Materials Modelling and Interoperability –Siemens PLM VisionNovember 2017
Realize innovation.Unrestricted © Siemens AG 2017
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Siemens PLM – Simulation & Test Solutions
Lower Fuel Consumption … Renewable Energy … CO2 Emission Management … Lighter Materials
Mission: Help end user industry to manufacture better products more efficientlyApproach: addressing Industry’s burning needs related to their product development
R&D strategy: foster technology innovation through R&D collaboration
Products: PLM Software, Test Software and Hardware, Engineering ServicesSupporting the end to end engineering process workflow, and seamlessly linking to manufacturing.
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MacroModelMat (M3) program
Knowledge centers
Industry
Solving lightweight challenges
byadvanced testing
& simulation
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M3 research program for composites: multi-material, -scale, -attribute & -physics
Multi-Material
right material
at right place
Multi-Scale
µicro
meso
MacroMulti-Attribute
applications
MacroModelMatMacro level
simulation solutions
Multi-PhysicsmanufacturingT [C]
Curing
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M3 research program for additive manufacturing: multi-material, -scale, -attribute & -physics
M3 AMESTO (in prep.)
Multi-Physicsmanufacturing
Multi-Material
right material
at right place
Multi-Scale Multi-Attributeapplicationsµicro Macro
meso
MacroModelMatMacro level
simulation solutions
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NX Nastran, Samcef
Simcenter 3D
Simcenter™ Portfolio for Predictive Engineering Analytics Simcenter 3D & NX Nastran
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Open environment, with Multi-CAE solver support
NX Nastran
ANSYS
LMS Samcef
LS-Dyna
Abaqus
MSC Nastran
Simcenter 3D• Multi-CAD geometry editing• Comprehensive meshing• Assembly management
• Solution / subcase management• Post-processing & reporting• Associativity
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Simcenter 3DUnified, scalable, open and extensible environment
Centralized pre/postto efficiently build models for
your solver of choice
A scalable environment for analysts, discipline specialists,
and design engineers
Customizable (via NX Open and DMAP scripting, user materials)
to meet (y)our needs
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Predictive CAE for (CFR) Composites through Multiscale Modelling!
1.E-12
1.E-11
1.E-10
1.E-09
1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06
Information Management:Store the answer...… input for next scale
Requirements flow:... Ask the question
Leng
th S
cale
(m)
Time Scale (Seconds)
Molecular dynamics
Constituents (fiber, matrix, interface)
Reinforcement type
Macro FEA
RVE incl. stacking
sequence
UD
* WiseTex, courtesy of KU Leuven.
*
Two-level interactions are emerging(one example shown here):• Coupled simulation: concurrent
two-scale codes in one solver;• Co-simulation: two interacting
codes, interchanging per timestep.
Figure: courtesy of
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Predictive CAE for (CFR) Composites through Multiscale Modelling!
1.E-12
1.E-11
1.E-10
1.E-09
1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06
Information Management:Store the answer...… input for next scale
Requirements flow:
... Ask the question
Leng
th S
cale
(m)
Time Scale (Seconds)
Molecular dynamics
Constituents (fiber, matrix, interface)
Reinforcement type
Macro FEA
RVE incl. stacking
sequence
UD
DISCRETE C-MESOC-MICRO C-MACRO
Any info from lower levels that can help to alleviate the need for testing & simulation at OEMs / other suppliers is an advantage.
This requires further R&D and development of new modeling and new standardized testing procedures.
Macro-level structure modelfor performance predictions
StiffnessStrengthDamage…
Coupon Tests
PI
Material scans (e.g. Micro-CT)
ArchitectureGeometry
(e.g. weave, laminates)Physics/Chemistry:
additional source of information
Constitutive BehaviourFailure mechanisms
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“We need more simulation-based product design data and coupon level testing to establish a dependable simulation process for all the material and design choices at hand.”Dr. Yuta Urushiyama, Chief Engineer, Technology Research Division, Honda
Composite design at Honda R&D Co., Ltd.Enabled by multi-scale approach
CouponDesign/Validation of material models
ComponentModel validation on
componentsand joint technology
SubsystemValidation of complexsubsystem modeling
VehicleExpertise build-up
full vehicle simulationMulti-scale simulation
FrontloadingComposite design
to maximizedesign spaceexploration
(Multi-attributes)
Joseph - www.autoblog.com - 2013
Joseph - www.autoblog.com - 2013
Originates from project“M3Strength”
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Virtual Material CharacterizationTo Accelerate the Composites Design Process
Critical Enabler for Expanded Composite Design Space Exploration and Optimization
Test Based(Coupon) Simcenter - Virtual Material Characterization
Micro – Meso Models Simulation - Analysis Material Characteristics:
Damage, Permeability…
Very much reduced number of tests
Include performance and manufacturing-related aspects (effect of defects)
Allows multi-attribute virtual material optimization
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Trend: need for better product performancedrives OEMs to lower-level knowledge …
Need arises to analyze and prioritize underlying physics and chemistry characteristics: • Surface roughness• Porosity• Chemical bonding / adhesion properties• Crystalline structure• ... of fiber & matrix material
Automotive OEM: “We wish to avoid shear failure at the fiber-matrix interface”.
