siemens plm portfolio for bdss - the european materials ......siemens plm portfolio for bdss...

Siemens PLM portfolio for BDSS Accurate Prediction – Collaboration and Linking – Materials Data Management – Decision Making / Optimization

Realize Innovation.

Unrestricted © Siemens AG 20182018.03.27 Siemens PLM Software

Business Decision Support System (BDSS) - Requirements & Solutions

BDSS Requirements Siemens PLM SolutionsAccurate 3D CAE ProductPerformance Prediction with Minimal Testing

Simcenter 3D with Virtual Material Characterization

Collaboration & Linking of projects and activities across the enterprise

Teamcenter

Materials Data Management

Teamcenter Integrated Materials Management (IMM) – link to Granta MI

Decision Making and Product Optimization

HEEDS


Business Decision Support System (BDSS) - 5 points to describe our BDSS

5 points to describe our BDSS Siemens PLM Solutions1. Tell us about your decision-making tool and how it integrates materials modelling outcomes

See previous slide – it’s a combination of tools (Simcenter 3D including VMC – Teamcenter including IMM – HEEDS)

2. What models are integrated in your BDSS?

Simcenter 3D addresses continuum models, but is open to receive predictive inputs from discrete levels. Teamcenter is open to all data, and HEEDS can link to any data/model.

3. Who are the main actors using the BDSS and what type of projects do they use it for?

CAE analysts, CAE engineers, materials engineers, design engineers, …

4. What are the main benefits, or KPIs, linked with integrating material model output with business decisions?

Shorter model preparation time, higher quality models, better product performance decisions, shorter design cycles, shorter time to market for end user products, … .

5. Is your BDSS working successfully to include materials modelling outcomes?

Yes – success is to deliver materials decisions and product value to the industrial end users.


Accurate Prediction: Simcenter 3D3D CAE for the digital twin

Multi-discipline integration

Best-in-class simulation modeling

Openness and scalability

Leading in system simulation

Leverage industry expertise


NX Nastran, Samcef

Simcenter 3D

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

Unrestricted © Siemens AG 20182018.03.27Page 7 Siemens PLM Software

Virtual Material Characterization (VMC)To Accelerate the Composites Design Process

Critical Enabler for Expanded Composite Design Space Exploration and Optimization

Test Based(Coupon) Simcenter 3D – 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


Resulting material properties

FE solutionsPeriodic BC

Meshing & local material properties

Geometry creation

Virtual Material CharacterizationAutomated homogenization for elastic properties


Virtual Material CharacterizationValidation example

Reference: P. G. Catera, F. Gagliardi, D. Mundo, L. De Napoli, A. Matveeva, L. Farkas, “Multi-scale modelling of triaxial composites for FE-based modal analysis of hybrid metal-composite gears”, Composite Structures, Volume 182, 15 December 2017, Pages 116-123.


BDSS: Accurate Prediction – Collaboration and Linking –Materials Data Management – Decision Making / Optimization

CAE SimulationSimcenter 3D, STAR-CCM+, NX Nastran, …

Physical TestingSimcenter Testlab, Scadas,Simcenter Testxpress, …

System SimulationSimcenter Amesim, Simcenter ESD, Simcenter System Synthesis, …

Collaboration and Linking -

Teamcenter

Decision M

aking / Optim

ization -HEED

S

Materials D

ata Mngt. -IM

M, Link to G

ranta


Collaboration and Linking: Teamcenter

Connect more people to the data they need

Connect all users to relevant product data and processes

Empower users to reach beyond their own functional boundaries to

share information


Collaboration and Linking: Teamcenter - Reach Beyond Functional Boundaries - Connect people to the data they need

• Designs – everyone has access to designs and designers have access to PLM

• Documents – everyone understands specifications and requirements

• BOMs – everyone knows what's in the product

• Change – more people participate and more data is gathered

• Reporting – Everyone can see the big picture with on-demand reports

Enterprise wide access to data


From raw test dataOr data sheet

Data Management and Parameter Identification (PI)

Manufacturing Simulation

Updating input data for optimisation

From Simulation: VMC

Performance Simulation

Static Damage Durability etc.

