently offers
the best overall concept for making materials and the
manufacturing processes part of the digital realm of metal
additive manufacturing. Digitalization of everything materials and
manufacturing processes related feeds ICME.
The methodologies have in many cases yielded case studies
with results significantly improved over those using legacy
approaches enabling the expansion of the AM design space,
new freedoms and features to optimize, tailor and select better
solutions.
The drivers are ultimately better products with optimized material
solutions to market faster with decreased cost. Metal AM needs
an all-around approach merging material, process, CAx and
optimization toolsets and practices.
Thank You!
http://www.vttresearch.com/propertune [email protected]
Acknowledging contributions by:
N. Ofori-Okopu, Northwestern University, USA
N. Provatas, McGill University, Canada
D. Pal, B. Stucker, UofL & 3DSIM, USA