Many interacting phenomena (physics, chemistry, mechanics) together determine whether or not a shear failure mode is likely to occur.
First step is understanding this & converting the new know-how into guidelines (Translator activity).
Second step is to script and automate such information transfers into new software processes / tools that connect databases and interface with users.
© Walt Disney, 1956
TRANSLATOR
Courtesy of Ghent University.
Example
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Differences in data needs at different industry segments
1.E-12
1.E-11
1.E-10
1.E-09
1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06
Information Management:Store the answer...… input for next scale
Requirements flow:
... Ask the question
Leng
th S
cale
(m)
Molecular dynamics
Constituents (fiber, matrix, interface)
Reinforcement type
Macro FEA
RVE incl. stacking
sequence
UD
DISCRETE C-MESOC-MICRO C-MACRO
Material Manufacturers:• Strong need for detailed physics
& chemistry data and models, to support materials design challenge
• IPR / secrecy of recipe and manufacturing process
End-user product manufacturers: • Need for macro-level model
parameters (from testing, characterization, calculation/simulation, …)
• Need for new materials that are fit-for-purpose for challenging applications
Time Scale (Seconds)
Collaboration between people is key:
physicists, chemists, materials scientists, engineers
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From raw test dataOr data sheet
DATA – How to generate, store and use?
Data Management and Parameter Identification (PI)
Manufacturing Simulation
Updating input data for optimisation
From Simulation
Performance Simulation
Static Damage Durability etc.
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Towards an optimal design of 3D printed lightweight structures
• Objective: Optimal design of 3D printed structures.
• Content: Achieve an optimal design of 3D printed lightweight structures by adopting the predictive CAE workflow including topology optimization.
Print final lightweight design
5
Design space & FE Model preparation for Topology Optimization (TO)
1TO drives solution to find zones for Lightweight (Lattice) and Bulkconsidering true lattice material properties and manufacturability
Red = Bulk
Blue = Lattice
2
Octet
Lightweight structure creationVariable local truss diameter based on TO results
Post TO treatment3
FE verification of design for any load case
4
Originates from project“M3AMCAE”
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Melt Pool Simulation: Simulation strategies
Continuum simulation approach• Solve mainly energy conservation equation; optionally, flow
transport equation• Approximate melt pool surface shape, relatively coarse spatial
discretization• Surface forces (surface tension, Marangoni effect, wetting, recoil
pressure) neglected or approximated • Moderate computational cost
Powder-scale simulation• Flow + energy equations• Detailed surface shape, fine spatial discretization (powder particles)• Surface forces modeled in detail (surface tension, wetting, recoil
pressure) • High computational cost
Jamshidinia et al, 2013
Panwisawas et al, 2017
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Melt Pool Simulation: Target of the simulations with flow
Prediction of melt pool size & thermal history Flow transport due to Marangoni effect increases heat transport and can influence melt pool size & thermal history
Prediction of defects/undesirable effects: • Balling (Plateau-Rayleigh instability) • Spattering (melt pool surface disturbances) • Porosity due to incomplete melting• Keyhole-related porosity• …
Qiu et al, 2015
King et al, 2015
Jamshidinia et al, 2013
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Optimization of 3D printing machines and process window
Optimal DED nozzles
Understanding of gas flow in SLMFine tuning of AM process parameters
• Simcenter 3D–STAR-CCM+ supports:• CFD-based SLM melt-pool simulation to determine
optimal AM process parameters• Design of 3D printers:
• Design of optimal DED nozzles• Optimization of gas-flow in SLM build chambers for
performant AM machines
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Additive Manufacturing
Courtesy of Access, a business partner of Siemens PLM
Thank you!
For comments or questions about this presentation, please contact [email protected].