Siemens PLM portfolio for BDSS

Transcription

1 Siemens PLM portfolio for BDSS Accurate Prediction Collaboration and Linking Materials Data Management Decision Making / Optimization Realize Innovation.

2 Business Decision Support System (BDSS) - Requirements & Solutions BDSS Requirements Accurate 3D CAE Product Performance Prediction with Minimal Testing Collaboration & Linking of projects and activities across the enterprise Materials Data Management Decision Making and Product Optimization Siemens PLM Solutions Simcenter 3D with Virtual Material Characterization Teamcenter Teamcenter Integrated Materials Management (IMM) link to Granta MI HEEDS Page 2

3 Business Decision Support System (BDSS) - 5 points to describe our BDSS 5 points to describe our BDSS Siemens PLM Solutions 1. Tell us about your decision-making tool and how it integrates materials modelling outcomes 2. What models are integrated in your BDSS? 3. Who are the main actors using the BDSS and what type of projects do they use it for? 4. What are the main benefits, or KPIs, linked with integrating material model output with business decisions? 5. Is your BDSS working successfully to include materials modelling outcomes? See previous slide it s a combination of tools (Simcenter 3D including VMC Teamcenter including IMM HEEDS) 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. CAE analysts, CAE engineers, materials engineers, design engineers, Shorter model preparation time, higher quality models, better product performance decisions, shorter design cycles, shorter time to market for end user products,. Yes success is to deliver materials decisions and product value to the industrial end users. Page 3

4 Accurate Prediction: Simcenter 3D 3D CAE for the digital twin Best-in-class simulation modeling Multi-discipline integration Openness and scalability Leading in system simulation Leverage industry expertise Page 4

5 Simcenter Portfolio for Predictive Engineering Analytics Simcenter 3D & NX Nastran Simcenter 3D NX Nastran, Samcef Page 5

6 Virtual Material Characterization (VMC) To Accelerate the Composites Design Process Test Based (Coupon) Micro Meso Models Simcenter 3D Virtual Material Characterization 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 Critical Enabler for Expanded Composite Design Space Exploration and Optimization Page 7

7 Virtual Material Characterization Automated homogenization for elastic properties Geometry creation Periodic BC FE solutions Resulting material properties Meshing & local material properties Page 8

8 Virtual Material Characterization Validation example Page 9 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

9 BDSS: Accurate Prediction Collaboration and Linking Materials Data Management Decision Making / Optimization System Simulation Simcenter Amesim, Simcenter ESD, Simcenter System Synthesis, CAE Simulation Simcenter 3D, STAR-CCM+, NX Nastran, Physical Testing Simcenter Testlab, Scadas, Simcenter Testxpress, Collaboration and Linking - Teamcenter Materials Data Mngt. - IMM, Link to Granta Decision Making / Optimization - HEEDS Page 10

10 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 Page 11

11 Collaboration and Linking: Teamcenter - Reach Beyond Functional Boundaries - Connect people to the data they need Enterprise wide access to data 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 Page 12

12 Materials Data Management How to generate, store & use data? From raw test data Or data sheet From Simulation: VMC Manufacturing Simulation Data Management and Parameter Identification (PI) Updating input data for optimisation Performance Simulation Static Damage Durability etc. Page 14

13 Integrated Materials Management (IMM) data model Material life cycle IMM provides full traceability Test Definition: Fatigue Material: 1E4140 Design: Shaft2135 Further Consumption Test Result Sequence 5 Parameter Table 1E4140 Rev C Parameter Shaft2135 Rev A Substance Table Page 15 Substance

14 Decision Making: HEEDS Discover Better Designs, Faster! Multidisciplinary Design Exploration Platform High level of automation. Leverage HPC infrastructure Large ecosystem of integrated solvers. Connect to any inhouse 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 Page 17

15 Decision Making: HEEDS Innovative Products and Technologies Measurements Data Data interdependency Math model creation Sensitivity assessment Test matrix definition CAD data Parametric geometry Simulation data Simulation model prediction improvement Test / Simulation data correlation Performance improvement Robustness assessment Geometry, material, manufacturing, load case dispersion Product Innovation Page 18 Bidirectional Data Workflow Automation

16 Decision Making: HEEDS New Paradigm for Design Exploration Traditional Process Define Objectives Baseline model Variables Responses Objectives Constraints Simplify Model Either: Limit Variables and/or Fit Response Surface Model (DOE/RSM) Select Algorithm Gradient search Genetic algorithm Particle swarm Ant colony Simulated anneal Etc. Tune Algorithm Population size # of generations Crossover rate Mutation rate Selection type Etc. Conduct Search Interpret Results Requires Expertise Steep learning curve Limited deployment 19 Page Modern Process (HEEDS SHERPA) Define Objectives Baseline model Variables Responses Objectives Constraints Automated Search Select number of evaluations Run optimization Interpret Results Optionally: Study robustness & sensitivity of optimal design(s) HEEDS surpasses anything on the market in its ability to help us drive innovation. -- Anders Ahlström, Scania Truck No optimization expertise required-time Savings Discover Better Designs, Faster Any engineer can use Adoption in a matter of few days Global deployment

17 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 algorithm Graph showing function for two variables (n=2) Competitive Hybrid x 2 x 1 Page 20 Average values for 25 optimizations from random baselines Design space points evaluated by HEEDS (SHERPA)

18 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 Page 21

19 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). Page 22

20 Thank you! For comments or questions about this presentation, please contact