Siemens PLM portfolio for BDSS Accurate Prediction – Collaboration and Linking – Materials Data Management – Decision Making / Optimization
Realize Innovation.
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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
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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.
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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
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NX Nastran, Samcef
Simcenter 3D
Simcenter™ Portfolio for Predictive Engineering Analytics Simcenter 3D & NX Nastran
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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
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Resulting material properties
FE solutionsPeriodic BC
Meshing & local material properties
Geometry creation
Virtual Material CharacterizationAutomated homogenization for elastic properties
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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.
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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
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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
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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
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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?
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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
…
…
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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!
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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
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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
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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
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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
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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).
Thank you!For comments or questions about this presentation, please contact [email protected].