Materials Data Management – How to generate, store & use data?


Integrated Materials Management (IMM) data modelMaterial life cycle – IMM provides full traceability

Material:1E4140

Parameter

Test Definition:

Fatigue

Substance

Test Result Sequence 5

Table

Parameter

SubstanceTable

1E4140 –Rev C

Design:Shaft2135

Shaft2135 –Rev A

Further Consumption

…

…


Multidisciplinary Design Exploration Platform

• High level of automation. Leverage HPC infrastructure

• Large ecosystem of integrated solvers. Connect to any in-house application

• Multidisciplinary optimization. Optimize any parameter including CAD or mesh morphing shape parameters

• Rapid attainment of optimal designs considering competing objectives - no need for search algorithm selection and tuning thanks to proprietary technology

• Strong data mining capabilities. Sensitivity, robustness, and trends analysis

• In PLM context, configurations are stored, managed and can be reused

• Easy to use – no need to be an optimization specialist• Easy to deploy across organizations

“I was often asked what type of design search strategy should be applied to a problem and my answer was “it depends”… I no longer have to answer this question because HEEDS does the work for you.” – Douglas Zhu, Honeywell Aerospace

Decision Making: HEEDSDiscover Better Designs, Faster!


Decision Making: HEEDSInnovative Products and Technologies

Data interdependency

Sensitivity assessment

Math model creation

Simulation model prediction improvementTest / Simulation data correlation

Performance improvement

Product Innovation

Robustness assessment Geometry, material, manufacturing, load casedispersion

Measurements Data Simulation data

Bidirectional Data Workflow Automation

Test matrix definition

CAD dataParametric geometry


Decision Making: HEEDS New Paradigm for Design Exploration

19 19

Traditional ProcessDefine

ObjectivesSimplify Model

Select Algorithm

Either:• Limit Variablesand/or• Fit Response

Surface Model (DOE/RSM)

• Gradient search• Genetic algorithm• Particle swarm• Ant colony• Simulated anneal• Etc.

Tune Algorithm

Conduct Search

Interpret Results

• Population size• # of generations• Crossover rate• Mutation rate• Selection type• Etc.

• Baseline model• Variables• Responses• Objectives• Constraints

“HEEDS surpasses anything on the market in its ability to help us drive innovation.”

-- Anders Ahlström, Scania Truck

Discover Better Designs, Faster

Define Objectives

Automated Search

Interpret Results

Modern Process (HEEDS SHERPA)

• Baseline model• Variables• Responses• Objectives• Constraints

Optionally:• Study robustness &

sensitivity of optimal design(s)

• Select number of evaluations

• Run optimization No optimization expertise required-Time Savings

• Requires Expertise

• Steep learning curve

• Limited deployment

• Any engineer can use

• Adoption in a matter of few days

• Global deployment


Example of Decision Making: Benchmark Results Boeing

Challenge:• Find minimal function value in least number of evaluations

Results:• In 2000 evaluations, SHERPA performed >10% better than any

other algorithm and >30% better than the nearest hybrid algorithmGraph showing function for

two variables (n=2)

Average values for 25 optimizations from random baselines

x1

x2

Design space points evaluated by HEEDS (SHERPA)

Competitive Hybrid


Example of Decision Making: Auto/Steel Partnership – Vehicle Compartment

Challenge:• Minimize mass• Constraints

• Intrusion during side impact• Minimum roof crush force• Natural frequencies• Bending/torsional stiffness

• Design variables (120)• 61 shapes• 39 thicknesses• 20 materials

Results:• 30 kg (23%) reduction in mass


Acknowledgements

The work on Virtual Material Characterization (VMC) included in this presentation has been funded by the SBO and IBO projects “M3Strength”, which fit in the MacroModelMat (M3) research program funded by SIM(Strategic Initiative Materials in Flanders) and VLAIO (Flemish government agency Flanders Innovation & Entrepreneurship).

http://www.sim-flanders.be/

https://www.vlaio.be/nl

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

mailto:[email protected]