Materials Modelling and Interoperability – Siemens PLM Vision · Need arises to analyze and prioritize underlying physics and chemistry characteristics: • Surface roughness •

Materials Modelling and Interoperability –Siemens PLM VisionNovember 2017

Realize innovation.Unrestricted © Siemens AG 2017

Unrestricted © Siemens AG 20172017.11.07Page 2 Siemens PLM Software

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.


MacroModelMat (M3) program

Knowledge centers

Industry

Solving lightweight challenges

byadvanced testing

& simulation


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


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


NX Nastran, Samcef

Simcenter 3D

Simcenter™ Portfolio for Predictive Engineering Analytics Simcenter 3D & NX Nastran


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


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


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


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


Requirements flow:

... Ask the question

Leng

th S

cale

(m)


Molecular dynamics


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


“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”


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


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


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


Requirements flow:

... Ask the question

Leng

th S

cale

(m)

Molecular dynamics


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


Collaboration between people is key:

physicists, chemists, materials scientists, engineers


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.


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”


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


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

Unrestricted © Siemens AG 2017Page 19 Siemens PLM Software

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


Additive Manufacturing

Courtesy of Access, a business partner of Siemens PLM

Thank you!

For comments or questions about this presentation, please contact [email protected].

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Materials Modelling and Interoperability – Siemens PLM Vision · Need arises to analyze and prioritize underlying physics and chemistry characteristics: • Surface roughness •